Bioinformatics Concentration


This Concentration is designed for students interested in computational discovery and management of biological data, primarily genomic, proteomic or metabolomic data. Bioinformatics concentration studies emphasize computational, statistical and other mathematical approaches for depicting (modeling) and analyzing high-throughput biological data, and the inherent structure of biological information. Example research problems include finding statistical patterns that reveal genomic or evolutionary or developmental information, or how regulatory sequences give rise to “programs” of gene expression, or how the genome encodes the capabilities of the human mind.

 

 

APPROVED LIST - CONCENTRATIONS FOR THE ACADEMIC YEAR 2016-2017

Recommended courses are shown with *. Tentative schedule in parentheses.

Consult Curricular & Courses at www.registrar.ucla.edu/catalog/ for detailed course descriptions.


I. The following 2 premajor Program in Computing (PIC) courses (10 units) (in addition to PC 10A or CS 32 (4 units) required in the premajor):

PIC 10B Intermediate Programming  
PIC 10C Advanced Programming  
  OR  
CS32 Introduction to Computer Science II  

II. Required CORE Courses

1. MCDB M140: Cell Biology: Cell Cycle ( 5 units) (Spring)

OR

MCDB 144: Molecular Biology (5 units) (Fall & Spring)

2. Phy Sci 125: Molecular Systems Biology (4 units) (Spring)

OR

MCDB 172: Genomics and Bioinformatics (5 units) (Winter)

3. Chem C160A/CM260A: Introduction to Bioinformatics and Genomics (4 units) (Fall)

4. Comp Sci CM124: Computational Genetics (4 units) (Spring)

III. One additional course selected from the following subarea groups, chosen in consulatation with a BI mentor, justified as coherent in the proposal submitted when applying to the Major, and approved by the Program Chair.

(A) BI Methodology

Phys Sci 125 Molecular Systems Biology (5)  
MCDB 172 Genomics and Bioinformatics (5)  
Chem C160B/C260B Algorithms in Bioinformatics and Systems Biology (4)  
Stats M254/Biomath M271 Statistical Methods in Computational Bioligy (4)  
Human Genetics C144 Genomic Technology (4)  
Human Genetics M207A Theoretical Genetic Modeling (4) (same as Biomath M207A, Biostats M272; really rigorous complement to BI, for the strong at heart only)  
Human Genetics M207B Applied Genetic Modeling (4) (same as Biomath M207B, Biostats M237); more applied, less rigorous, still substantive. Requires Biostats 110A, 110B)  
Biomath M211 Statistical and Mathematical Phylogenetics (4)  

(B) BI Computer Science

Math 157 Software Techniques for Scientific Computing (4)  
PIC 60 Data Structure and Algorithms (4)  
PC 110 Parallel and Distinguished Computing (5)  
CS 130 Software Engineering (4)  
CS 143 Introduction to Data Base Systems (4)  
CS 170A Mathematical Modeling & Methods for Computer Science (4)  
CS 171L Data Communications Lab (2-4)  
CS 180 Introduction to Algorithms and Complexity (4)  
CS 181 Introduction to Formal Languages & Automata Theory (4)  
CS CM186/CM286 Biomedical Systems/Biocybernetics Research Lab (4)  
CS M296A Modeling Methodology in Biological Systems (4)  

(C) BI Molecular & Cellular Biochemistry

MCDB 156 Human Genetics (4)  
MIMG 101 Introductory Microbiology (4)  
MIMG 106 Molecular and Genetic Basis of Bacterial Infections (4)  
MIMG 168 Molecular Parasitology (good biological systems analysis course) (4)  
MIMG 185A Immunology (5)  
Chemistry 110A Physical Chemistry: Chemical Thermodynamics (4)  
Chemistry 110B Physical Chemistry: Intro to Statistical Mechanics & Kinetics (4)  
Chemistry 125 Computers in Chemistry (4)  
Chemistry 153A Biochemistry: Intro to Structure, Enzymes & Metabolism (4)  
Chemistry 153B Biochemistry: DNA, RNA and Protein Synthesis (4)  
Chemistry 153G Macromolecular Structure (4)  
Chemistry 156 Physical Biochemistry (4)  
Biological Chemistry CM169 Cell Structure, Signaling and Differentiation (Same as Human Genetics and MCDB CM169)