Courses

1. Molecular Biology Fundamentals

Trainees are required to take one of the following courses.

• Chemistry and Biochemistry
  • 153A. Biochemistry: Introduction to Structure, Enzymes, and Metabolism. Units: 4. Lecture, four hours; discussion, one hour. Structure of proteins, carbohydrates, and lipids; enzyme catalysis and principles of metabolism, including glycolysis, citric acid cycle, and oxidative phosphorylation.
  • 153B. Biochemistry: DNA, RNA, and Protein Synthesis. Units: 4. Lecture, three hours; discussion, one hour; tutorial, one hour. Nucleotide metabolism; DNA replication; DNA repair; transcription machinery; regulation of transcription; RNA structure and processing; protein synthesis and processing.
• Ecology and Evolutionary Biology
  • 121. Molecular Evolution. Units: 4. Lecture, three hours; discussion, one hour. Molecular biology, with emphasis on evolutionary aspects. DNA replication, RNA transcription, protein synthesis, gene expression, and molecular evolution.
• Microbiology, Immunology, and Molecular Genetics
  • 101. Introductory Microbiology. Units: 4. Lecture, three hours; discussion, one hour. Historical foundations of microbiology; introduction to bacterial structure, physiology, biochemistry, genetics, and ecology.
  • 102. Introductory Virology. Units: 4. Lecture, three hours; discussion, one hour. Requisites: Life Sciences 3, or 7A, 7B, and 23L with grades of C- or better. Biological properties of bacterial and animal viruses, replication, methods of detection, interactions with host cells and multicellular hosts.
• Molecular, Cell, and Developmental Biology
  • 144. Molecular Biology. Units: 5. Lecture, three hours; discussion, one hour. Development of sophisticated understanding of DNA, RNA, and protein as well as capability of designing experiments to address fundamental questions in biology and interpreting experimental data.

2. Probability and Statistics Fundamentals

Trainees are required to take one of the following two-quarter sequences.

• Statistics
  • 100A. Introduction to Probability Theory. Units: 4. Lecture, three hours; discussion, one hour. Probability distributions, random variables, vectors, and expectation.
  • 100B. Introduction to Mathematical Statistics. Units: 4. Lecture, three hours; discussion, one hour. Survey sampling, estimation, testing, data summary, one- and two-sample problems.

The above “B” quarter can be substituted with:

• 236. Introduction to Bayesian Statistics. Units: 4. Lecture, three hours; discussion, one hour. Introduction to statistical inference based on use of Bayes theorem, covering foundational aspects, current applications, and computational issues. Topics include Stein paradox, nonparametric Bayes, and statistical learning.

• Biostatistics
  • 100A. Introduction to Biostatistics. Units: 4. Lecture, three hours; discussion, one hour; laboratory, one hour. Introduction to methods and concepts of statistical analysis. Sampling situations, with special attention to those occurring in biological sciences. Topics include distributions, tests of hypotheses, estimation, types of error, significance and confidence levels, sample size.
  • 100B. Introduction to Biostatistics. Units: 4. Lecture, three hours; discussion, one hour; laboratory, one hour. Introduction to analysis of variance, linear regression, and correlation analysis.
  • 216. Mathematical Methods for Biostatistics. Units: 2. Lecture, two hours. Requisites: Mathematics 31A, 31B, 33A. Designed for incoming first-year MS and PhD students. Review, and in some cases introduction, of specialized topics in linear algebra, multivariable calculus, and scientific computing. Interplay between mathematical methods and scientific computing within R statistical computing environment. Detailed training on numerical algorithms used in linear algebra and probabilistic simulations commonly used by statisticians.

  • 203B. Introduction to Data Science. Units: 4. Lecture, three hours; laboratory, two hours. Requisite: course 203A. Principles of data science. Topics include Health Insurance Portability and Accountability Act (HIPAA) and data ethics, databases and data retrieval, data merging and cleaning, data visualization and web presentation, reproducible research, collaborative research, cluster computing, and cloud computing.

  • 257. Computational Methods for Biostatistical Research. Units: 4. Lecture, three hours; discussion, one hour. Requisites: course 250A or Statistics 100C, Mathematics 115A. Preparation for quantitative research in statistics and data sciences. Numerical analysis and hands-on computing techniques for handling big data. Numerical analysis topics include computer arithmetic, solving linear equations, Cholesky factorization, QR factorization, regression computations, eigenvalue problems, iterative solvers, numerical optimization, and design and analysis of statistical simulation experiments. Computing techniques include basics of R programming, reproducible research using R and RStudio, collaborative research, parallel computing, and cloud computing. No prior knowledge of R assumed.

3. Quantitative Genomics Courses

The following course is required.

• Human Genetics
  • 236A. Advanced Human Genetics A: Molecular Aspects. Units: 4. Lecture, three hours. Advanced topics in human genetics related to molecular genetics and relevant technologies. Topics include genomic technologies, human genome, mapping and identification of disease-causing mutations, transcriptomics, proteomics, functional genomics, epigenetics, and stem cells.

Trainees are required to take two of the following courses.

• Human Genetics
• • 224. Computational Genetics. Units: 4. Lecture, four hours; discussion, two hours; outside study, six hours. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics.
  • 244. Genomic Technology. Units: 4. Lecture, three hours; discussion, one hour. 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.
  • 265. Computational Methods in Genomics. Units: 4. Lecture, two and one half hours; discussion, two and one half hours; outside study, seven hours. Introduction to computational approaches in bioinformatics, genomics, and computational genetics and preparation for computational interdisciplinary research in genetics and genomics. Computational techniques and methods include those from statistics and computer science.
• Bioinformatics
  • 260A. Introduction to Bioinformatics. Units: 4. Lecture, four hours; discussion, two hours. Introduction to bioinformatics and methodologies, with emphasis on concepts and inventing new computational and statistical techniques to analyze biological data. Focus on sequence analysis and alignment algorithms.

• Biomathematics
  • 207A. Theoretical Genetic Modeling. Units: 4. Lecture, three hours; discussion, one hour. Mathematical models in statistical genetics. Topics include population genetics, genetic epidemiology, gene mapping, design of genetics experiments, DNA sequence analysis, and molecular phylogeny.
  • 207B. Applied Genetic Modeling. Units: 4. Lecture, three hours; laboratory, one hour. Covers basic genetic concepts. Topics include statistical methodology underlying genetic analysis of both quantitative and qualitative complex traits. Laboratory for hands-on computer analysis of genetic data; laboratory reports required.
  • 211. Mathematical and Statistical Phylogenetics. Units: 4. Lecture, three hours; laboratory, one hour. 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.
• Ecology and Evolutionary Biology
  • 200A. Units: 4. Lecture, two hours; discussion, two hours. Current concepts and topics in evolutionary biology, including microevolution, speciation and species concepts, analytical biogeography, adaptive radiation, mass extinction, community evolution, molecular evolution, and development of evolutionary thought.
  • 235. Population Genetics. Units: 4. Lecture, three hours; discussion, one hour. Basic principles of genetics of population, dealing with genetic structure of natural populations and mechanisms of evolution. Equilibrium conditions and forces altering gene frequencies, polygenic inheritance, molecular evolution, and methods of quantitative genetics.
• Microbiology, Immunology, and Molecular Genetics
  • 256. Human Genetics and Genomics. Units: 5. (Same as Molecular, Cell, and Developmental Biology CM256.) Lecture, three hours; discussion, one hour. Requisites: Life Sciences 3, 4, and 23L, or 7A, 7B, and 7C. Application of genetic principles in human populations, with emphasis on genomics, family studies, positional cloning, Mendelian and common diseases, cancer genetics, animal models, cytogenetics, pharmacogenetics, population genetics, and genetic counseling. Lectures and readings in literature, with focus on current questions in fields of medical and human genetics and methodologies appropriate to answer such questions. Concurrently scheduled with course CM156. Independent research project required of graduate students.
• Statistics
  • 254. Statistical Methods in Computational Biology. Units: 4. Lecture, three hours; discussion, one hour. Introduction to statistical methods developed and widely applied in several branches of computational biology, such as gene expression, sequence alignment, motif discovery, comparative genomics, and biological networks, with emphasis on understanding of basic statistical concepts and use of statistical inference to solve biological problems.

4. Career Development Course

Trainees are required to the following course is required to be taken each year they are on the grant.

• Human Genetics
  • Human Genetics 282. Topics on Scientific Careers. Units: 2. Lecture, two hours. Covers topics related to scientific careers such as scientific writing and presentation (including to non-scientific audiences), grant writing and reviewing, curricula vitae, hiring process, social media usage, developing short- and long-term goals, balancing career and non-work life, and social and ethical issues in biomedical research. Exploration of differences between industry, government, teaching-college, and research-college careers. S/U grading.

5. Ethics Course

Trainees are required to take Microbiology, Immunology, and Molecular Genetics 234 or Biomathematics M261 by the end of their first year as a trainee.

• Microbiology, Immunology, and Molecular Genetics
  • 234. Ethics and Accountability in Biomedical Research. Units: 2. Seminar, two hours. Responsibilities and ethical conduct of investigators in research, data management, mentorship, grant applications, and publications. Responsibilities to peers, sponsoring institutions, and society. Conflicts of interest, disclosure, animal subject welfare, human subject protection, and areas in which investigational goals and certain societal values may conflict. S/U grading.
• Biomathematics
  • M261. Responsible Conduct of Research Involving Humans. Units: 2. Lecture, two hours; discussion, two hours. Discussion of current issues in responsible conduct of clinical research, including reporting of research, basis for authorship, issues in genetic research, principles and practice of research on humans, conflicts of interest, Institutional Review Board (IRB), and related topics. S/U or letter grading