Proposal for a New Interdepartmental Graduate

Degree Program in Bioinformatics (M.S. and Ph.D.)

To be offered by the Interdepartmental Degree Committee in Bioinformatics

University of California, Los Angeles

Submitted by: Christopher Lee, Department of Chemistry and Biochemistry

Parag Mallick, Department of Chemistry and Biochemistry

Matteo Pellegrini, Department of Molecular, Cell, and Developmental Biology

Eleazar Eskin, Department of Computer Science

January 30, 2007

Table of Contents

Section 1. Introduction........................................................................................................ 5

Section 1-1. Aims and objectives of the program...................................................... 5

Section 1-2. Historical development of the field and historical development of the departmental strength in the field........................................................................................................................... 6

Section 1-3. The timetable for development of the program..................................... 9

1-3a. Specific timing................................................................................................... 9

1-3b. Consistency of enrollment projections with the campus enrollment plan......... 10

1-3c. Reduction of enrollments in programs in order to accommodate the proposed program, if any 10

Section 1-4. Relationship of the proposed program to existing programs on campus and the Campus Academic Plan. If the program is not in the campus academic plan, why is it important that it be begun now?.............................................................................................................................. 10

1-4a. Could the curriculum be offered just as effectively within an existing structure (e.g., as a pathway with an existing major program)?........................................................................................... 11

1-4b. Overlap between the proposed curriculum and the curricula of other units on this campus 12

1-4c. Effect of the proposed program on undergraduate programs offered by the sponsoring department(s) 12

Section 1-5. Interrelationship of the program with other University of California programs 13

1-5a. Possibility of cooperation or competition with other programs within the University 13

1-5b. Differences from other similar programs within the University and other California institutions 13

1-5c. Letters of evaluation from chairs of departments in related fields from the UC system and outside. 16

Section 1-6. Department or group which will administer the program................. 16

Section 1-7. Plan for evaluation of the program by the offering department and campuswide 16

Section 1-8. Evidence that the different participating disciplines contribute to the total program in such a way that the student cannot achieve necessary knowledge without substantial study in two or more established departments............................................................................................. 16

Section 2. Program............................................................................................................ 17

Section 2-1. Undergraduate preparation for admission......................................... 17

2-1a. Field examinations and/or other pre-qualifying examinations.......................... 17

2-1b. Qualifying examinations—written and/or oral................................................. 17

2-1c. Relationship of master’s and doctor’s programs.............................................. 17

2-1d. Special preparation for careers in teaching....................................................... 17

2-1e. Other admissions requirements........................................................................ 17

Section 2-2. Foreign language requirement.............................................................. 18

Section 2-3. Program of Study................................................................................... 18

2-3a. Specific fields of emphasis............................................................................... 18

2-3b. Plan(s): Masters I and/or II; Doctorate............................................................ 19

2-3c. Unit requirements............................................................................................. 19

2-3d. Required and recommended courses, including teaching requirement............. 20

2-3e. Requirements for licensing or certification....................................................... 20

Section 2-4. Field examinations—written and/or oral............................................. 20

Section 2-5. Qualifying examinations—written and/or oral................................... 20

Section 2-6. Thesis and/or dissertation expectations............................................... 20

Section 2-7. Final examination requirements........................................................... 21

Section 2-8. Special requirements over and above Graduate Division minimum requirements 21

Section 2-9. Relationship of masters and doctoral programs................................. 21

Section 2-10. Special preparation for careers in teaching....................................... 21

Section 2-11. Student sample program for each year.............................................. 21

2-12. Normative time from matriculation for degree (assuming student has no deficiencies and is enrolled full-time)....................................................................................................................... 22

2-12a. Normative lengths of time for pre-candidacy and candidacy periods............. 22

2-12b. Other incentives to support expeditious times-to-degree................................ 22

Section 3. Projected Need.................................................................................................. 22

Section 3-1. Student demand for the program........................................................ 22

3-1a. Documentation of demand for program with three to five years of enrollment and admissions statistics from this or other institutions, or data on rate of student inquiries............................. 23

3-1b. Evidence that this demand will be stable and long-lasting................................ 23

3-1c. For new programs that are extensions of existing disciplines, enrollment statistics from related courses to demonstrate demand................................................................................................... 24

3-1d. Statistics or other documentation of need......................................................... 24

Section 3-2. Opportunities for placement of graduates........................................... 24

3-2a. Placement records of other UC programs in the field in recent years............... 24

3-2b. Demonstration of a strong market for program graduates by listing recent job listings, employer surveys, assessments of future job growth............................................................................... 24

Section 3-3. The importance of the new program to the discipline........................ 27

Section 3-4. Ways in which the program will meet the needs of society................ 28

Section 3-5. Relationship of the program to research and/or professional interests of the faculty 28

3-5a. Core faculty...................................................................................................... 28

3-5b. Associate faculty.............................................................................................. 29

Section 3-6. Differentiation of the proposed program from existing UC and California independent university programs, and from similar programs proposed by other UC campuses 30

Section 4. Staff.................................................................................................................. 30

4-1a. List of program faculty, their ranks, their highest degree and other professional qualifications, and a citation of no more than five recent publications (abbreviated curricula vitae); Data concerning faculty limited to that information pertinent to the committee’s evaluation of faculty qualifications....... 30

4-1b. Comments from chairs of departments with graduate programs and/or faculty closely related to or affected by the proposed program........................................................................................... 30

4-1c. For participating faculty members outside of the sponsoring department, copies of letters indicating their interest in the program (critical for interdisciplinary programs).................................. 30

Section 4-2. Organizational Structure....................................................................... 31

Section 4.3 Sources of available fellowship and assistantship support................. 32

Section 5. Courses.............................................................................................................. 32

Section 5-2. Elective Bioinformatics Courses........................................................... 32

The following supporting courses are offered in related fields: Statistics 100A (or equivalent preparation) is required as a prerequisite for Chemistry CM260A. CS 31 (or equivalent programming skills) is required for Chemistry C260BL (Bioinformatics Algorithms Laboratory). The Program in Computing offers a range of courses (PIC 10ABC, PIC 20AB, PIC 60, PIC 110) that are also very useful for bioinformatics students. The many departments of the Medical School and Life Sciences offer a wide range of coursework on biology that is highly relevant to bioinformatics students.................................................................. 35

Section 5-3. Program degree requirements................................................................ 35

Section 6. Resource Requirements and Enrollment Plans.................................................... 35

Section 6-1. Methods for funding.............................................................................. 35

6-1a. FTE faculty...................................................................................................... 35

6-1b. Staff FTE......................................................................................................... 36

6-1c. Library acquisitions.......................................................................................... 36

6-1d. Computing costs.............................................................................................. 36

6-1e. Equipment—inventory of current equipment and future needs........................ 37

6-1f. Space and other capital facilities—inventory of current facilities and future requirements 37

6-1g. Other operating costs (technical and administrative staff, supplies and expenses, lab maintenance and other facilities) and description of current staffing levels and future requirements.............. 37

Section 6-2. Projected doctoral enrollments for the first five years........................ 37

Section 7. Graduate Student Support.................................................................................. 37

Section 7-1. Strategy for meeting support needs..................................................... 37

Section 7-2. Current availability of faculty grants to support graduate students and funding trends in agencies expected to provide future research or training grants............................. 39

Section 7-3. Other extramural resources likely to provide graduate student support, or internal fellowships and other institutional support made available to the program......... 39

Section 7-4. Campus fund-raising initiatives that will contribute to support of graduate students 40

Section 7-5. Graduate student support table listing maximum number of students projected and sources of support for the first six years of the program...................................................... 40

Section 8. Changes in Senate Regulations........................................................................... 41

Section 9. Abstract............................................................................................................. 41

Section 10. Departmental Commitment to Proposed Program............................................ 41

Tables

Table 1. Sample Student Program for the First Year........................................................... 22

Table 2. Enrollments of Bioinformatics Core Courses ....................................................... 26

Table 3. Present and Proposed Courses for the Program .................................................... 32

Table 4. Projected Doctoral Enrollments for the First Five Years........................................ 38

Table 5. Graduate Student Support.................................................................................... 40

Appendices

Appendix A. Summary of Information Required by the California Postsecondary Education Commission

Appendix B. Letters of Support from Comparable California Programs

Appendix C. Brief Curricula Vitae of Program Faculty

Appendix D. Letters of Support from UCLA Departments

Appendix E. Letters of Interest from Participating Faculty Members

Appendix F. Catalog Descriptions of All Required and Recommended Courses

Appendix G. Program Degree Requirements

Section 1. Introduction

Section 1-1. Aims and objectives of the program

We propose to create an Interdepartmental Program (IDP) in Bioinformatics composed of faculty from 14 departments, so that it is possible for students to apply for and receive graduate training in bioinformatics at UCLA. This will strengthen faculty research in the Divisions of Life and Physical Sciences and the Schools of Medicine and Engineering, help train the next generation of scientists in a field of rapidly growing prominence, attract new sources of funding to the UCLA campus, aid faculty recruitment and retention, and drive growth at UCLA.

Our main goals are:

To provide integrated graduate training for biologists, computer scientists and mathematicians that will enable them to invent new kinds of bioinformatics throughout their future careers.
To create a strong, collaborative community of bioinformatics research groups at UCLA that unites faculty from many departments, and fosters leading-edge research in this highly interdisciplinary field. An excellent graduate program is a critical success factor for world-class research and for attracting superb faculty.
To make major contributions to bioinformatics research, through the development of new methodologies, new analyses of the human genome and other genomics data, and discoveries that directly affect human health.

Bioinformatics can be defined broadly as the study of the inherent structure of biological information. Some of this inherent structure is very obvious (e.g., codon usage biases that reveal protein coding regions), while others are less obvious but still immediately fruitful (e.g., how transcriptional regulatory sites give rise to “programs” of gene expression), while others are profound long-term challenges (e.g., how the genome encodes the capabilities of the human mind). Bioinformatics is the marriage of biology and the information sciences. Long term, this is a profound intellectual project. Fortunately, it is producing immediately valuable results now, e.g.:

Statisticians have invented analyses of DNA microchip results (expression measurements of all 30,000 human genes simultaneously) that can distinguish different types of tumors with dramatically different treatment requirements, which previously were hard to differentiate clinically.
Evolutionary biologists have developed bioinformatics analyses of genome sequence data that reveal the precise pathways by which dangerous pathogens (like HIV) evolve drug resistance, and how to slow the evolution of multi-drug resistance.
Computer scientists have created powerful new ways for mapping brain functions automatically from standard imaging data.

Bioinformatics is of central importance to biomedical research in the 21^st century (see Section 1-2 below), and to the economy of California. By training both Bioinformatics M.S. and Bioinformatics Ph.D. scientists from a variety of backgrounds, the proposed IDP will contribute directly to the skilled workforce that California’s biotechnology and software companies require for success. The IDP’s research may also give rise to new technologies and new companies. Indeed, many of the faculty have already done so in the past.

The proposed IDP will provide an academic home for bioinformatics at UCLA that will bring many different efforts together for the first time. Examples of current bioinformatics research conducted by the core faculty include:

The analysis of gene and protein sequences to reveal protein evolution and alternative splicing
The development of computational approaches to study and predict protein structure to further our understanding of function
The analysis of mass spectrometry data to, for example, understand the connection between phosphorylation and cancer
The development of computational methods to utilize expression data to reverse engineer gene networks in order to more completely model cellular biology
The study of population genetics and its connection to human disease

UCLA has already established a strong record of bioinformatics research and graduate training (see Section 1-2 below). In 1999 the faculty established a graduate core curriculum in bioinformatics, which has been offered continuously since that time (see Section 3-1a), demonstrating the faculty’s commitment to collaborative teaching and to long-term development of an integrated bioinformatics program. These initiatives have been recognized by a large number of awards of multi-investigator Project and Training grants in bioinformatics from NIH, NSF, DOE and other funding sources. These many disparate efforts need a strong graduate program to make them cohesive, successful, and competitive in the long term.

The establishment of the Bioinformatics IDP will allow UCLA to overcome the limitations of the current situation, in which no single program brings together bioinformatics students. Specifically, we expect to resolve these existing weaknesses:

Prospective bioinformatics graduate students do not know which program to apply to at UCLA, since neither departmental graduate programs nor ACCESS specifically recruit and train them.
Graduate students at UCLA who are conducting bioinformatics research currently take diverse courses that do not necessarily cover the core training in bioinformatics that we believe they need and that we propose here.
Relative to other UC schools that already have established bioinformatics graduate programs (see Section 1-5 below), the lack of such a program at UCLA places UCLA at a disadvantage in competing for the top bioinformatics students, and this impacts the ability of our faculty to obtain funding in this area.

The creation of the Bioinformatics IDP at UCLA will allow us to overcome all of these limitations.

Section 1-2. Historical development of the field and historical development of the departmental strength in the field

Intellectually, the proposed IDP draws strength from one of the most fundamental trends now visible in the life sciences. Over the last fifty years, biology has had one major theme—the marriage of biology and physical chemistry (symbolized by the omnipresence of “molecular biology” throughout the life sciences). Its ultimate expression—the Human Genome Project—has now been completed, setting the stage for a new era. Over the next fifty years, the most important engine of discovery will be the marriage of biology with the information sciences, notably computer science.

The reason for this is the revolutionary technological change in the biological sciences. It has always been a truism that biology is information. However, in the past biologists lacked the experimental and computational tools to deal with the true complexity of biology’s information dimension. Instead of being able to see and analyze the whole “computer program” of a cell or organism, biologists were limited to breaking the computer down to its smallest components and measuring their activity one gene at a time. This is akin to trying to understand a computer’s program by attaching a voltmeter to a single component inside it. While this reductionist program has been incredibly productive in terms of discovering new components, it traditionally was often difficult to put these pieces back together again in a way that could predict or explain the behavior of the entire system.

Over the last ten years, a wave of revolutionary technologies such as genomics has changed this situation dramatically. A marriage of molecular biology, engineering and computer science has increased experimental bandwidth—the ability to read out the program of information or activities in cells—by up to 10,000 times what was possible before this automation. Where previously a research project would sequence a gene, now it can sequence the entire genome. Where previously an experiment would measure the expression of one gene, now it can easily and rapidly measure the expression of all genes in the genome. The dramatic completion of the human genome five years before its planned deadline was only the most visible example of this universal trend. Already genome sequences of more than four hundred different organisms have been completed. Many waves of genomics technologies are spreading to new areas of biology, such as proteomics, and this process is accelerating.

These technologies have created an incredible avalanche of new experimental data, driving the need for bioinformatics. Genomics produces raw data; bioinformatics interprets its meaning. Completion of the human genome, for example, led to a strange anticlimax: once we had the data, it became obvious that we could understand very little of what it means. The answers to this problem—and there are already many—come from bioinformatics.

Already, bioinformatics has gained enormous importance and prominence in the human genome project and many other areas of biology, because of a simple shift in the “rate-limiting step” of biological research. Previously, just getting experimental data was difficult and slow. Now, biologists are awash in huge amounts of experimental data (to cite just one example, over 400 fully sequenced genomes); now the real challenge lies in analyzing their meaning. This is bioinformatics.

A key feature of this new field is its strongly interdisciplinary character. As we will document in Section 1-8, there is no one department at UCLA that could conceivably cover the range of topics—from Bayesian statistics to database design to protein function—that come up in nearly every bioinformatics research project. Statistics matter. Mathematical models matter. Computational algorithms and software engineering matter. Genetics matters. Molecular biology matters. Protein structure matters. There is no escaping the problem of interdisciplinary training in bioinformatics. The proposed IDP would provide an academic solution to this problem at UCLA for the first time.

UCLA has made seminal contributions to bioinformatics research. UCLA’s strengths can be divided into several categories:

Its bioinformatics faculty is strongly interdisciplinary, spanning departments ranging from Statistics, Chemistry, and Computer Science, to Human Genetics. UCLA is highly fortunate to bring together all the capabilities of a world-class medical school, engineering school, life sciences, mathematics and physical sciences, all in the adjacent buildings of South Campus. There are few universities that bring the full firepower of a complete research university to bear on bioinformatics, as UCLA truly does. The importance of the fact that all of these are co-localized on one quadrangle—and many meet every day for lunch at the café there—should not be underestimated.
The bioinformatics faculty is highly collaborative. They have established a six-year record of joint teaching of the bioinformatics core curriculum, and a long list of multi-investigator collaborative research projects, shared students, and joint training grants. The faculty has demonstrated that they can work together to assemble successful joint research and training programs.
The faculty is strong in mathematical and statistical approaches to bioinformatics problems, not only in the Departments of Statistics and Mathematics, but also throughout the group. Mathematical and statistical approaches are widely recognized to be key to solving many bioinformatics problems, and are of increasing importance in the field.
The faculty combines experimental and theoretical approaches integrally in their research. Whereas there is often a strong division between these research perspectives, UCLA’s bioinformatics faculty work to integrate theory and experimental analyses seamlessly, and have demonstrated that they can do this very productively.
The faculty is strong in both of the major focuses of bioinformatics: genomics (focusing on DNA), and proteomics (focusing on proteins). Indeed, many of the faculty integrate both approaches to make discoveries that would be impossible without this crossover.
These diverse strengths have been recognized by the selection of UCLA for establishment of a number of major centers in bioinformatics, including the DOE- and Keck Foundation funded Centers for Genomics and Proteomics; the first Integrated Graduate Education, Research and Training (IGERT) grant in Bioinformatics funded by NSF in 2000; the NIH-funded National Programs of Excellence in Biomedical Computing; the NIH-funded Genomic Analysis training grant; and the NIH-funded Center for Computational Biology.

Below we list the faculty of the proposed IDP, indicating academic department (in bold), bioinformatics IDP affinity group(s), and whether each is a core or an associate member.

James Bowie: Chemistry & Biochemistry – structural biology (core).
Guillaume Chanfreau: Chemistry and Biochemistry – genetics (associate).
Ivo Dinov: Statistics – neuroinformatics (associate).
Joseph DiStefano: Computer Science – systems biology (core).
David Eisenberg: Chemistry & Biochemistry – structural biology (core).
Eleazar Eskin: Computer Science – genetics, computational methodology (core).
Fred Fox: Microbiology, Immunology & Molecular Genetics – bioinformatics in industry; industrial contacts (associate).
Tom Graeber: Department of Molecular & Medical Pharmacology – systems biology (core).
Steve Horvath: Biostatistics – statistical methodology (core).
Siavash Kurdistani: Biological Chemistry – genetics; systems biology (associate).
James Lake: Molecular, Cell & Developmental Biology – evolution (core).
Ralf Landgraf: Biological Chemistry – structural biology (associate).
Ken Lange: Biomathematics – genetics; statistical methodology (core).
Christopher Lee: Chemistry and Biochemistry – genetics; evolution; computational methodology (core).
Ker-Chau Li: Statistics – statistical methodology (core).
James Liao: Chemical Engineering – systems biology (core).
Joseph Loo: Chemistry and Biochemistry – mass spectrometry (associate).
Parag Mallick: director of clinical proteomics at Cedars-Sinai Medical Center and Chemistry and Biochemistry – mass spectrometry (core).
Stan Nelson: Human Genetics – genetics (associate).
John Novembre: Ecology and Evolutionary Biology (Fall 2007) -- genetics (core).
Stott Parker: Computer Science – computational methodology (core).
Matteo Pellegrini: Molecular, Cell and Developmental Biology – systems biology; statistical methodology (core).
Peipei Ping: Physiology, Medicine, and Cardiology – mass spectrometry (associate).
Chiara Sabatti: Statistics – statistical methodology (core).
Janet Sinsheimer: Human Genetics – statistical methodology; genetics; evolution (core).
Stefano Soatto: Computer Science – computational methodology; neuroinformatics (associate).
Marc Suchard: Biomathematics – statistical methodology; genetics; evolution (core).
Charles Taylor: Ecology and Evolutionary Biology – population genetics (associate).
Arthur Toga: Neurology – neuroinformatics (associate).
Yingnian Wu: Statistics – statistical methodology (associate).
Todd Yeates: Chemistry and Biochemistry – structural biology (core).
Qing Zhou: Statistics – developing statistical methods in cis-regulatory analysis, including motif and cis-regulatory module discovery, gene expression analysis, ChIP-chip data analysis, and comparative genomics (core).

Section 1-3. The timetable for development of the program

1-3a. Specific timing

Program approval: Because of substantial delays in the consideration of an earlier proposal, we would like to have approval by July 2007, in order to recruit a class for entry to the M.S. and Ph.D. programs in Fall 2008.

New faculty hires: Currently, the bioinformatics core faculty consists of 20 tenure-track FTE. Inevitably, this number will grow as new hires are made in Bioinformatics. As evidence of the importance of this field at UCLA, five of the 20 core faculty we list here have joined UCLA within the last year or will join in 2007.

Course approvals: Chemistry CM260A was first approved in 1999. C260B was approved in Jan. 2007. 260BL, which is pending approval, was previously part of the approved 260 course. Statistics M254 was approved in 1999. Chemistry 202 was approved in 1999.

First availability of core offerings: The core courses for the proposed IDP have been offered in most cases for several years: e.g., Chemistry 260 (Bioinformatics & Genomics), first offered Spring 2000; Chemistry 202, first offered Fall 1999; Statistics M254, first offered Winter 2001; Chemistry M252, first offered Spring 2002.

Availability of space/facilities needed for program: Bioinformatics students can currently access a variety of computational resources: (1) a room of desktop computers in the Chemistry Building and (2) a cluster of computers in Boyer Hall.

Year of admission for first cohort of graduate students: We hope to admit the first cohort during the 2007-2008 application cycle, for admission to study at UCLA beginning Fall 2008.

Anticipated year of awarding first degrees: M.S. students admitted Fall 2008 would graduate in 2010. Ph.D. students admitted Fall 2008 would graduate most likely beginning 2013. It is possible that upon approval of the IDP some existing UCLA graduate students would transfer into the program to receive either the Bioinformatics M.S. or Ph.D. degree. (See list of current course enrollments in Section 3-1c.) This could advance the date of first degree awards to 2009-2010.

1-3b. Consistency of enrollment projections with the campus enrollment plan

The planned enrollments of the IDP (Table 1, page 23) are relatively small, and fit within the campus enrollment plan, which calls for increased graduate enrollment in line with supporting increased undergraduate enrollment (“Tidal Wave II”).

1-3c. Reduction of enrollments in programs in order to accommodate the proposed program, if any

We do not anticipate a substantial reduction in enrollments in any existing program as a result of the proposed IDP. Currently, the largest category of students enrolled in the bioinformatics core courses are admitted via the ACCESS program. Since the proposed IDP would create a new degree area (UCLA has no existing bioinformatics Ph.D. or M.S. degree program), it is reasonable to expect it would attract a new population of students, rather than simply taking from existing admissions programs. However, even if we assume that every ACCESS student enrolled in bioinformatics courses would apply to the proposed IDP (and not ACCESS), the reduction to ACCESS would be slight (approximately five students per year, which would be 6-9% of ACCESS yearly admissions of 55-75 students).

Section 1-4. Relationship of the proposed program to existing programs on campus and the Campus Academic Plan. If the program is not in the campus academic plan, why is it important that it be begun now?

The fact that this recommendation was made five years ago indicates that the need for the proposed IDP is urgent. It is essential because it would bring together UCLA’s many academic resources in bioinformatics for the first time. The interdisciplinary character of UCLA’s bioinformatics faculty—scattered among 11 departments in four schools—is both a strength and a potential weakness. As other prominent universities develop bioinformatics graduate degree programs and competition grows, UCLA will be at a severe disadvantage in recruiting top graduate student candidates if it does not offer a bioinformatics degree and admissions program. First, UCLA does not offer a bioinformatics degree; instead, students must apply to an existing department (e.g., Chemistry), and complete its traditional degree requirements while performing dissertation research in bioinformatics. For students who are serious about bioinformatics research because they see it as a field of great importance, not even having the word “bioinformatics” on their degree is disadvantageous. Perhaps worst of all, there is no way for an incoming student to access the full bioinformatics faculty (who are in many different departments and schools). Effectively, students are forced to guess who would be their dissertation advisor even before applying. Similarly, the scattered bioinformatics faculty currently has relatively little voice in admissions decisions about bioinformatics candidates, for the simple reason that there is no bioinformatics admissions program.

Failure to establish a graduate program would have negative effects on all of the existing initiatives in bioinformatics and genomics at UCLA. Long-term, it is hard to imagine that the bioinformatics training grant awards that UCLA has received would be renewed if the campus failed even to begin a graduate program. Faculty research in bioinformatics depends critically upon the quality of graduate student recruitment. Lack of a bioinformatics graduate program would become a major disadvantage once other universities establish strong bioinformatics degree programs, which is already happening. This in turn would hurt the quality of bioinformatics research at UCLA, and make it difficult to retain top bioinformatics faculty or recruit new faculty.

1-4a. Could the curriculum be offered just as effectively within an existing structure (e.g., as a pathway with an existing major program)?

As noted in Section 1-2, bioinformatics is a highly interdisciplinary field, and there is no way that general bioinformatics graduate training can be offered in a single existing UCLA department or program. The potential problems would be numerous, including:

Bioinformatics graduate students can (and should) come from a wide variety of undergraduate backgrounds, including (but not limited to) Biology, Biochemistry, Chemistry, Physics, Computer Science, Statistics, and Mathematics. There is no one existing department or program at UCLA whose admissions requirements and criteria are appropriate for all of these bioinformatics students.
There is no existing UCLA admissions program that includes all or even most of the bioinformatics faculty. Thus there is no program to which students can apply that will allow them to do rotations and choose an advisor from among the bioinformatics faculty. For example, while ACCESS includes many of the faculty based in life sciences departments, it includes none of the faculty from the engineering school, mathematics or statistics.
Students who wish to focus on bioinformatics often do not wish to be subjected to another extensive set of course requirements (e.g., Chemistry) as a result of being forced to get their degree from a traditional department. Because these requirements were established long before the field of bioinformatics existed, many are not necessarily relevant to bioinformatics study. Meanwhile, there is much else to learn to become an expert in bioinformatics.
The interdisciplinary character of bioinformatics—the fact that it has so many separate pieces—creates a substantial danger that the student’s training will fail to connect these pieces adequately. For example, it is essential to teach all the students (be they from computer science, math or biology) a core of shared vocabulary, concepts and methodologies, so that bioinformatics researchers from different backgrounds can actually talk to each other, and work together productively. Thus, it is essential to create a new unified program where students learn together and work together throughout their graduate training, to build strong connections across their original disciplinary boundaries.

1-4b. Overlap between the proposed curriculum and the curricula of other units on this campus

There is relatively little overlap with existing programs at UCLA. Here we list all programs that have a nominal overlap:

ACCESS. ACCESS is an umbrella graduate admissions program for the life sciences at UCLA. It organizes its faculty into a number of “affinity groups”, and has a bioinformatics affinity group, consisting of several of the faculty from this proposal who are located in life sciences departments. However, ACCESS neither provides bioinformatics graduate training (its course requirements are designed for traditional molecular biology/biochemistry research and have no bioinformatics component), nor provides access to all of the bioinformatics faculty at UCLA. Many of the bioinformatics faculty are in departments not covered by ACCESS.
Biomathematics. Biomath is a small department that emphasizes traditional mathematical modeling of biological systems. With a few exceptions, its faculty do not perform bioinformatics research, and its course requirements do not include bioinformatics requirements.
Biomedical Engineering. BME is a relatively new graduate program in the School of Engineering that emphasizes engineering approaches to biomedical problems, especially the design of medical devices. Its course requirements do not include bioinformatics courses, and since most bioinformatics faculty are outside of the engineering school, it does not include them.
Human Genetics. Human Genetics launched a graduate program about seven years ago. Although the department’s main focus is on experimental approaches (genetics and genomics), bioinformatics is relevant to the interpretation of these data. The department inaugurated a “bioinformatics track” within its Ph.D. in Human Genetics, to enable some of its students to gain credit for bioinformatics coursework. However, the Ph.D. course requirements overall remain focused on human genetics. Furthermore, most bioinformatics faculty at UCLA are not in the Department of Human Genetics, and thus are not included.

1-4c. Effect of the proposed program on undergraduate programs offered by the sponsoring department(s)

Establishing a strong graduate degree program in bioinformatics will be beneficial to undergraduate programs in a number of departments. UCLA already offers both a number of undergraduate bioinformatics courses (such as Chemistry C160A), and an undergraduate degree (Computational and Systems Biology) designed for bioinformatics study. The growth and quality of these curricula depend on the expertise of teaching assistants drawn from graduate students in bioinformatics. Strong undergraduate interest in the field of bioinformatics is demonstrated by growing enrollments in courses such as Chemistry C160A, and in the Bioinformatics specialization of the Computational and Systems Biology major. Continued development of undergraduate bioinformatics curricula requires a strong graduate program in bioinformatics.

Section 1-5. Interrelationship of the program with other University of California programs

1-5a. Possibility of cooperation or competition with other programs within the University

We do not see significant competition with other graduate programs. As stated above, a few students may decide to apply to the Bioinformatics IDP instead of ACCESS, but this would constitute only a small fraction of the total number of students applying to ACCESS (about 9% at most). Similarly a few of the students who will apply to the Bioinformatics IDP would have applied to individual departments (e.g., Chemistry) if the program were not available, but again this would be a small number compared to the total number of students applying to the department.

We see a significant opportunity to collaborate with the ACCESS program in our recruitment efforts. This could also include cooperation in administering faculty presentations for the students. The Center for Computational Biology could provide administrative help in running the proposed IDP, as graduate education is one of its priorities. Finally, we see an opportunity to leverage the computational resources of the UCLA-DOE Institute for Genomics and Proteomics.

1-5b. Differences from other similar programs within the University and other California institutions

Most of the top research universities in California already have graduate degrees in bioinformatics. Stanford, UC Riverside, UCSF, UCSC and UCSD all offer Ph.D.s in Bioinformatics while UC Berkeley and USC offer computational biology minors that accompany a traditional Ph.D. in another program. Below we describe these programs in detail and point to some of the similarities and differences with the IDP proposed here.

UCSF

UCSF offers a program in Biological and Medical Informatics (BMI) which encompasses data, information, and knowledge acquisition, representation, modeling, integration, communication, and interpretation ranging across basic science and engineering through clinical practice and policy. The primary mission of the BMI Program is to train biomedical informatics researchers for academia and industry. The Program's focus is on the science of biomedical informatics, with special emphasis on rigorous methodology, innovation, and generalizability of findings, rather than the routine application of technology to biomedical science and practice. Training spans the full spectrum of biomedical informatics—from bench to bedside to health system. Graduates of the Program are well positioned to contribute at the interface of bio- and medical informatics, where future research opportunities are excellent.

The Program offers training leading to both the M.S. and the Ph.D. degrees. The M.S. program is open only to students with a health sciences-related graduate or professional degree (e.g., M.D., Pharm.D., M.S. in epidemiology); the Ph.D. program is open to students with a baccalaureate degree and the appropriate prerequisites.

All students take the same curriculum in the first year. In the second year, students present their work in a seminar, which is evaluated by selected faculty. Masters' students then work on a final research project, while Ph.D. students prepare for their oral qualifying examination. Upon successful completion of their orals, Ph.D. students advance to candidacy and begin work on their dissertation. A thesis defense is required for graduation.

The UCSF program is more focused on medical informatics than the one we propose here. Unlike our IDP, which integrates expertise from medicine, engineering, life sciences, math and physical sciences here, UCSF’s is based solely in a medical school.

UCSC

The Department of Biomolecular Engineering offers B.S., M.S., and Ph.D. degrees in bioinformatics. Bioinformatics combines mathematics, science, and engineering to explore and understand biological data from high-throughput experiments, such as genome sequencing and gene expression chips. The program builds on the research and academic strengths of the faculty in the Center for Biomolecular Science & Engineering.

The immense growth of biological information stored in computerized databases has led to a critical need for people who can understand the languages, tools, and techniques of mathematics, science, and engineering. A classically trained scientist may be unfamiliar with the statistical and algorithmic knowledge required in this field. A classically trained engineer may be unfamiliar with the chemistry and biology required in the field. This program strives for a balance of the two; an engineer focused on the problems of the underlying science, or, conversely, a scientist focused on the use of engineering tools for analysis and discovery.

The graduate degree prepares the student for life as a cutting-edge researcher in bioinformatics, creating new tools to answer new questions. Support is available to graduate students through an NIH training grant in bioinformatics, CBSE Diversity Fellowships in Genomic Science, and various other sources detailed on the CBSE website.

The primary difference between our IDP and the UCSC Bioinformatics program is the breadth of the core faculty, which in our case spans 11 departments, whereas the UCSC program is more focused, offered by one academic department. Thus the programs’ scientific strengths differ significantly in their mix of disciplines.

UCSD

The Bioinformatics graduate program draws upon the interdisciplinary expertise of affiliated faculty from seven participating departments and graduate programs: Bioengineering, Biology, Biomedical Sciences, Chemistry and Biochemistry, Computer Science and Engineering, Mathematics, and Physics. In addition, UCSD offers an interdisciplinary specialization in Bioinformatics.

In recent years, Bioinformatics has been identified by the UCSD administration as one of the most important growth areas for the campus. Several recent new faculty hires have been targeted in bioinformatics-related fields. UCSD has also seen a significant increase in the research activity associated with Bioinformatics across the traditional disciplines.

In general, the UCSD program is very similar to the one we are proposing for UCLA and could be used as a model.

USC

Computational molecular biology has been thriving at USC since 1982, when Michael Waterman joined the Departments of Mathematics and Biological Sciences. Since that time the group has grown to include eight professors and numerous research students and postdoctoral associates.

USC’s approach to research and training in the Computational Biology and Bioinformatics area has biology as its essential motivation. An important aspect involves problem formulation, an interactive process that involves collaborations between biologists and mathematical scientists. Once a problem is formulated, the solution often involves a blend of statistics and algorithms. This is why the group has had close associations with three departments: Mathematics, Biological Sciences, and Computer Science. Several of the faculty have joint appointments in these departments, as well as in the Keck School of Medicine.

Students wanting a Ph.D. in Applied Mathematics or Computer Science with an emphasis in computational biology may enroll directly in the appropriate Ph.D. programs. These students must satisfy the relevant degree requirements of their home department, in addition to a number of additional courses (chosen in consultation with faculty advisors) that ensure appropriate breadth in biology, computer science and mathematics. In Fall 2003 USC began a Ph.D. program in Computational Biology and Bioinformatics, based in the Department of Biological Sciences.

In contrast to the IDP we are proposing for UCLA, the USC program is run out of a single department. The emphasis in computational biology that is offered at USC, which does grant students access to faculty from three departments, differs from our proposal in that it supplements the existing Ph.D. degrees offered by these departments, and therefore requires substantially more coursework than the Ph.D. program we are proposing.

Stanford

The mission of the Stanford Biomedical Informatics (BMI) program is to train the next generation of researchers in biomedical informatics. Specifically, their students develop and apply methods for acquiring, representing, retrieving, and analyzing biomedical knowledge and data. The BMI training program encompasses bioinformatics, clinical informatics, and public health informatics. Bioinformatics focuses on methods for relevant to basic biology. Clinical informatics focuses on methods relevant to patient care. Public health informatics focuses on methods relevant to entire health systems. Examples from these areas can be found in the list of interests of their participating faculty.

As in the case of UCSF, Stanford’s program in bioinformatics is focused on medical applications and not basic science. As seen in the breadth of research of our core faculty, our research spans both medical informatics and basic science, and therefore offers graduate students a much broader research base to choose from.

UC Riverside

The Genetics, Genomics and Bioinformatics (GGB) Graduate Program leads to conferral of a Ph.D. in GGB, with a subspecialization in Genomics/Bioinformatics. The GGB Program also administers a full undergraduate curriculum in Genomics/Bioinformatics within the Biological Sciences major.

The program is diverse, with research opportunities ranging from plant breeding to population/evolutionary genetics to molecular biology to bioinformatics. Robust cross-disciplinary interactions are fostered, using genetics as a common theme. These collaborations often bridge fundamental and applied genetics that merge the basic and agricultural life sciences with statistics and computer sciences. This same intellectual diversity also offers incoming students a wide variety of choices for conducting their dissertation research. Trainees are able to choose among one of the three available curricular tracks. These tracks have been created so that there is significant overlap in coursework. Personalized academic programs are easily developed.

The Genomics & Bioinformatics track was implemented in the 2001-2002 academic year and is rapidly becoming a very popular option within the Genetics Ph.D. program. The track melds relevant Statistics, Computer Science and Life Science courses into a flexible curriculum that would appeal to computationally-oriented informaticians or to "wet-bench" -oriented genomicists. This curricular track has helped the GP retain its unique identity in an environment of rapid programmatic expansion in the College of Natural and Agricultural Sciences.

The primary difference between UC Riverside’s program and ours is that the Bioinformatics component is part of a broader program in Genetics, Genomics and Bioinformatics. We are proposing a IDP that is exclusively focused on Bioinformatics in order to attract the top students in this area.

UC Berkeley

The Graduate Group established the Designated Emphasis (DE) in Computational and Genomic Biology to provide specialized multi-disciplinary training and research opportunities in the different facets of computational biology and genomics. The DE is a specialization offered by existing Ph.D. programs. At UC Berkeley, acquiring a DE is like earning a “minor” with a Ph.D. degree.

In this sense, the Berkeley program differs significantly from ours, which would grant a Ph.D. in Bioinformatics, and not merely a minor. This should provide us a significant competitive advantage in recruiting students who are interested in bioinformatics research.

1-5c. Letters of evaluation from chairs of departments in related fields from the UC system and outside.

See Appendices D.

Section 1-6. Department or group which will administer the program

The Bioinformatics IDP will report to the Dean of Life Sciences and be administered by the IDP Graduate Program Committee. Program administration is described in Section 4-2.

Section 1-7. Plan for evaluation of the program by the offering department and campuswide

The IDP will first be reviewed after two years of operation, by an internal review committee consisting of the Executive Committee and additional members appointed by the Deans of the Schools of Medicine and Engineering and the Deans of the Divisions of Life Sciences and Physical Sciences. Results of the review will be provided to all four Deans. Subsequently, the IDP will undergo external review every eight years. We will also establish an external advisory board consisting of distinguished scientists from industry.

Section 1-8. Evidence that the different participating disciplines contribute to the total program in such a way that the student cannot achieve necessary knowledge without substantial study in two or more established departments

Bioinformatics is the marriage of biology and the information sciences. As such it depends on substantial study in both of these fields, which cannot be achieved without study in two or more existing departments. This is perhaps best illustrated by a typical example of a bioinformatics laboratory at UCLA. Bioinformatics analyses in a UCLA laboratory (Lee) have demonstrated the ubiquitous importance of alternative splicing in human genes. These analyses required thorough understanding of: the molecular biology of mRNA processing; the experimental methods, data, and pitfalls of genomics detection of mRNAs; computer science algorithms of sequence analysis and alignment; statistical methods for measuring evidence such as Hidden Markov Models, and hidden mixture deconvolution; computer database design and data-mining technologies; software engineering techniques for massively parallel computing. This collection of skills spanning several disciplines is the rule, not the exception, in bioinformatics.

Currently, there is no UCLA graduate program that offers instruction in all of these skills in one place.

Section 2. Program

Section 2-1. Undergraduate preparation for admission

2-1a. Field examinations and/or other pre-qualifying examinations

Applicants must take the Graduate Record Examination (GRE) General Test, and a GRE Subject Advanced Test in the area of the applicant’s undergraduate major is highly recommended. Since there is no GRE subject test in Bioinformatics, such students should take a subject test in Biology, Biochemistry, Computer Science or Mathematics.

2-1b. Qualifying examinations—written and/or oral

There is no requirement for an additional examination for admission to the IDP.

2-1c. Relationship of master’s and doctor’s programs

Admissions requirements will be identical for the M.S. and Ph.D. programs. Typical M.S. students will be primarily receiving Ph.D. degrees from other campus degree-granting programs. Consequently, M.S. students will have the option of transferring to the Ph.D. program subject to the approval of the program administration. Similarly, Ph.D. students transferring to alternate campus programs may receive the M.S. degree upon completion of its requirements.

2-1d. Special preparation for careers in teaching

No special preparation for teaching will be required for admission to the program.

2-1e. Other admissions requirements

Expected undergraduate preparations for the program fall into three major categories:

Bioinformatics and computational biology majors: an increasing number of universities are offering undergraduate majors in bioinformatics and computational biology. (UCLA itself offers undergraduate bioinformatics study via its Computational and Systems Biology major). This represents an ideal undergraduate preparation for the program, because it demonstrates the applicant’s performance in each of the essential subject areas—biology, mathematics, and computer science.
Double majors in biology and computer science or mathematics: in our view, the single greatest difficulty of bioinformatics is its interdisciplinary character. Most undergraduates only have strong preparation in one subject area, making it difficult to evaluate their likely performance in other areas that are essential for bioinformatics. We can control for this risk by recruiting students who have double majored in a life science plus a quantitative science, preferably computer science or mathematics.
Single majors with strong evidence of interdisciplinary skills: in some cases we will admit exceptionally strong students from a single traditional major. In this case, the student’s academic record and research experiences must show clear evidence of ability in other areas essential for bioinformatics, outside of his/her major. In particular, non-life science majors must demonstrate strong performance in relevant life science coursework. Similarly, life science majors must demonstrate strong quantitative skills in computer science and mathematics.

It should be emphasized that the program is seeking to build a small, high quality graduate program, and thus intends to be highly selective. For example, we intend to concentrate on students with double majors or (increasingly over time) computational biology majors.

The UCLA Computational and Systems Biology major with Specialization in Bioinformatics provides an example of a template of appropriate undergraduate preparation for the program, albeit with a strong engineering background (http://www.cs.ucla.edu/~cyber/major_majorfieldrequirements.htm).

Other undergraduate preparations that are more life science-oriented are equally valid. During this period of rapid change in bioinformatics and the early development of bioinformatics curricula, no fixed formula for appropriate preparation can be enforced. Flexibility and case-by-case evaluation of a student’s demonstrated skills and interests are essential.

Additionally, the program will place a strong emphasis on applicants’ bioinformatics research experience. Currently, most students entering UCLA via various departments to pursue bioinformatics have extensive bioinformatics research experience. Success in bioinformatics research, and strong letters of recommendation from bioinformatics faculty advisors, provide the program with clear evidence of a student’s ability to combine the interdisciplinary skills necessary for bioinformatics. This is very important, and will continue to be a vital consideration for admissions.

As is standard for all UCLA graduate programs, the Test of English as a Foreign Language (TOEFL) will be required for admissions of students who are not native speakers of English.

Section 2-2. Foreign language requirement

Ability in a foreign language, while recommended, is not required for the bioinformatics program. English is ubiquitous as the language of bioinformatics journals, conferences and discussion, and is spoken as a first or second language by nearly all researchers in the field.

Section 2-3. Program of Study

2-3a. Specific fields of emphasis

We believe that in bioinformatics, creative discoveries will come from "crossover" collaboration between experts in different disciplines, and that special training is required to give scientists the vocabulary and skills for this teamwork. Over the years we have developed and refined the following basic elements designed to address this challenge:

Bioinformatics Core Curriculum: All students must take the core curriculum, designed to teach the core concepts and methodologies which researchers must know to invent new kinds of bioinformatics. In other words, rather than just teaching examples of “bioinformatics past” (as is the case for some bioinformatics courses), these courses define the hard core of fundamental concepts—information theory, probabilistic measures of evidence, and computational complexity—that underlie the invention of each new kind of bioinformatics. Most importantly, the core curriculum gives students from widely varying backgrounds a common vocabulary and set of concepts, so they can communicate and collaborate with researchers throughout the field of bioinformatics.

We segment bioinformatics into a number of existing application areas, which are likely to grow in the future:

Genomics: focused on the bioinformatics of genome biology. Genomics examples include genome assembly, microarray gene expression analysis, QTL and disease gene mapping, alternative splicing, and comparative genomics.
Proteomics: the bioinformatics of protein structure and function. Proteomics examples include protein interaction networks, protein isoform identification by mass spectrometry, protein domain and structure prediction.
Neuroinformatics: bioinformatics of neuronal systems, including three-dimensional imaging of brain structures. Examples include brain mapping, image analysis.
Computer science: focused on algorithms and architectures of bioinformatics computation. Examples include Hidden Markov Models, bioinformatics database architecture, and massively parallel algorithms.
Math and Statistics: focused on mathematical analysis and models of bioinformatics problems. Examples include Bayesian statistical methodologies for gene expression analysis, statistical genetics, and bioinformatics applications of information theory.

2-3b. Plan(s): Masters I and/or II; Doctorate

M.S. candidates may fulfill the requirements of the degree either by writing a master’s dissertation or by passing an oral examination. The Ph.D. degree requires an oral qualifying exam and oral dissertation defense. Following completion of course requirements all students will be expected to take and pass a qualifying exam that will test their proficiency in each of the concentration areas and their ability to integrate the disparate disciplines.

2-3c. Unit requirements

Both M.S. and Ph.D. students must complete the core curriculum (three four-unit lecture courses and two three-unit lab courses), and an elective area (at least three four-unit courses from a designated list). M.S. students must also complete at least 12 units of laboratory research. Ph.D. students must complete an additional elective course, at least 12 units of laboratory rotation (596), and at least six units of seminar courses. This constitutes a total of 42 units for the M.S. degree, and 64 units for the Ph.D. degree.

2-3d. Required and recommended courses, including teaching requirement

Both M.S. and Ph.D. students must complete:

The Core Curriculum: Bioinformatics and Genomics (Chemistry CM260A, formerly Chemistry 260), Advanced Algorithms for Bioinformatics (Chemistry C260B, a new course pending approval), Statistical Methods in Computational Biology (Statistics M254, proposed to be cross-listed as Chemistry M260C), Bioinformatics Algorithms Laboratory (Chemistry 260BL, a new course), Bioinformatics Interdisciplinary Research Seminar (Chemistry 202, a year-long course), Advanced Methods in Computational Biology (Chemistry 252, a year-long course). See Section 5 for details.
Elective courses from concentration areas. Each student’s course list will be subject to approval by an IDP faculty advisor. See Section 5 for recommended course listings.

To help Bioinformatics IDP students to develop their teaching skills, they will be required to do a minimum of one quarter of teaching assistantship. Students will be encouraged to gain additional teaching experience both for bioinformatics courses and courses in their major area.

2-3e. Requirements for licensing or certification

There are no licensing or certification requirements for the program.

Section 2-4. Field examinations—written and/or oral

The program will not require a field examination.

Section 2-5. Qualifying examinations—written and/or oral

Ph.D. degree candidates, but not M.S. candidates, will be required to pass a written qualifying examination, consisting of a research proposal outside their dissertation research topic, and an oral qualifying exam defending their dissertation research proposal before their dissertation committee. Moreover, before taking the Ph.D. qualifying exams, the student must first complete the Core Curriculum. Following qualifying exams and advancement to candidacy, students must present a summary of their research to date and proposed future research within an oral seminar format. This summary should be presented approximately one year after advancement to candidacy.

Section 2-6. Thesis and/or dissertation expectations

M.S. degree candidates may opt to write a dissertation to fulfill the requirements of the M.S. degree. In this case, the candidate should choose an IDP faculty advisor and submit a dissertation proposal by the end of the third quarter of study. To proceed, the proposal must be approved by a faculty member who agrees to become the student’s dissertation advisor. Completed dissertations will be evaluated by a committee of at least three IDP faculty members, which must be approved by the IDP Director. The student must present the completed dissertation in a public seminar.

Ph.D. students must write a dissertation. During their first year, Ph.D. students will perform laboratory rotations with IDP faculty whose research interests them, and must select a dissertation advisor from the IDP faculty by the end of their third quarter. Next, students must select a qualifying committee by the end of their second Spring quarter, and submit a written dissertation proposal by the end of their second year. The qualifying committee must consist of the faculty advisor and at least three additional faculty, of whom two must be IDP faculty. The qualifying committee must be approved by the IDP Director and the campus graduate Dean. The written dissertation proposal must be given to all the qualifying committee members prior to the scheduled oral qualifying exam. Students will advance to candidacy when they pass the oral qualifying exam, complete the IDP course requirements, clear all incompletes from their transcript, and pay the filing fee. Students should advance to candidacy by the end of their third Fall quarter. Failure to advance to candidacy by the end of the fourth year will result in academic probation.

The completed dissertation must be submitted to the reading committee three weeks before the dissertation defense. The reading committee must consist of the faculty advisor and at least two additional readers from the IDP faculty, and must be approved by the IDP Director. The candidate must present a public seminar, immediately followed by the dissertation defense. The committee must determine whether the dissertation completely fulfills the requirements of original, important, publishable research in bioinformatics, and whether revisions are required. Ph.D. dissertations must conform to a high standard of originality, relevance and impact suitable for publication in peer-reviewed journals in the field. Successful completion of the dissertation and defense constitutes the final requirement for the Ph.D. degree.

Section 2-7. Final examination requirements

No written examinations are required for the M.S. or Ph.D. degrees. M.S. candidates may fulfill the requirements of the degree by passing an oral examination. Ph.D. candidates must pass both a qualifying examination and dissertation defense described above in Section 2-6.

Section 2-8. Special requirements over and above Graduate Division minimum requirements

The IDP imposes no special requirements.

Section 2-9. Relationship of masters and doctoral programs

The M.S. and Ph.D. programs are freestanding programs. Both programs can admit and train students independent of each other. However, it is reasonable to permit students to switch between programs. Thus, M.S. students will be allowed to proceed to the Ph.D. program, subject to approval by the IDP Director. Similarly, Ph.D. students can receive the M.S. degree upon completion of all its requirements.

Section 2-10. Special preparation for careers in teaching

The IDP curriculum presents numerous opportunities for graduate students to gain teaching experience. Students interested in teaching careers will be strongly encouraged to gain experience as teaching assistants both in undergraduate courses (e.g., Chemistry C160A) and graduate courses in bioinformatics. All students in the program must complete at least one quarter of teaching assistantship in bioinformatics courses or in courses of participating departments.

Section 2-11. Student sample program for each year

Ordinarily students will complete the IDP curriculum in their first year.

Table 1. Sample Student Program for the First Year

Fall	Winter	Spring
Chem CM260A (Intro. to Bioinformatics and Genomics)	Chem C260B (Algorithms in Bioinformatics and Systems Biology)	Stats M254 (in future Chem M260C?) (Statistical Methods in Computational Biology)
elective	Chem 260BL (Advanced Bioinformatics Computational Laboratory)	elective
Chem 202 (Bioinformatics Interdisciplinary Research Seminar)	elective	Chem 202 (Bioinformatics Interdisciplinary. Research Seminar)
Chem M252 (Seminar: Advanced Methods in Computational Biology)	Chem 202 (Bioinformatics Interdiscip. Research Seminar)	Chem M252 (Seminar: Advanced Methods in Computational Biology)
	Chem M252 (Seminar: Adv. Methods in Computational Biology)

Ordinarily students would be expected to complete the Oral Qualifying Exam and Bioinformatics Practicum during the Summer quarter of their first year, and complete their Written Qualifying Exam and Midstream Seminar by the end of their second year.

2-12. Normative time from matriculation for degree (assuming student has no deficiencies and is enrolled full-time)

2-12a. Normative lengths of time for pre-candidacy and candidacy periods

We expect M.S. students to complete the program within two years, and perhaps in as little as five quarters. We expect Ph.D. students to take two years to advance to candidacy, and three additional years to complete the dissertation. This is similar to the ACCESS program, whose average time to completion of the Ph.D. degree is five years and one quarter.

2-12b. Other incentives to support expeditious times-to-degree

The IDP Student Advising Committee will conduct a yearly progress review of all students, based upon coursework performance, research achievements, and faculty advisor comments. Students who are not progressing at the expected pace or evidently experiencing difficulties will be contacted by the IDP Graduate Advisor to discuss the problem and recommend solutions. Students who fail to make adequate progress towards completion (as defined by the Graduate Handbook on satisfactory academic progress) may be dismissed from the program. Students must complete the core curriculum within the first two years. Students normally must complete all course requirements by the end of the second year (M.S.) or third year (Ph.D.). Ph.D. students should advance to candidacy by the end of their third year. Failure to advance to candidacy by the end of the fourth year will result in academic probation.

Section 3. Projected Need

Section 3-1. Student demand for the program

3-1a. Documentation of demand for program with three to five years of enrollment and admissions statistics from this or other institutions, or data on rate of student inquiries

UCLA established a bioinformatics curriculum in 1999. Since then we have recommended to applicants interested in a Bioinformatics Ph.D. that they apply to an existing UCLA graduate program (such as ACCESS) and then complete the bioinformatics curriculum.
In the two years that we recorded contacts (by suggesting that interested applicants register via our online website), we received inquiries from 315 students in 2000, and 961 students in 2001. Given that this number of inquiries greatly exceeded our capacity to respond, we halted use of the online inquiry system in 2002.

Table 2. Enrollments of Bioinformatics Core Courses

Academic Year	Course	Final Enrollment
1999-0	Chemistry 202	25
	Chemistry 160/260	8 + 16
2000-1	Chemistry 202	13
	Chemistry 160/260	21 + 20
2001-2	Chemistry 202	7
	Chemistry 160/260	23 + 28
	Statistics 254	34
2002-3	Chemistry 202	10
	Chemistry 160/260	34 + 28
	Statistics 254	30
2003-4	Chemistry 202	11
	Chemistry 160/260	12 + 14
	Statistics 254	19
2004-5	Chemistry 160/260	20 + 11
	Statistics 254	22
2005-6	Chemistry 160/260	26 + 10

3-1b. Evidence that this demand will be stable and long-lasting

There is abundant evidence of a widespread and long-term commitment to bioinformatics research.

In the published literature: over the past few years several bioinformatics scientific journals have been created. These include:

o PLoS Computational Biology

o BMC Bioinformatics

o Bioinformatics

o Briefings in Bioinformatics

o Briefings in Functional Genomics and Proteomics

o Computers in Biology and Medicine

o Computer Methods and Programs in Biomedicine

o Human Genomics

o Human Molecular Genetics

o Journal of Biomedical Informatics

o Journal of Computational Biology

In NIH funding: the NIH has funded seven centers of computational biology throughout the country. One of these, the Center for Computational Biology, is located here at UCLA.
In faculty hiring: four of the bioinformatics core faculty have been hired within the last year (Eskin, Pellegrini, Graber and Mallick), and one will be coming to UCLA in Fall 2007 (Novembre).
In establishment of major national laboratories devoted to bioinformatics and genomics not only in the United States (the National Center for Biotechnology Information, and the National Human Genome Research Institute, both in Bethesda, MD), but also the European Union (the European Bioinformatics Institute, with multiple sites).
In growth of demand for bioinformatics databases and queries. For example, the National Center for Biotechnology Information is the top online bioinformatics resource in the world. In 2000, its databases received 3,000,000 bioinformatics queries per day from users worldwide. In 2002, its databases processed an average of more than 7,000,000 bioinformatics search requests per day. This demonstrates the extremely broad usage of bioinformatics tools not only by academic and industrial researchers, but also by doctors, lawyers and even the public at large.
In the creation of bioinformatics societies: e.g., The International Society for Computational Biology, which organizes several international bioinformatics meetings (ISMB and RECOMB).

3-1c. For new programs that are extensions of existing disciplines, enrollment statistics from related courses to demonstrate demand

Not applicable.

3-1d. Statistics or other documentation of need

See Section 3-2b below.

Section 3-2. Opportunities for placement of graduates

3-2a. Placement records of other UC programs in the field in recent years

This data has not been available.

3-2b. Demonstration of a strong market for program graduates by listing recent job listings, employer surveys, assessments of future job growth

To demonstrate the strong market demand for bioinformaticists, we list below the job openings posted on the web site of the International Society for Computational Biology for July through September of 2006. There is a total of about 50 listings in both academia and industry.

2006-09-12		Postdoctoral Position in Plant Computational Biology	University of Dundee, Scotland, UK
2006-09-11	USA-SC-Columbia	Assistant Professor	University of South Carolina
2006-09-11	USA-SC-Columbia	Assistant Professor	University of South Carolina
2006-09-08	Singapore-Singapore	Post-Doctoral Research Fellow and Scientist: Computational Biologist/Bioinformatician	Genome Institute of Singapore
2006-09-08	France-Paris	Postdoc in Systems Biology/Data integration	Pasteur Institute
2006-09-08	France-Paris	Postdoc in mass spectrometry algorithms	Pasteur Institute
2006-09-06	USA-ILLINOIS-Maywood	Post-Doctoral Research Associate	Loyola University Medical Center
2006-09-05	USA-NJ-Newark	Bioinformatics Researcher	UMDNJ-New Jersey Medical School
2006-09-01	France-Grenoble	Post-doctoral position in Systems Biology	INRIA
2006-08-30	USA-Arizona-Phoenix	Scientific Programmer	TGen
2006-08-28	USA-MO-St. Louis	Post doctorate researcher/research assistant in computational plant genome analysis	Donald Danforth Plant Science Center
2006-08-28	USA-MI-Ann Arbor	Post-Doctoral Position in Bioinformatics and Proteomics	University of Michigan
2006-08-28		Post-doc in Computational Biology and Bioinformatics	NYU School of Medicine
2006-08-25	45056-OH-Oxford	Ph.D./M.S. Assistantships in Plant Genomics and Bioinformatics	Miami University
2006-08-23	USA-Delaware-Newark	Postdoc Research Fellow	University of Delaware
2006-08-22	USA-CO-Boulder	Postdoctoral researcher in computational molecular biology	University of Colorado at Boulder
2006-08-22	South Korea----Seoul	Graduate studentship	Research Center for Women’s Diseases, Sookmyung Univ.
2006-08-20		Postdoc positions in microbial pathway/network modeling and simulation	University of Georgia
2006-08-17	USA-CA-La Jolla	Bioinformatics postdoc at UCSD	UCSD
2006-08-14	USA-Iowa-Ames	Tenure-track Position in Systems Biology	Iowa State University
2006-08-14	Iowa-Ames	PLEXdb Curator	Iowa State University
2006-08-14	Iowa-Ames	PLEXdb Bioinformaticist	Iowa State University
2006-08-09	Italy-Tuscany-Siena	BIOINFORMATICS SCIENTIST	Siena Biotech S.p.A.
2006-08-09	Italy-Tuscany-Siena	Postdoctoral position in Bioinformatics/Proteomics	Siena Biotech S.p.A.
2006-08-08	US-Massachusetts-Boston	Post-Doc position in Computational Biology at HMS	Harvard Medical School / Children’s Hospital
2006-08-04	France-Lyon	Post-doc in bioinformatics	French Institute of Computer Science (INRIA)
2006-08-01	USA-California-Los Angeles	Presidential Professorship in Mathematical Biology	Loyola Marymount University
2006-07-30	USA-California-Los Angeles	Post-doc in Computational Biology and Bioinformatics	University of Southern California
2006-07-28	TX-Houston	Postdoc Position in Bioinformatics	University of Houston
2006-07-27	USA-GA-Athens	Postdoc position in bioinformatics/computational biology	University of Georgia
2006-07-27	USA-Georgia-Atlanta	Influenza Bioinformatics Scientist	Centers for Disease Control and Prevention
2006-07-26	USA-NC-Chapel Hill	Post-Doc opportunity in Plant Genome Sciences	University of North Carolina at Chapel Hill
2006-07-26	USA-MA-Boston	Postdoctoral Fellow	Boston College
2006-07-26	Canada-ON-Toronto	Postdoctoral positions in computational biology/bioinformatics of cellular networks	University of Toronto
2006-07-24	USA-Michigan-East Lansing	Faculty Positions in Plant Proteomics and Quantitative Biology	Michigan State University
2006-07-24	United Kingdom-Berkshire-Bracknell	Computational Biologist	Syngenta Crop Protection AG
2006-07-23	USA-NY-New York	Postdoctoral position in Computational Biophysics	Weill Medical College of Cornell University
2006-07-20	USA-New York-New York	Systems programmer/administrator	Memorial Sloan-Kettering Cancer Center
2006-07-18	USA-OH-Oxford	Postdoc in bioinformatics/computational biology	Miami University
2006-07-17	USA-AR-Jefferson	Oracle DBA	Z-Tech Corporation
2006-07-14	USA-Wisconsin-Madison	Bioinformatics Researcher	Center for Eukaryotic Structural Genomics--Univ. of Wisconsin
2006-07-13	USA-CA-Palo Alto	Research Positions in Biocomputation/Bioinformatics	Stanford University School of Medicine
2006-07-10	USA-Maryland-Baltimore	Postdoctoral fellowship in computational biology of angiogenesis	Johns Hopkins University
2006-07-07	USA-GA-Athens	postdoc positions in bioinformatics	University of Georgia
2006-07-07	Canada-ON-Ottawa	Tenure-track faculty appointment in the area of biomedical engineering or bioinformatics	University of Ottawa
2006-07-05	Canada-BC-Vancouver	Bioinformatics Post-Doc	University of British Columbia (Vancouver)
2006-07-03	USA-CA-Irvine	Postdoctoral Positions in Bioinformatics/Chemoinformatics	University of California, Irvine
2006-07-01	France-Bordeaux	Bioinformatics engineer	LaBRI, University of Bordeaux
2006-06-28	USA-Kansas City	Postdoctoral or Pre-Doctoral Research Associate	Stowers Institute for Medical Research
2006-06-26	USA-TX-Houston	Chair, Department of Medical Informatics	The University of Texas M. D. Anderson Cancer Center
2006-06-22	Japan-Yamagata-Tsuruoka	Computational Biology Software Project Leader	Keio University / E-Cell Project
2006-06-19	USA-CA-Santa Barbara	postdoctoral scholar	University of California at Santa Barbara
2006-06-16		postdoc	Johns Hopkins
2006-06-16	USA-MD-Rockville	Bioinformatics Engineer	The Institute for Genomic Research
2006-06-16	USA-MD-Rockville	Bioinformatics Analyst	The Institute for Genomic Research

Section 3-3. The importance of the new program to the discipline

UCLA will help prepare the next generation of bioinformatics scientists, emphasizing core strengths that are needed by the field. First, UCLA provides strongly integrated training that brings together perspectives (genomics, proteomics, computer science, mathematics and statistics) that are often distant from each other in the existing bioinformatics community. The fact that UCLA brings together many bioinformatics faculty in each of these four areas, joining together the full power of world-class schools in medicine, engineering, and life and math/physical sciences, is a major strength replicated in very few universities worldwide. Second, UCLA has translated this strong integration and collaboration from its research into its graduate training. Our students receive explicit training in the skills of interdisciplinary collaboration, and will be able to cross academic boundaries and collaborate with bioinformatics researchers from other backgrounds.

These skills are important for the field of bioinformatics because it is new, requires interdisciplinary collaboration, and has a serious shortage of scientists who are able to work successfully across the boundaries of fields as distant as molecular biology and computer science. Bioinformatics is a brand-new field; most of the fundamental science needed to deliver on its promises (e.g., the ability to understand how the human genome encodes the capabilities of the human mind) has yet to be invented. Until researchers from many different backgrounds are able to work together in both a collaborative and creative way, these incredible challenges will remain unsolved. Currently, there is a shortage of bioinformatics scientists who are able to bridge such gaps in the way that is needed. This need has been pointed out by study after study at both institutional and national levels. We believe that UCLA can make a valuable contribution to this need.

Section 3-4. Ways in which the program will meet the needs of society

As technology advances, experimental techniques used in biomedical labs will continue to generate more and more data. While only a decade ago a typical experiment generated only a handful of data points, current experimental techniques generate thousands. Furthermore, there are rapidly growing repositories of this data throughout the web. This revolution is changing the very nature of biomedical research. It is becoming increasingly difficult for young scientists to make important contributions in their field unless they are adept at analyzing and extracting information from this data.

Bioinformatics training is therefore a critical component of our scientific training. Without a strong foundation in this field, our society will no longer be able to compete and make contributions in biomedical research. It is clear not only that the IDP in bioinformatics proposed here will contribute to enhancing the scientific base of a few graduate students at UCLA, but that this training will have far reaching implications for the competitiveness of biomedical research in the US over the next few decades.

Section 3-5. Relationship of the program to research and/or professional interests of the faculty

3-5a. Core faculty

James Bowie: Chemistry & Biochemistry – computational methods to predict membrane protein structure; analysis of primary sequence determinants of membrane protein structure.
Joseph DiStefano: Computer Science – systems biology.
David Eisenberg: Chemistry & Biochemistry – protein interaction networks; structural genomics; prediction of protein folding by 3D profile methods; comparison and analysis of protein structures, and protein sequence-structure relationships.
Eleazar Eskin: Computer Science – computational analysis of genetics and genomics data.
Tom Graeber: Molecular & Medical Pharmacology – understanding cancer signaling from a systems viewpoint.
Steve Horvath: Biostatistics – allelic association and DNA microarray data analysis. Identification and characterization of disease genes.
James Lake: Molecular, Cell & Developmental Biology – bioinformatics analyses of genome evolution, focusing on the evolution and origins of eukaryotes and prokaryotes.
Ken Lange: Biomathematics – computational genetics, population genetics, gene mapping, radiation hybrids, pedigree analysis, biomedical imaging, computational statistics, applied stochastic processes.
Christopher Lee: Chemistry & Biochemistry – evidence-confidence computation for genome-wide wide analyses of raw experimental data. High-throughput discovery of single- nucleotide polymorphisms, alternative splicing, and gene structure. Proteome annotation and databases.
Ker-Chau Li: Statistics – high dimensional data analysis.
James Liao: Chemical Engineering – intracellular regulation and metabolism at the systems level.
Parag Mallick: director of clinical proteomics at Cedars-Sinai Medical Center and Chemistry and Biochemistry – bioinformatics with a focus on mass spectrometry.
John Novembre: Ecology and Evolutionary Biology (Fall 2007) -- genetic variation within species and genomic divergence between species
Stott Parker: Computer Science – databases and data-mining. Novel database architectures for genomics.
Matteo Pellegrini: Molecular, Cell and Developmental Biology – development of computational approaches to model and interpret genomic data with a focus on the reconstruction of molecular networks.
Chiara Sabatti: Statistics – statistical genetics, Markov Chain Monte Carlo, Bayesian statistics.
Janet Sinsheimer: Human Genetics – statistical modeling of evolution and genetics.
Marc Suchard: Biomathematics – statistical methodology; genetics; evolution.
Todd Yeates: Chemistry & Biochemistry – structure proteomics - working to discover and determine new protein folds. Protein evolution - investigating unusual mechanism of protein evolution. Functional genomics – working on methods to infer function from sequence.
Qing Zhou: Statistics – development of statistical methods in cis-regulatory analysis, including motif and cis-regulatory module discovery, gene expression analysis, ChIP-chip data analysis, and comparative genomics.

3-5b. Associate faculty

Guillaume Chanfreau: Chemistry & Biochemistry – RNA structure and processing.
Ivo Dinov: Statistics – brain image analysis.
Fred Fox: Microbiology, Immunology and Molecular Genetics – bioinformatics in industry; industrial contacts.
Siavash Kurdistani: Biological Chemistry – genomic studies of chromatin biology.
Ralf Landgraf: Biological Chemistry – structure-function relationships of macromolecular interactions.
Joseph Loo: Chemistry and Biochemistry – mass spectrometry.
Stanley Nelson: Human Genetics – DNA microarrays. Identification and characterization of disease genes
Peipei Ping: Physiology, Medicine, and Cardiology – functional proteomic analysis of the normal, ischemic and protected myocardium.
Stefano Soatto: Computer Science – image processing and shape analysis.
Charles Taylor: Ecology and Evolutionary Biology – population genetics and adaptation; population structure of malaria vectors; adaptation in sensor arrays, especially for bioacoustics.
Arthur Toga: Neurology – brain imaging.
Yingnian Wu: Statistics – image modeling, statistical computing.

Section 3-6. Differentiation of the proposed program from existing UC and California independent university programs, and from similar programs proposed by other UC campuses

The program we propose here is focused on providing skills to perform innovative bioinformatics research. This will involve both basic and applied research. Some of the other programs in California universities focus more on medical data and therefore more on applied research. Other programs combine informatics with experimental techniques used for genomic research (e.g., Riverside). Other programs do not provide a Ph.D., and therefore mainly provide an addition to existing graduate programs (e.g., UC Berkeley).

The key points that differentiate our program from others are therefore:

A large list of hardcore bioinformatics faculty unifying the approaches of four different schools: Medicine (Human Genetics; Biomathematics; Biostatistics; MMP); Engineering (Computer Science; Chemical Engineering); Life Sciences (MCDB; EEB); Physical Sciences (Statistics; Chemistry).
A rigorous, integrated core curriculum teaching the fundamental principles for inventing new kinds of bioinformatics far into the future.
A Bioinformatics Ph.D., and a Masters program, designed for students with a bioinformatics / computational biology undergraduate degree, or equivalent high level of preparation for the field.

We believe that these are the critical components that will make this program attractive to undergraduate students focused on pursuing a research career in bioinformatics and distinct from other programs offered by California universities.

Section 4. Staff

Section 4-1. Program Faculty

There are 20 core faculty involved in teaching the required and elective courses of the proposed IDP. The core faculty are listed in Section 3-5a, and associate faculty are listed in Section 3-5b. Faculty hiring plans are described in Section 1-3a.

4-1a. List of program faculty, their ranks, their highest degree and other professional qualifications, and a citation of no more than five recent publications (abbreviated curricula vitae); Data concerning faculty limited to that information pertinent to the committee’s evaluation of faculty qualifications

See Section 3-5 above. Brief curriculum vitae of core and associate faculty are in Appendix C.

4-1b. Comments from chairs of departments with graduate programs and/or faculty closely related to or affected by the proposed program

See Appendix D.

4-1c. For participating faculty members outside of the sponsoring department, copies of letters indicating their interest in the program (critical for interdisciplinary programs)

See Appendix E.

Section 4-2. Organizational Structure

The Chair of the IDP will be recommended to the Dean of the Graduate Division by the Dean of Life Sciences, as specified by the regulations of the Academic Senate that govern the appointment of IDP chairs. The Executive Graduate Program Committee for the IDP will be constituted of no less than four members, each representing one of the participating Schools or Divisions – Medicine, Engineering, Life Sciences and Physical Science. Executive Committee members will normally serving a three year term. Members of the committee will be recommended to the Dean of the Graduate Division by the Chair of the IDP and the Dean representing the home Division of School of the Executive Committee member. Additional members may be recommended to the Dean of the Graduate Division by agreement of the Chair of the IDP and the IDP Executive Committee. Members will be selected to provide broad representation of the areas of specialization. The duties of the Graduate Program Committee will be to determine policy for the administration of the IDP, select students for admission to the IDP, review and approve course selections for meeting each student's requirement for coursework, and approve selection of the student's advisory committee. The IDP will be evaluated and advised by a UCLA Faculty Advisory Committee with representatives from academic units outside Life Sciences, an External Advisory Board, and an Industrial Advisory Board. The proposed organizational structure of the IDP is illustrated below:

Section 4.3 Sources of available fellowship and assistantship support

Doctoral students will be supported through intra and extramural resources including teaching assistant appointments through Life Science, Physical Science or Engineering, unrestricted fellowship funds provided through formulaic allocations to the IDP from the Graduate Division, institutional NRSA training grants including but not restricted to Cell and Molecular Biology, Genetics, Biotechnology, and Genomic Analysis, as well as through GSR appointments from research grants obtained by their mentors. See Section 7 for details.

Section 5. Courses

Section 5-1. Present and proposed courses, with planned instructors, and supporting courses offered in related fields

Table 3. Present and Proposed Courses for the Program

Course Title	Instructors	Status	Major Area	Schedule
Core Curriculum
Introduction to Bioinformatics & Genomics (Chem CM260A)	Lee (Chem)	Existing	Core	Fall, annually
Advanced Algorithms in Bioinformatics (Chem C260B)	Mallick (Chem)	Existing (taught W07)	Core	Winter, annually
Bioinformatics Algorithms Laboratory (Chem 260BL)	Eskin (CS), Mallick (Chem)	Pending (Winter 2008)	Core	Winter annually
Statistical Methods in Computational Biology (Stat 254/Chem 260C)	Sabbati (Hum Gen / Statistics)	Existing	Core	Spring, annually
Bioinformatics Interdisciplinary Research Laboratory (Chem 202)	Lee (Chem) Pellegrini (MCDB), core faculty	Existing	Core	Fall, Winter, Spring, annually
Additional Required Courses
Computational Biology Research Seminar (Chem M252)	Eisenberg (Biol Chem)	Existing	Required	Fall, Winter, Spring, annually
Laboratory Rotation (596)	Core faculty	Existing	Required for Ph.D.	Every quarter

Section 5-2. Elective Bioinformatics Courses

Full course catalog descriptions of all required and elective courses are included in Appendix F.

Ecology and Evolutionary Biology M231. Molecular Evolution. (Same as Earth and Space Sciences M217.) Lecture, two hours; discussion, two hours. Series of advanced topics in molecular evolution, with special emphasis on molecular phylogenetics. Topics may include nature of the genome, neutral evolution, molecular clocks, concerted evolution, molecular systematics, statistical tests, and phylogenetic algorithms.

Biomath M203. Stochastic Models in Biology. (Same as Human Genetics M203.) Lecture, four hours. Requisite: Mathematics 170A or equivalent experience in probability. Mathematical description of biological relationships, with particular attention to areas where conditions for deterministic models are inadequate. Examples of stochastic models from genetics, physiology, ecology, and a variety of other biological and medical disciplines.

Biomath M207A. Theoretical Genetic Modeling (Formerly numbered 207.) (Same as Biostatistics M237A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 115A, 131A, Statistics 100B Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny.

Biomath 210. Optimization Methods in Biology. Lecture, four hours. Preparation: undergraduate mathematical analysis and linear algebra; familiarity with programming language such as FORTRAN or C. Modern computational biology relies heavily on finite-dimensional optimization. Survey of theory and numerical methods for discrete and continuous optimization, with applications from genetics, medical imaging, pharmacokinetics, and statistics.

Biomath M211. Mathematical and Statistical Phylogenetics. (Same as Human Genetics M211.) Lecture, three hours; laboratory, one hour. Requisites: Biostatistics 110A, 110B, Mathematics 170A. Theoretical models in molecular evolution, with focus on phylogenetic techniques. Topics include evolutionary tree reconstruction methods, studies of viral evolution, phylogeography, and coalescent approaches. Examples from evolutionary biology and medicine. Laboratory for hands-on computer analysis of sequence data.

Biostatistics M272. Theoretical Genetic Modeling. (Formerly numbered M237A.) (Same as Biomathematics M207A and Human Genetics M207A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 115A, 131A, Statistics 100B. Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny.

Biostatistics M278. Statistical Analysis of DNA Microarray Data. (Formerly numbered 278.) (Same as Human Genetics M278.) Lecture, three hours. Requisite: course 200C. Instruction in use of statistical tools used to analyze microarray data. Structure corresponds to analytical protocol an investigator might follow when working with microarray data.

Chemistry 298. Seminar: Computational Methods in Bioinformatics. To be proposed. Discussion, one hour. Detailed analysis of new methods, including algorithms and representation of bioinformatics data, and research problems ranging from genome-wide sequence analysis, to fold recognition, to energy calculation and protein design.

Computer Science 249. Current Topics in Data Structures. Lecture, four hours; outside study, eight hours. Review of current literature in an area of data structures in which instructor has developed special proficiency as a consequence of research interests. Students report on selected topics.

Human Genetics 244. Genomic Technology. Lecture, three hours; discussion, one hour. Requisite: Life Sciences 4. Survey of key technologies that have led to successful application of genomics to biology, with focus on theory behind specific genome-wide technologies and their current applications. Concurrently scheduled with course C144.

MCDB 292. Seminar: Molecular Evolution. Discussion, three hours. Detailed analysis of current understanding of evolution of molecular sequences and structures.

Pathology 255. Mapping and Mining Human Genomes. Lecture, 90 minutes; discussion, 90 minutes. Basic molecular genetic and cytogenetic techniques of gene mapping. Selected regions of human genomic map scrutinized in detail, particularly gene families and clusters of genes that have remained linked from mouse to human. Discussion of localizations of disease genes.

Statistics 165: Statistical Methods and Data Mining. Lecture, three hours. Requisite: course 100A. Introduction and overview of up-to-date statistical methods in microarray analysis designed for students in biostatistics, statistics, and human genetics who are interested in technology and statistical analysis of microarray experiments. Useful for biology students with basic statistical training who are interested in understanding logic underlying many statistical methods.

Neuroinformatics

Neuroscience/Physiological Science CM272. Neuroimaging and Brain Mapping. (Same as Physiological Science M272 and Psychology M213.) Lecture, three hours. Requisites: courses M201, M202. Theory, methods, applications, assumptions, and limitations of neuroimaging. Techniques, biological questions, and results. Brain structure, brain function, and their relationship discussed with regard to imaging.

Statistics 233 - Statistical Methods for Biomedical Imaging. Lecture, three hours. Requisite: course 100A. Brief review of common general statistical techniques. Advanced statistical methods for analysis of medical imaging, integration, visualization, interrogation, and interpretation of imaging and nonimaging metadata.

(http://www.stat.ucla.edu/%7Edinov/courses_students.dir/04/Spring/Stat233.dir/STAT233.html)

Biomedical Physics 208A. Medical Physics Laboratory: Medical Imaging. Discussion, two hours; laboratory, four hours. Requisite: course 205. Hands-on experience performing acceptance testing and quality control checks of imaging equipment such as fluoroscopy, digital subtraction angiography, mammography, ultrasound, magnetic resonance imaging, computed tomography, and computed radiography.

Biomedical Physics 210. Principles of Medical Imaging. Lecture, three hours; discussion, one hour. Requisite: course 209. Study of image representation, computational structures for imaging, linear systems theory, image enhancement and restoration, image compression, segmentation, and morphology. Special topics include visualization techniques, three-dimensional modeling, computer graphics, and neural net applications. Laboratory projects apply concepts developed in class.

Biomedical Physics 214. Medical Image Processing Systems. Lecture, three hours; discussion, one hour. Requisites: courses 209, 210. Advanced image processing and image analysis techniques applied to medical images. Discussion of approaches to computer-aided diagnosis and image quantitation, as well as application of pattern classification techniques (neural networks and discriminant analysis). Examination of problems from several imaging modalities (CT, MR, CR, and mammography).

Biomedical Physics M266. Advanced Magnetic Resonance Imaging. (Same as Neuroscience M267 and Psychiatry M266.) Lecture, four hours. Starting with basic principles, presentation of physical basis of magnetic resonance imaging (MRI), with emphasis on developing advanced applications in biomedical imaging, including both structural and functional studies. Instruction more intuitive than mathematical.

Biomedical Physics M285. Functional Neuroimaging: Techniques and Applications. (Same as Psychiatry M285.) In-depth examination of activation imaging, including PET and MRI methods, data acquisition and analysis, experimental design, and results obtained thus far in human systems. Strong focus on understanding technologies, how to design activation imaging paradigms, and how to interpret results. Laboratory visits and design and implementation of a functional MRI experiment.

Biomedical Engineering M217. Biomedical Imaging. (Same as Electrical Engineering M217.) Lecture, three hours; laboratory, two hours; outside study, seven hours. Requisite: Electrical Engineering 114D or 211A. Mathematical principles of medical imaging modalities: X-ray, computed tomography, positron emission tomography, single photon emission computed tomography, magnetic resonance imaging. Topics include basic principles of each imaging system, image reconstruction algorithms, system configurations and their effects on reconstruction algorithms, specialized imaging techniques for specific applications such as flow imaging.

The following supporting courses are offered in related fields: Statistics 100A (or equivalent preparation) is required as a prerequisite for Chemistry CM260A. CS 31 (or equivalent programming skills) is required for Chemistry C260BL (Bioinformatics Algorithms Laboratory). The Program in Computing offers a range of courses (PIC 10ABC, PIC 20AB, PIC 60, PIC 110) that are also very useful for bioinformatics students. The many departments of the Medical School and Life Sciences offer a wide range of coursework on biology that is highly relevant to bioinformatics students.

Section 5-3. Program degree requirements

See Appendix G.

Section 6. Resource Requirements and Enrollment Plans

Section 6-1. Methods for funding

Bioinformatics research has been an integral part of the Life Science, Physical Science, Medical and Engineering departments at UCLA for several years. The development of this degree program utilized resources already available to faculty and research programs without significant additional costs.

6-1a. FTE faculty

The core faculty, consisting of 20 existing FTE, will teach, administer and support the activities of the proposed IDP. As documented in Sections 4 and 5, the existing faculty is more than adequate to provide this graduate program at its project enrollment levels. UCLA is indeed fortunate to have a group of bioinformatics faculty already assembled and working together. As early as 1999, a strategic planning group for the Schools of Medicine, Life Sciences, and Physical Sciences, under the direction of Deans Levey, Eiserling and Peccei, recommended that “the highest research priority area that emerged from this process is bioinformatics. We have interacted considerably on these issues and we feel strongly also that this area should be UCLA’s highest priority.” The strength of the current bioinformatics core faculty demonstrates UCLA’s success in achieving this goal. A variety of departments have made recent hires of new faculty working in the field of bioinformatics; for example, in the last two years: Ralf Landgraf (Biological Chemistry), Matteo Pellegrini (MCDB), Thomas Graeber (Molecular and Medical Pharmacology), Parag Mallick (Chemistry and Biochemistry), Eleazar Eskin (Computer Science), and John Novembre (Ecology and Evolutionary Biology). Long-term campus plans have called for hiring several additional faculty members in bioinformatics over the next five years. Startup costs for each new faculty hire are likely to average $500,000, with annual salaries plus benefits of $100,000.

6-1b. Staff FTE

The program will be administered by existing student affairs staff (Student Affairs Officer II) in the Department of Molecular, Cell and Developmental Biology and the umbrella unit, Life Sciences South Administration (MSP I, Functional Manager) in addition to current duties. It is anticipated that less that 50% FTE will be required to provide administrative support for student counseling, admissions processes and IDP administration.

6-1c. Library acquisitions

The Library’s current holdings are sufficient to support the proposed program. Bioinformatics, biology and computer science journals are largely available on-line via the Worldwide Web. In addition to major initiatives such as PubMed Central that are providing universal free access to a large number of journals in the field, the UCLA Library has negotiated extensive online access agreements that cover virtually all of the journals in the bioinformatics field. The current situation regarding journal access is completely satisfactory for the needs of the proposed IDP.

Regarding library holdings, it would be helpful to select one library on campus to act as a center for bioinformatics holdings. However, this is by no means a critical issue for creation of the IDP.

No retrospective materials (monographic and serial fills) are required for graduate level research. Bioinformatics research is distributed via two mechanisms: publication in peer-reviewed journals; and via the Internet (e.g., making software available on a website for download by other researchers, or creating an online database which researchers can access over the Internet).

The Library currently subscribes to the most important serial publications (print or electronic) in the field. Current subscriptions and journal online access agreements are entirely satisfactory for the needs of the IDP.

6-1d. Computing costs

Computing costs constitute a major component of research expenditures, and have been paid from several sources in the past: 1) faculty research grants; 2) private foundation grants (e.g., grants from the Keck Foundation and Sun Microsystems); 3) multi-investigator “center” or training grants. The most recent equipment award was a $200,000 supplement in 2002 from UCLA’s NSF-IGERT, for shared computing equipment for IGERT Bioinformatics students. Grant applications for replacing or upgrading computer facilities will be necessary in the future. The projected number of new student enrollments under this program does not pose a serious additional burden on existing computing resources.

6-1e. Equipment—inventory of current equipment and future needs

UCLA Bioinformatics has many years of experience building and running powerful clusters of low-cost LINUX PC nodes. Currently, the computing resources of the IDP faculty include over 1000 CPUs and approximately 50 terabytes of RAID disk storage. These needs are driven primarily by the faculty’s research requirements, not by IDP training requirements. As noted above in Section 6-1d, we already have sufficient computer power specifically for the projected IDP enrollment.

As one example, UCLA’s NSF IGERT training grant provided funds for establishment of a 150 CPU computer cluster specifically for bioinformatics graduate students. The Dean of Physical Sciences supplied $30,000 for installation of sufficient air conditioning for the heat load of this cluster. Space for the IGERT cluster (in Young Hall 2069) was provided by the Department of Chemistry and Biochemistry.

6-1f. Space and other capital facilities—inventory of current facilities and future requirements

No new space or capital facilities will be required for this program beyond that already used by program faculty.

6-1g. Other operating costs (technical and administrative staff, supplies and expenses, lab maintenance and other facilities) and description of current staffing levels and future requirements

Funds to support the periodic production of recruitment materials and development of a speakers program will be coordinated through the Dean of Life Sciences with appropriate level contributions solicited from Medicine, Engineering and Physical Sciences.

Section 6-2. Projected doctoral enrollments for the first five years

Table 4. Projected Doctoral Enrollments

Year	New	Continuing	Total
1	3	0	3
2	5	3	8
3	5	8	13
4	5	13	18
5	5	18	23

Section 7. Graduate Student Support

Section 7-1. Strategy for meeting support needs

UCLA has already secured many sources of funding needed for supporting both the projected graduate enrollments. Historically, UCLA has been very successful in competing for newly created research and training grants for bioinformatics:

In 2000, UCLA was one of two universities nationwide to be awarded one of the first large-scale training grants in bioinformatics from the National Science Foundation, the Integrated Graduate Education and Research Training (IGERT). This $3 million, five-year grant funded up to 15 Ph.D. students per year.
In 2001, UCLA was awarded a large-scale center grant in computational biology from a new program established by the National Institutes of Health, the National Programs of Excellence in Biomedical Computing.
In 2001, UCLA was awarded a training grant to establish a M.S. degree in Bioinformatics by the Sloan Foundation. This $100,000, three-year grant supported up to five M.S. students per year.
In 2002, UCLA was awarded a large training grant in statistical genetics and genomics by the National Institutes of Health (Genomic Analysis and Interpretation Training Grant, Ken Lange, PI). This five-year grant supports up to 12 Ph.D. students per year.
In 2004, UCLA was awarded a five-year, $20 million grant to establish the Center for Computational Biology, one of the first four National Centers for Biomedical Computing, as part of NIH’s Roadmap Initiative in Bioinformatics and Computational Biology. This grant supports both research and training.
In 2004, UCLA received a five-year, $5 million grant for the UCLA-DOE Laboratory, which officially changed its name to the Institute for Genomics and Proteomics to emphasize genome and proteome analysis using bioinformatics.
In 2005, UCLA was awarded a Biotechnology training grant by NIH (Hal Monbouquette, PI).
In addition to these multi-investigator training grants, the faculty of the IDP pay for graduate student support directly from individual research grants awarded by public agencies and private foundations.

The Bioinformatics IDP’s strategy for funding Ph.D. student support has the following elements:

Admitted Ph.D. students will be offered guaranteed funding for at least one year from the above funding sources.
After the first year of rotations, each Ph.D. student joins the laboratory of an IDP faculty member. From this point on, students usually are supported by a competitive training grant or from the faculty advisor’s research grants.
Bioinformatics graduate training is a high priority area for many agencies that offer individual graduate fellowship awards. We will strongly encourage our top students to apply for outside graduate fellowships, such as the NSF Graduate Research Fellowship; DOE Computational Science Graduate Fellowship; HHMI Predoctoral Fellowship, etc.
UCLA Graduate Division provides funding for graduate support. The Graduate Division provides funds to programs according to the number of enrolled students. Based on current allocation levels, we anticipate an annual allocation of approximately $75,000 when the program reaches steady state.
Regarding available teaching assistants and resources for TAs through enrollment growth, reallocation, or combination, and the effect of any reallocations on support in existing graduate programs: as described above, TA support is not needed as a major element of graduate student support for the proposed IDP. Some IDP students will work as Teaching Assistants in bioinformatics courses and in some affiliated undergraduate courses (Chemistry 160). Currently, these total about four to five TAships per year, but are likely to increase (up to 15 TAships per year) once the IDP begins operation. This will come from increased TA allocations due to increased bioinformatics course enrollments, from development of new undergraduate courses in bioinformatics and computational biology, and from existing course offerings in associated departments. This will have a minimal impact on support in existing graduate programs.

Section 7-2. Current availability of faculty grants to support graduate students and funding trends in agencies expected to provide future research or training grants

The National Institutes of Health (NIH) is the major source of funding for biomedical research in the U.S. NIH has prioritized Bioinformatics as one of the most important areas for new grants for research and education; indeed it was one of the first four fields targeted under the NIH Roadmap Initiative. To carry out this mandate, NIH established the Biomedical Information Science and Technology Initiative (BISTI). Since its inception in 2000, BISTI and other NIH institutes have launched over 35 distinct funding programs in bioinformatics (see www.bisti.nih.gov). The pace of new funding program announcements has accelerated in recent years: 1 in 1998; 2 in 2000; 4 in 2001; 8 in 2002; and 15 in 2003. These bioinformatics funding programs now involve nearly all of the individual Institutes of NIH, demonstrating that the need for bioinformatics is perceived as essential for all areas of biomedical research from basic science, to clinical studies of the genetics of disease, to brain science and mental health.

For example, just one BISTI program, the National Centers for Biomedical Computing (NCBC), calls for the creation of up to 20 centers totaling approximately $400 million in the first five years of funding. UCLA was awarded one of the very first center grants (a five-year grant totaling $20 million). So far, in the first two years of the National Centers program, seven centers have already been established. Following the model of the NCBC program, BISTI has launched a number of new center programs targeting bioinformatics: Centers of Excellence in Genomic Science; Centers of Excellence in Complex Biomedical Systems Research; Core Centers for Advanced Neuroinformatics Research; National Centers for Systems Biology.

Other federal agencies have also identified bioinformatics area as a key area of research, and have established grant programs to support bioinformatics research and training.

NSF has established a large program of grant support for bioinformatics research and bioinformatics resources. For example, through its Biological Databases and Informatics program and associated grants, NSF has made 97 grants totaling $64 million since its inception in 2000. Many other NSF grant programs include a large bioinformatics component. For example, NSF established the Integrated Graduate Education and Research Traineeship program (IGERT) to foster interdisciplinary graduate training, and bioinformatics was a significant focus from the beginning. In fact, UCLA was awarded one of the first three IGERT grants, a five-year, approximately $3 million grant for Ph.D. training in bioinformatics.
DOE
Howard Hughes Medical Institute
Many private foundations offer research and training grants for bioinformatics. For example, the Pharmaceutical Research and Manufacturers of America Foundation (PhRMA) has targeted bioinformatics as one of six areas it is funding through its grants program.

Section 7-3. Other extramural resources likely to provide graduate student support, or internal fellowships and other institutional support made available to the program

Bioinformatics students already receive support from a number of externally funded training grants at UCLA, and students enrolled in the IDP will be eligible for the following grants: the Genomic Analysis and Interpretation Training Grant (K. Lange, PI); the Training Program in Genetic Mechanisms (R. Simons, PI); and Biotechnology Training in Biomedical Sciences and Engineering Program (H. Monbouquette, PI).

Bioinformatics graduate training is a high priority area for many agencies that offer individual graduate fellowship awards (e.g., Howard Hughes Medical Institute, National Science Foundation, Department of Energy). We will strongly encourage our top students to apply for outside graduate fellowships.

Section 7-4. Campus fund-raising initiatives that will contribute to support of graduate students

The Bioinformatics IDP faculty have participated in a number of successful campus fund-raising initiatives, including:

Keck Center for Proteomics: in 2000, the Keck Foundation awarded UCLA a $5 million grant to establish a proteomics laboratory, Bioinformatics User Facility and genomics core facilities.
Howard Hughes Medical Institute: in 2000, HHMI awarded the UCLA School of Medicine a $2.1 million Research Resource Grant for bioinformatics and other research core facilities.
Sun Microsystems: in 2000, Sun contributed approximately $1 million in enterprise computing equipment to the UCLA Bioinformatics program.

Currently, the IDP faculty and Deans of Medicine, Life Sciences, and Physical Sciences are developing a fund-raising initiative in genomics, which may include a component for bioinformatics graduate student support. In addition, the recently created EUREKA endowment for graduate student support has raised approximately $3 million of a planned $40 million endowment fund. Bioinformatics IDP students will be eligible for these funds.

Section 7-5. Graduate student support table listing maximum number of students projected and sources of support for the first six years of the program

Table 5. Graduate Student Support

Year	Students	Source of Support
1	3	Graduate Division NRSA Training Grants
2	8	Graduate Division NRSA Training Grants Teaching Assistantships Extramural Research Grants
3	13	Graduate Division NRSA Training Grants Teaching Assistantships Extramural Research Grants
4	18	Graduate Division NRSA Training Grants Teaching Assistantships Extramural Research Grants
5	23	Graduate Division NRSA Training Grants Teaching Assistantships Extramural Research Grants
6	28	Graduate Division NRSA Training Grants Teaching Assistantships Extramural Research Grants

Section 8. Changes in Senate Regulations

None.

Section 9. Abstract

Bioinformatics is of central importance to biomedical research in the 21^st century, and to the economy of California. We propose to create an Interdepartmental Program (IDP) in Bioinformatics composed of 20 core faculty from 11 departments, so that it is possible for students to apply for and receive M.S. and Ph.D. training in bioinformatics at UCLA. This will strengthen faculty research in four schools and divisions (Life Sciences, Physical Sciences, Medicine and Engineering), help train the next generation of scientists in a field of rapidly growing prominence, attract large new sources of funding to the UCLA campus, aid faculty recruiting and retention, and drive growth at UCLA of this field which has been targeted as a top priority (genetics and bioinformatics) of the campus Five-Year Academic Plan. UCLA has already established a strong record of bioinformatics research and graduate training. The proposed IDP will provide an academic home for bioinformatics at UCLA that brings many different efforts together for the first time. The IDP will leverage many existing resources, including 20 core bioinformatics faculty, existing funding for graduate student support, and existing supercomputing resources.

Section 10. Departmental Commitment to Proposed Program

See Appendix D for departmental letters of support for the proposed IDP.

Appendix A

Summary of Information Required by

the California Postsecondary Education Commission

1. Name of Program: Bioinformatics Interdepartmental Degree Program

2. Campus: UCLA

3. Degree/Certificate: M.S., Ph.D.

(The certificate category referred to includes all organized programs which award certificates for academic achievement. This includes professional certificate programs. Skills programs which are designed specifically for state licensing purposes need not be reported.)

4. CIP Classification (to be completed by Office of the President):

5. Date to be started: Fall Quarter 2007

6. Modification of existing program, identify that program and explain changes. (This means new programs that have roots in existing programs--which may or may not be degree programs.) This is a new program.

7. Purpose (academic or professional training), distinctive features and justification. How does this program differ from others, if any offered in California. The document issued by CPEC titled "Inventory of Academic and Occupational Programs in California Colleges and Universities" is in the Graduate Council Office (extension 51162). The new program must be compared with similar programs listed in the inventory.

UCLA has already established a strong record of bioinformatics research and graduate training. In 1999 the faculty established a graduate core curriculum in bioinformatics, which has been offered continuously since that time, demonstrating the faculty’s commitment to collaborative teaching and to long-term development of an integrated bioinformatics program. These initiatives have been recognized by a large number of awards of multi-investigator Project and Training grants in bioinformatics from NIH, NSF, DOE and other funding sources. These many disparate efforts need a strong graduate program to make them cohesive, successful, and competitive in the long term.

Prospective bioinformatics graduate students do not know which program to apply to at UCLA, since neither departmental graduate programs or ACCESS specifically recruit and train them.
Graduate students at UCLA who are conducting bioinformatics research currently take diverse courses that do not necessarily cover the core training in bioinformatics that we believe they need and that we propose here.
Relative to other UC schools that already have established bioinformatics graduate programs, the lack of such a program at UCLA places UCLA at a disadvantage in competing for the top bioinformatics students, and this impacts the ability of our faculty to obtain funding in this area.

Comparisons with specific programs at other universities are detailed under item 14 below and in the proposal.

8. Type(s) of students to be served. Graduate students seeking integrated training in bioinformatics in an interdisciplinary setting.

9. If program is not in current campus academic plan, give reason for starting program now.

In 1999 the School of Medicine conducted a strategic planning process, which identified bioinformatics as one of the highest priorities for immediate development. One of the recommendations to emerge from this review was for the deans to develop a specific plan for bioinformatics training, which included a degree-granting education program. The fact that this recommendation was made five years ago indicates that the need for the proposed IDP is urgent. It is essential because it would bring together UCLA’s many academic resources in bioinformatics for the first time. The interdisciplinary character of UCLA’s bioinformatics faculty—scattered among 11 departments in four schools and divisions—is both a strength and a potential weakness. As other prominent universities develop bioinformatics graduate degree programs and competition grows, UCLA will be at a severe disadvantage in recruiting top graduate student candidates if it does not offer a bioinformatics degree and admissions program. Failure to establish a graduate program would have negative effects on all of the existing initiatives in bioinformatics and genomics at UCLA. Long-term, it is hard to imagine that the bioinformatics training grant awards that UCLA has received would be renewed if the campus failed even to begin a graduate program. Faculty research in bioinformatics depends critically upon the quality of graduate student recruitment. Lack of a bioinformatics graduate program would become a major disadvantage once other universities establish strong bioinformatics degree programs, which is already happening. This in turn would hurt the quality of bioinformatics research at UCLA, and make it difficult to retain top bioinformatics faculty or recruit new faculty.

10. If program requires approval of a licensure board, what is the status of such approval? Not applicable.

11. Please list distinctive features of the program having the character of credit for experience, internships, lab requirements, etc. Not applicable.

12. List all new courses required:

Department	Course Number	Title	Hours/Week Lecture & Lab
Chemistry	260B	Advanced Algorithms for Bioinformatics	4 lecture, 4 lab
Chemistry	260BL	Bioinformatics Algorithms Laboratory	4 lab

13. List all other required courses:

Department	Course Number	Title	Hours/Week Lecture/ Discussion/Lab	Notes
Chemistry	260A	Bioinformatics and Genomics	3 lecture, 1 discussion	formerly Chemistry 260
Chemistry	202	Bioinformatics Interdisciplinary Research Laboratory	2 lecture, 2 discussion
Stats	254	Statistical Methods in Computational Biology	3 lecture, 1 discussion	to be listed also as Chem M260C in future

14. List UC campus and other California institutions, public or private, which now offer or plan to offer this program or closely related programs. (The current requirement is that these programs be listed. What is of concern is possible duplication. Proposal sponsors should be aware of this and give careful attention to the program justification in #6.)

University	Program Name	Degrees Granted	Notes
Stanford University	Biomedical Informatics	Ph.D.	focused on biomedical applications
UC Berkeley	Computational and Genomic Biology	Designated Emphasis in existing Ph.D. programs	a minor only
UC Riverside	Genetics, Genomics and Bioinformatics	Ph.D.	bioinformatics part of GGB program, not main focus
UC San Francisco	Biological and Medical Informatics	M.S., Ph.D.	based in medical informatics
UC Santa Cruz	Bioinformatics	B.S., M.S., Ph.D.	not interdepartmental; less breadth in core faculty
UC San Diego	Bioinformatics	Ph.D.; also a graduate specialization	closest to proposed program
University of Southern California	Computational Biology and Bioinformatics	Ph.D.	not interdepartmental

15. List any related program offered by the proposing institution and explain relationship.

ACCESS: umbrella graduate admissions program, does not include many bioinformatics faculty, no bioinformatics component in training

Biomathematics: does not involve bioinformatics requirements or research

Biomedical Engineering: does not require bioinformatics, and most bioinformatics faculty are outside of the School of Engineering

Human Genetics: requirements focused on human genetics; most bioinformatics faculty are not in this department

16. Summarize employment prospects for graduates of the proposed program. Give results of job market survey if such have been made. (This is aimed at graduate or undergraduate professional programs.)

Bioinformatics is of central importance to biomedical research in the 21^st century and to the economy of California, and employment prospects are expected to be excellent. To demonstrate the strong market demand for bioinformaticists the proposal lists the job openings posted on the web site of the International Society for Computational Biology for July through September of 2006 -- a total of about 50 listings in both academia and industry.

17. Give estimated new and total enrollment for the first five years and state basis for estimate.

Projected Doctoral Enrollments

Year	New	Continuing	Total
1	3	0	3
2	5	3	8
3	5	8	13
4	5	13	18
5	5	18	23

18. Give estimates of the additional cost of the program by year for five years in each of the following categories:

The development of this degree program utilized resources already available to faculty and

research programs without significant additional costs.

FTE Faculty: New faculty in bioinformatics may be hired by specific departments in future, but this proposal does not require this.

Library Acquisitions: The Library’s current holdings are sufficient, assuming appropriate updates.

Computing: Existing computing facilities will be utilized.

Other Facilities: None.

Equipment: Existing equipment will be utilized.

Provide brief explanation of any of the costs where necessary.

(The additional resources, if any, required by the proposed program must be included even if they can be supplied by campus reallocation.)

No additional costs are anticipated.

19. How and by what agencies will the program be evaluated? (This refers to the campus and professional review procedures.)

Appendix B

Letter of Support from Comparable California Programs

Appendix C

Brief Curricula Vitae of Program Faculty

Appendix D

Letters of Support from UCLA Departments

Appendix E

Letters of Interest from Participating Faculty Members

Appendix F

Catalog Descriptions of all Required and Recommended Courses

(Current as of 1/25/07)

Required Courses:

Chemistry 202. Bioinformatics Interdisciplinary Research Seminar. (4)

Seminar, two hours; discussion, two hours. Concrete examples of how biological questions about genomics data map to and are solved by methodologies from other disciplines, including statistics, computer science, and mathematics. May be repeated for credit. S/U or letter grading.

Chemistry CM260A. Introduction to Bioinformatics and Genomics. (4)

(Formerly numbered CM260.) (Same as Human Genetics M260A.) Lecture, three hours; discussion, one hour. Recommended requisite: Statistics 100A or 110A. Genomics and bioinformatics results and methodologies, with emphasis on concepts behind rapid development of these fields. Focus on how to think genomically via case studies showing how genomics questions map to computational problems and their solutions. Concurrently scheduled with course C160A. S/U or letter grading.

Chemistry C260B. Algorithms in Bioinformatics and Systems. (4)

Lecture, 4 hours; laboratory, 4 hours. Enforced requisite: C160A or C260A with a grade of C- or better. Recommended: Statistics 100A and 110A and PIC 32 and 60. Development and application of computational approaches to biological questions. Understanding of mechanisms for determining statistical significance of computationally derived results. Students will develop a foundation for innovative work in Bioinformatics and Systems Biology. Concurrently scheduled with course C160B. S/U or letter grading.

Chemistry 260BL. Advanced Bioinformatics Computational Laboratory. (2)

Laboratory, 4 hours. Enforced requisite: CM260A. Co-requisite: C260B. This course will focus on the development and application of computational approaches to ask and answer biological questions. Students completing the course should be able to implement a variety of bioinformatics and systems biology algorithms. Correspondingly, they should have an appreciation for the advantages and disadvantages of different algorithmic methods for studying biological questions. Furthermore, students should gain a preliminary understanding of how to compute the statistical significance of their results, a process which may involve writing an estimation or sampling program. The course will focus on development of a conceptual understanding of implementation of bioinformatics algorithms and give students a foundation for how to do innovative work in these fields. Material will be drawn from specific, relevant biological problems and will closely parallel 260B. As a complement to 260, students will gain experience in observing the impact of computational complexity of an algorithm in computing a solution. S/U or letter grading.

Statistics M254. Statistical Methods in Computational Biology. (4)

(Same as Biomathematics M271.) Lecture, three hours; discussion, one hour. Preparation: elementary probability concepts. Requisite: course 100A. Training in probability and statistics for students interested in pursuing research in computational biology, genomics, and bioinformatics. Letter grading. (To be multiple-listed in future as Chemistry 260C.)

Elective Courses:

Biomathematics M203. Stochastic Models in Biology. (4)

(Same as Human Genetics M203.) Lecture, four hours. Requisite: Mathematics 170A or equivalent experience in probability. Mathematical description of biological relationships, with particular attention to areas where conditions for deterministic models are inadequate. Examples of stochastic models from genetics, physiology, ecology, and a variety of other biological and medical disciplines. S/U or letter grading.

Biomathematics M207A. Theoretical Genetic Modeling. (4)

(Same as Biostatistics M272 and Human Genetics M207A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 115A, 131A, Statistics 100B. Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny. S/U or letter grading.

Biomathematics 210. Optimization Methods in Biology. (4)

Lecture, four hours. Preparation: undergraduate mathematical analysis and linear algebra; familiarity with programming language such as Fortran or C. Modern computational biology relies heavily on finite-dimensional optimization. Survey of theory and numerical methods for discrete and continuous optimization, with applications from genetics, medical imaging, pharmacokinetics, and statistics. S/U or letter grading.

Biomathematics M211. Mathematical and Statistical Phylogenetics. (4)

(Same as Human Genetics M211.) Lecture, three hours; laboratory, one hour. Requisites: Biostatistics 110A, 110B, Mathematics 170A. Theoretical models in molecular evolution, with focus on phylogenetic techniques. Topics include evolutionary tree reconstruction methods, studies of viral evolution, phylogeography, and coalescent approaches. Examples from evolutionary biology and medicine. Laboratory for hands-on computer analysis of sequence data. S/U or letter grading.

Biomedical Engineering M217. Biomedical Imaging. (4)

(Same as Electrical Engineering M217.) Lecture, three hours; laboratory, two hours; outside study, seven hours. Requisite: Electrical Engineering 114D or 211A. Mathematical principles of medical imaging modalities: X-ray, computed tomography, positron-emission tomography, single photon emission computed tomography, magnetic resonance imaging. Topics include basic principles of each imaging system, image reconstruction algorithms, system configurations and their effects on reconstruction algorithms, specialized imaging techniques for specific applications such as flow imaging. Letter grading.

Biomedical Physics 208A. Medical Physics Laboratory: Medical Imaging. (4)

Discussion, two hours; laboratory, four hours. Requisite: course 205. Hands-on experience performing acceptance testing and quality control checks of imaging equipment such as fluoroscopy, digital subtraction angiography, mammography, ultrasound, magnetic resonance imaging, computed tomography, and computed radiography.

Biomedical Physics 210. Principles of Medical Image Processing. (4)

Lecture, three hours; discussion, one hour. Requisite: course 209. Study of image representation, computational structures for imaging, linear systems theory, image enhancement and restoration, image compression, segmentation, and morphology. Special topics include visualization techniques, three-dimensional modeling, computer graphics, and neural net applications. Laboratory projects apply concepts developed in class.

Biomedical Physics 214. Medical Image Processing Systems. (4)

Lecture, three hours; discussion, one hour. Requisites: courses 209, 210. Advanced image processing and image analysis techniques applied to medical images. Discussion of approaches to computer-aided diagnosis and image quantitation, as well as application of pattern classification techniques (neural networks and discriminant analysis). Examination of problems from several imaging modalities (CT, MR, CR, and mammography).

Biomedical Physics M266. Advanced Magnetic Resonance Imaging. (4)

(Same as Neuroscience M267 and Psychiatry M266.) Lecture, four hours. Starting with basic principles, presentation of physical basis of magnetic resonance imaging (MRI), with emphasis on developing advanced applications in biomedical imaging, including both structural and functional studies. Instruction more intuitive than mathematical. Letter grading.

Biomedical Physics M285. Functional Neuroimaging: Techniques and Applications. (4)

(Same as Psychiatry M285.) In-depth examination of activation imaging, including PET and MRI methods, data acquisition and analysis, experimental design, and results obtained thus far in human systems. Strong focus on understanding technologies, how to design activation imaging paradigms, and how to interpret results. Laboratory visits and design and implementation of a functional MRI experiment. S/U or letter grading.

Biostatistics M272. Theoretical Genetic Modeling . (4)

(Formerly numbered M237A.) (Same as Biomathematics M207A and Human Genetics M207A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 115A, 131A, Statistics 100B. Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny. S/U or letter grading.

Biostatistics M278. M278. Statistical Analysis of DNA Microarray Data. (4)

(Formerly numbered 278.) (Same as Human Genetics M278.) Lecture, three hours. Requisite: course 200C. Instruction in use of statistical tools used to analyze microarray data. Structure corresponds to analytical protocol an investigator might follow when working with microarray data. S/U or letter grading.

Chemistry 298. Seminar: Computational Methods in Bioinformatics. (To be proposed.) Discussion, one hour. Detailed analysis of new methods, including algorithms and representation of bioinformatics data, and research problems ranging from genome-wide sequence analysis, to fold recognition, to energy calculation and protein design.

Computer Science 249. Current Topics in Data Structures. (2 to 12)

Lecture, four hours; outside study, eight hours. Review of current literature in an area of data structures in which instructor has developed special proficiency as a consequence of research interests. Students report on selected topics. May be repeated for credit with consent of instructor. Letter grading.

Ecology and Evolutionary Biology M231. Molecular Evolution. (4)

(Same as Earth and Space Sciences M217.) Lecture, two hours; discussion, two hours. Series of advanced topics in molecular evolution, with special emphasis on molecular phylogenetics. Topics may include nature of the genome, neutral evolution, molecular clocks, concerted evolution, molecular systematics, statistical tests, and phylogenetic algorithms. Themes may vary from year to year. May be repeated for credit. S/U or letter grading.

Human Genetics C244. Genomic Technology. (4)

Lecture, three hours; discussion, one hour. Requisite: Life Sciences 4. Survey of key technologies that have led to successful application of genomics to biology, with focus on theory behind specific genome-wide technologies and their current applications. Concurrently scheduled with course C144. S/U or letter grading.

MCDB 292. Seminar: Molecular Evolution. (2)

Discussion, three hours. Detailed analysis of current understanding of evolution of molecular sequences and structures.

Neuroscience CM272. Neuroimaging and Brain Mapping. (4)

(Same as Physiological Science M272 and Psychology M213.) Lecture, three hours. Requisites: courses M201, M202. Theory, methods, applications, assumptions, and limitations of neuroimaging. Techniques, biological questions, and results. Brain structure, brain function, and their relationship discussed with regard to imaging. Concurrently scheduled with course C172. Letter grading.

Pathology 255. Mapping and Mining Human Genomes. (3)

Lecture, three hours. Basic molecular genetic and cytogenetic techniques of gene mapping. Selected regions of human genomic map scrutinized in detail, particularly gene families and clusters of genes that have remained linked from mouse to human. Discussion of localizations of disease genes. S/U or letter grading.

Statistics 165. Statistical Methods and Data Mining. (4)

Lecture, three hours. Requisite: course 100A. Introduction and overview of up-to-date statistical methods in microarray analysis designed for students in biostatistics, statistics, and human genetics who are interested in technology and statistical analysis of microarray experiments. Useful for biology students with basic statistical training who are interested in understanding logic underlying many statistical methods. P/NP or letter grading.

Statistics 233. Statistical Methods in Biomedical Imaging. (4)

Lecture, three hours. Requisite: course 100A. Brief review of common general statistical techniques. Advanced statistical methods for analysis of medical imaging, integration, visualization, interrogation, and interpretation of imaging and nonimaging metadata. S/U or letter grading.

Appendix G

Degree Program Requirements

Bioinformatics

Interdepartmental Program

Graduate Degrees

The Bioinformatics Program offers the Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees in Bioinformatics.

Admission

Program Name	Bioinformatics Bioinformatics is an interdepartmental program. Interdepartmental programs provide an integrated curriculum of several disciplines.
Address	xxx Box xxx Los Angeles, CA 90095-xxx
Phone	(310) xxx-xxxx
Email	xxx@ucla.edu
Leading to the degree of	M.S., Ph.D.
Admission Limited to	Fall
Deadline to apply	December 1st
GRE (General and/or Subject), TSE, TWE	GRE: General and Subject in Biology, Biochemistry, Computer Science or Mathematics.
Letters of Recommendation	3, from professors, supervisors, or others who may provide an evaluation of the applicant's accomplishments or potential in research, teaching, and related scholarly activities
Other Requirements	In addition to the University's minimum requirements and those listed above, all applicants are expected to submit a statement of purpose. The department encourages applications from students in all areas of science, but expects successful applicants to have or to acquire a background comparable to the requirements for the bachelor's degree in Computational and Systems Biology at UCLA. A background in chemistry, physics, and mathematics is desirable. Deficiencies in these or other subjects should be made up at the earliest opportunity. Undergraduates who are prospective applicants should remedy their deficiencies by preparatory study at an appropriate institution. Students with academic deficiencies may be admitted on a provisional basis.

Master's Degree

Advising

All academic affairs for graduate students in the department are directed by the Interdepartmental Program Graduate Adviser who is assisted by the administrative staff of the Graduate Affairs Office. The Graduate Adviser establishes, at the time of admission to graduate study, a guidance committee for each student that consists of three faculty members for each student.

The chair of the guidance committee acts as the provisional adviser until a permanent adviser is selected. Service as a provisional adviser is designed to be provisional for both professor and student. It does not commit the professor to supervise the thesis, nor does it commit the student to a provisional adviser. The provisional adviser serves until a permanent adviser is found and the master's examination or thesis committee is established.

Areas of Study

Study consists of coursework and research within the program in a core curriculum, genomics, proteomics, neuroinformatics, computer science, and mathematics and statistics.

Foreign Language Requirement

None.

Course Requirements

The program consists of at least nine courses completed in graduate standing, of which at least five must be graduate (200-series) courses. The remaining courses may be in the 100, 200, or 500 series. No more than two 596 courses (eight units) may be applied toward the nine courses required for the degree; only one 596 course (four units) may be applied toward the minimum five graduate courses required. Courses in the major that are taken for S/U grading may not be applied toward the minimum requirement. Courses outside of the major that are taken for S/U grading may be applied toward the minimum requirement if they are deemed applicable and provided that no more than one such course is taken per quarter. Students must take the core curriculum courses (Chemistry and Biochemistry 260A, Chemistry and Biochemistry 260B, Statistics 254, Chemistry and Biochemistry 260BL, Chemistry and Biochemistry 202, and Chemistry and Biochemistry 252), plus four elective courses from concentration areas, and at least 12 units of research.

Teaching Experience

One quarter of teaching experience is required.

Field Experience

Not required.

Comprehensive Examination Plan

Students who select this plan are required to take an oral examination. The advisory committee evaluates and grades the proposal as not pass or M.A. pass and forwards the results to the Graduate Adviser.

Thesis Plan

A student must choose an IDP faculty advisor and submit a dissertation proposal by the end of the third quarter of study. To proceed, the proposal must be approved by a faculty member who agrees to become the student’s dissertation advisor. Completed dissertations will be evaluated by a committee of at least three IDP faculty members, which must be approved by the IDP Director. The student must present the completed dissertation in a public seminar.

Time-to-Degree

The normative time-to-degree for the master's degree is five to six quarters.

Doctoral Degree

Advising

The Bioinformatics program provides a comprehensive system of advising for students throughout their graduate studies. During orientation the advising committee and program chair meet with new students to review the first-year requirements in general terms. Throughout the term, students are expected to meet individually with the chair or other members of the advising committee to identify faculty whose research is closest to their own interests and who would be most appropriate for laboratory rotations. At the end of the fall term, the entire advising committee meets informally with the first-year students to field questions that have come up after their initial entry into the program. In subsequent quarters, students' enrollment and performance in core courses and laboratory rotations are closely monitored and, as the need arises, students are counseled individually by the advising chair. At the end of Spring Quarter of the first year, students are required to submit a Faculty Mentor Approval Form (co-signed by the mentor) to the advising committee, which meets to consider the choice of mentor and the ability of the faculty to serve in this capacity.

The advising program continues after each student has chosen a faculty research mentor. Every year students receive a memorandum outlining current requirements (for example, course electives, the written and oral qualifying examinations and midstream seminar). The advising committee also meets every year to discuss the progress of all students and identify potential problems. The committee then sends each student a letter that assesses their current progress in the program and makes specific recommendations as needed. An overall assessment of student progress is also made annually to the neuroscience committee. In addition to the formal advising procedures outlined above, students are repeatedly encouraged to seek advice on career development from faculty members in the UCLA Bioinformatics community. Finally, an annual retreat serves the purpose of allowing informal and organized contacts between faculty and students, which provides further opportunity for advising.

Major Fields or Subdisciplines

These fields include genomics, proteomics, neuroinformatics, computer science, and mathematics and statistics.

Foreign Language Requirement

None.

Course Requirements

Students must take the core curriculum courses (Chemistry and Biochemistry 260A, Chemistry and Biochemistry 260B, Statistics 254, Chemistry and Biochemistry 260BL, Chemistry and Biochemistry 202, and Chemistry and Biochemistry 252), plus five elective courses from concentration areas. They must also complete 12 units of laboratory rotation and at least 6 units of seminar courses.

Teaching Experience

One quarter of teaching experience is required.

Written and Oral Qualifying Examinations

Academic Senate regulations require all doctoral students to complete and pass University written and oral qualifying examinations prior to doctoral advancement to candidacy. Also, under Senate regulations the University oral qualifying examination is open only to the student and appointed members of the doctoral committee. In addition to University requirements, some graduate programs have other pre-candidacy examination requirements. What follows in this section is how students are required to fulfill all of these requirements for this doctoral program.

During their first year, Ph.D. students will perform laboratory rotations with IDP faculty whose research interests them, and must select a dissertation advisor from the IDP faculty by the end of their third quarter. Next, students must select a qualifying committee by the end of their second Spring quarter, and submit a written dissertation proposal by the end of their second year. The qualifying committee must consist of the faculty advisor and at least three additional faculty, of whom two must be IDP faculty. The qualifying committee must be approved by the IDP Director and the campus graduate Dean. The written dissertation proposal must be given to all the qualifying committee members prior to the scheduled oral qualifying exam.

Advancement to Candidacy

Students will advance to candidacy when they pass the oral qualifying exam, complete the IDP course requirements, clear all incompletes from their transcript, and pay the filing fee. Students should advance to candidacy by the end of their third Fall quarter. Failure to advance to candidacy by the end of the fourth year will result in academic probation.

Doctoral Dissertation

Every doctoral degree program requires the completion of an approved dissertation that demonstrates the student's ability to perform original, independent research and constitutes a distinct contribution to knowledge in the principal field of study.

Final Oral Examination (Defense of Dissertation)

Required for all students in the program.

Time-to-Degree

In general, overall progress toward the degree is accomplished with completion of the written qualifying examination by the beginning of the second year. It is recommended that students complete the University Oral Qualifying Examination by the end of Spring Quarter of the third year. The approved normative time-to-degree is 16 quarters.

Termination of Graduate Study and Appeal of Termination

University Policy

A student who fails to meet the above requirements may be recommended for termination of graduate study. A graduate student may be disqualified from continuing in the graduate program for a variety of reasons. The most common is failure to maintain the minimum cumulative grade point average (3.00) required by the Academic Senate to remain in good standing (some programs require a higher grade point average). Other examples include failure of examinations, lack of timely progress toward the degree and poor performance in core courses. Probationary students (those with cumulative grade point averages below 3.00) are subject to immediate dismissal upon the recommendation of their department. University guidelines governing termination of graduate students, including the appeal procedure, are outlined in Standards and Procedures for Graduate Study at UCLA.

Special Departmental or Program Policy

A student must receive at least a B- in each core course or repeat the course. A student who receives three B- grades in the core courses, who fails all or part of the written or oral qualifying examinations two times (if the student fails all or part of the written qualifying examination the examination committee determines the form of reexamination), or who fails to maintain minimum progress may be recommended for termination by vote of the entire interdepartmental degree committee. A student may appeal a recommendation for termination in writing to the interdepartmental degree committee and may personally present additional or mitigating information to the committee, in person or in writing.

Appendix I

Existing Bioinformatics Equipment Resources