There are many graduate course offerings related to Bioinformatics at UCLA. The courses are listed below. The recommended starting point for any students interested in Bioinformatics is to take some of the Bioinformatics Core courses.

Students in the Bioinformatics Ph.D. program are recommended to take the following courses in their first year:

- Core Courses: CM260A (Fall), CM260B (Winter), CM265 (Winter), CM224 (Spring), CM271 (Spring)
- Seminar Courses: CM202 (Fall), CM252 (Fall, Winter, Spring for 2 years)

In addition, Bioinformatics Ph.D. students will take C234 Ethics and Accountability in Biomedical Research in Spring.

**Core Bioinformatics Courses:**

These are the Bioinformatics Core curriculum courses. They are the most important and relevant courses in Bioinformatics at UCLA. Students in the Bioinformatics Ph.D. program are required to take any 3 of the 5 courses. However, Bioinformatics Ph.D. students are strongly encouraged to take all 5 of the courses during their first year. Students can take the other 2 courses as electives. For students outside the Bioinformatics Ph.D. program, these courses are recommended as a starting point for gaining a background in Bioinformatics. The core courses assume some knowledge of programming and a background in statistics equivalent to an upper division course.

**BIOINFO M260A. Introduction to Bioinformatics**

Units: 4

(Same as **Chemistry CM260A**, **Computer Science CM221**, and **Human Genetics M260A**.) Lecture, four hours; discussion, two hours. Enforced requisites: Computer Science 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Prior knowledge of biology not required. Designed for engineering students as well as students from biological sciences and medical school. 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. S/U or letter grading.

**BIOINFO M260B. Algorithms in Bioinformatics and Systems Biology**

Units: 4

(Same as **Chemistry CM260B** and **Computer Science CM222**.) Lecture, four hours; discussion, two hours. Enforced requisites: Computer Science 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Course M260A is not requisite to M260B. Designed for engineering students as well as students from biological sciences and medical school. Development and application of computational approaches to biological questions, with focus on formulating interdisciplinary problems as computational problems and then solving these problems using algorithmic techniques. Computational techniques include those from statistics and computer science. Letter grading.

**BIOINFO M224. Computational Genetics**

Units: 4

(Same as **Computer Science CM224** and **Human Genetics CM224**.) Lecture, four hours; discussion, two hours; outside study, six hours. Enforced requisites: Computer Science 32 or Program in Computing 10C with grade of C- or better, and one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Designed for engineering students as well as students from biological sciences and medical school. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information, and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving those problems using computational techniques from statistics and computer science. Letter grading.

**BIOINFO M271. Statistical Methods in Computational Biology**

Units: 4

(Same as **Biomathematics M271** and **Statistics M254**.) Lecture, three hours; discussion, one hour. Preparation: elementary probability concepts. Requisite: course M260A or Statistics 100A or 200A. 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. Letter grading.

**BIOINFO M265. Computational Methods in Genomics**

Units: 4

(Same as **Computer Science M225** and **Human Genetics M265**.) Lecture, two and one half hours; discussion, two and one half hours; outside study, seven hours. Limited to bioinformatics, computer science, human genetics, and molecular biology graduate students. Introduction to computational approaches in bioinformatics, genomics, and computational genetics and preparation for computational interdisciplinary research in genetics and genomics. Topics include genome analysis, regulatory genomics, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genomic technologies. Computational techniques and methods include those from statistics and computer science. Letter grading.

**Bioinformatics Seminar Courses:**

There are two seminars in Bioinformatics at UCLA. The main seminar meets on Mondays at 4:00pm, usually in Boyer 159. The seminar information is available on the Bioinformatics website. Bioinformatics Ph.D. students are required to take the seminar course M252, which includes both the seminar as well as a meeting the week prior to discuss papers of the speaker in order to obtain background material about the seminar topics. The second seminar is the M202 seminar offered in Fall Quarter each year. This seminar is required of first year Bioinformatics Ph.D. students and provides an overview of research in Bioinformatics at UCLA. Two different UCLA Bioinformatics faculty give short seminars on their research. This seminar is available to students outside the Bioinformatics Ph.D. program by petition.

**BIOINFO M202. Bioinformatics Interdisciplinary Research Seminar**

Units: 4

(Same as **Chemistry M202**.) 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.

**BIOINFO M252. Seminar: Advanced Methods in Computational Biology**

Units: 2

(Same as **Chemistry M252** and **Human Genetics M252**.) Seminar, one hour; discussion, one hour. Designed for advanced graduate students. Examination of computational methodology in bioinformatics and computational biology through presentation of current research literature. How to select and apply methods from computational and mathematical disciplines to problems in bioinformatics and computational biology; development of novel methodologies. S/U or letter grading.

**Other Bioinformatics Courses:**

These courses are additional courses in Bioinformatics available at UCLA. For Bioinformatics Ph.D. students, these are excellent choices for electives in addition to taking the additional 2 core courses. Each term, the “Topics in Bioinformatics” course covers a distinct topic in Bioinformatics. Students can take multiple offerings of the course for elective credit since each offering covers different topics.

**BIOINFO M226. Machine Learning in Bioinformatics**

Units: 4

(Same as **Computer Science M226** and **Human Genetics M226**.) Lecture, four hours; outside study, eight hours. Enforced requisite: Computer Science 32 or Program in Computing 10C with grade of C- or better. Recommended: one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, or Statistics 100A. Familiarity with probability, statistics, linear algebra, and algorithms expected. Designed for engineering students as well as students from biological sciences and medical school. Biology has become data-intensive science. Bottleneck in being able to make sense of biological processes has shifted from data generation to statistical models and inference algorithms that can analyze these datasets. Statistical machine learning provides important toolkit in this endeavor. Biological datasets offer new challenges to field of machine learning. Examination of statistical and computational aspects of machine learning techniques and their application to key biological questions. Letter grading.

**COM SCI M229S. Seminar: Current Topics in Bioinformatics**

Units: 4

(Same as **Biological Chemistry M229S** and **Human Genetics M229S**.) Seminar, four hours; outside study, eight hours. Designed for graduate engineering students as well as students from biological sciences and medical school. Introduction to current topics in bioinformatics, genomics, and computational genetics and preparation for computational interdisciplinary research in genetics and genomics. Topics include genome analysis, regulatory genomics, association analysis, association study design, isolated and admixed populations, population substructure, human structural variation, model organisms, and genomic technologies. Computational techniques include those from statistics and computer science. May be repeated for credit with topic change. Letter grading.

**Background Courses in Genomics and Computational Biology:**

These courses are excellent courses in Genomics or Computational Biology. They all satisfy electives in the Bioinformatics Ph.D. program.

**BIOMATH 201. Deterministic Models in Biology**

Units: 4

Lecture, three hours; laboratory, three hours. Preparation: knowledge of linear algebra and differential equations. Examination of conditions under which deterministic approaches can be employed and conditions where they may be expected to fail. Topics include compartmental analysis, enzyme kinetics, physiological control systems, and cellular/animal population models. S/U or letter grading.

**BIOMATH M203. Stochastic Models in Biology**

Units: 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 variety of other biological and medical disciplines. S/U or letter grading.

**BIOMATH M207A. Theoretical Genetic Modeling**

Units: 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.

**BIOMATH M207B. Applied Genetic Modeling**

Units: 4

(Same as **Biostatistics M237** and **Human Genetics M207B**.) Lecture, three hours; laboratory, one hour. Requisites: Biostatistics 110A, 110B. Methods of computer-oriented human genetic analysis. 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. Course complements M207A; students may take either and are encouraged to take both. S/U or letter grading.

**BIOMATH M211. Mathematical and Statistical Phylogenetics**

Units: 4

(Same as **Biostatistics M239** and **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.

**BIOL CH 251A. Seminar: Transcriptional Regulation**

Units: 2

Seminar, two hours. Advanced course on mechanics of gene transcription in both eukaryotes and prokaryotes intended for students actively working or highly interested in transcription. S/U grading.

**BIOL CH 251B. Seminar: Transcriptional Regulation**

Units: 2

Seminar, two hours. Advanced course on mechanics of gene transcription in both eukaryotes and prokaryotes intended for students actively working or highly interested in transcription. S/U grading.

**CHEM C200. Genomics and Computational Biology**

Units: 5

Lecture, four hours; discussion, one hour. Introduction for biochemistry students of technologies and experimental data of genomics, as well as computational tools for analyzing them. Biochemistry and molecular biology dissected life into its component parts, one gene at time, but lacked integrative mechanisms for putting this information back together to predict what happens in complete organism (e.g., over 80 percent of drug candidates fail in clinical trials). High-throughput technologies such as sequencing, microarrays, mass-spec, and robotics have given biologists incredible new capabilities to analyze complete genomes, expression patterns, functions, and interactions across whole organisms, populations, and species. Use and analysis of such datasets becomes essential daily activity for biomedical scientists. Core principles and methodologies for analyzing genomics data to answer biological and medical questions, with focus on concepts that guide data analysis rather than algorithm details. Concurrently scheduled with course C100. S/U or letter grading.

**CHEM 256N. Seminar: Research in Biochemistry — Advanced Topics in Structural Biology
**Units: 2

Seminar, three hours. Advanced study and analysis of current topics in biochemistry. Discussion of current research and literature in research specialty of faculty member teaching course. S/U grading.

**CHEM C265. Metabolic Control by Protein Modification**

Units: 4

Lecture, three hours; discussion, one hour. Requisites: courses 153A, 153B, 153C. Biochemical basis of controlling metabolic pathways by posttranslational modification of proteins, including phosphorylation and methylation reactions. Concurrently scheduled with course C165. Letter grading.

**CHEM 266. Proteomics and Protein Mass Spectrometry**

Units: 3

Lecture, two hours. Essential technologies and concepts practiced in proteomics-based research, including methods for protein separation and display, protein quantitation, and protein identification. Emphasis on fundamentals of protein mass spectrometry. S/U or letter grading.

**EE BIOL M200A. Evolutionary Biology**

Units: 4

(Same as **Earth, Planetary, and Space Sciences M216**.) 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. S/U or letter grading.

**EE BIOL C235. Population Genetics**

Units: 4

(Formerly numbered 235.) 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. Concurrently scheduled with course C135. S/U or letter grading.

**EE BIOL 263. Seminar: Population Genetics**

Units: 2 or 4

Seminar, three to six hours. Seminar on topics of current interest in population genetics, such as kin selection, sociobiology, cultural evolution, conservation genetics, etc. S/U or letter grading.

**EE BIOL 297. Selected Topics in Ecology and Evolutionary Biology**

Units: 1 to 4

Seminar, one to three hours. Advanced study and analysis of variable research topics in research issues in ecology and evolutionary biology. Consult “Schedule of Classes” for topics and instructors. May be repeated for credit with consent of instructor. S/U or letter grading.

**HUM GEN 210. Topics in Genomics**

Units: 2

Seminar, two hours. Survey of current biological theory and technology used in genomic research. Topics include genomic technologies, functional genomics, proteomics, statistical genetics, bioinformatics, and ethical issues in human genetics. S/U grading.

**HUM GEN 236A. Advanced Human Genetics A: Molecular Aspects**

Units: 4

Lecture, three hours. Recommended preparation: prior knowledge of basic concepts in molecular biology and genetics. 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. Reading materials include original research articles and reviews or book chapters. Letter grading.

**HUM GEN 236B. Advanced Human Genetics B: Statistical Aspects**

Units: 4

Lecture, three hours; computer laboratory, one hour. Recommended preparation: introductory statistics knowledge equivalent to Biostatistics 100A or Statistics 13 and general genetics knowledge equivalent to Ecology and Evolutionary Biology 121, Human Genetics 236A, or Molecular, Cell, and Developmental Biology 144. Statistical and population genetics related to analysis of complex human genetic traits. Reading materials include original research papers and reviews. Letter grading.

**HUM GEN C244. Genomic Technology**

Units: 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.

**HUM GEN M255. Mapping and Mining Human Genome**

Units: 3

(Same as **Pathology M255**.) 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.

**Background Courses in Biology, Statistics, or Computation:**

These courses are courses recommended to provide a background in computation, statistics or biology. They all satisfy electives in the Bioinformatics Ph.D. program.

**BIOSTAT 202A. Theoretical Principles of Biostatistics**

Units: 4

Lecture, three hours; discussion, one hour. Recommended preparation: two years of calculus and linear algebra. Introduction to main principles of probability, random variables, discrete and continuous distributions, bivariate distributions, and distributions of functions of random variables. Letter grading.

**BIOSTAT 202B. Topics in Estimation**

Units: 4

Lecture, three hours; discussion, one hour. Requisite: course 202A. Basic concepts, sufficiency, biasedness, approximation methods in statistics, nonparametric models and estimation methods, maximum likelihood estimation, M-estimation, Bayesian estimation, and hypotheses testing. Letter grading.

**BIOSTAT 276. Inferential Techniques that Use Simulation**

Units: 4

Lecture, three hours; discussion, one hour. Requisites: Statistics 200A, 200B. Recommended: course 213. Theory and application of recently developed techniques for statistical inference that use computer simulation. Topics include bootstrap, multiple imputation, data augmentation, stochastic relaxation, and sampling/importance resampling algorithm. S/U or letter grading.

**COM SCI 262A. Learning and Reasoning with Bayesian Networks**

Units: 4

Lecture, four hours; outside study, eight hours. Requisite: course 112 or Electrical Engineering 131A. Review of several formalisms for representing and managing uncertainty in reasoning systems; presentation of comprehensive description of Bayesian inference using belief networks representation. Letter grading.

**EL ENGR 236A. Linear Programming**

Units: 4

Lecture, four hours; discussion, one hour; outside study, seven hours. Requisite: Mathematics 115A or equivalent knowledge of linear algebra. Basic graduate course in linear optimization. Geometry of linear programming. Duality. Simplex method. Interior-point methods. Decomposition and large-scale linear programming. Quadratic programming and complementary pivot theory. Engineering applications. Introduction to integer linear programming and computational complexity theory. Letter grading.

**EL ENGR 236B. Convex Optimization**

Units: 4

Lecture, four hours; discussion, one hour; outside study, seven hours. Requisite: course 236A. Introduction to convex optimization and its applications. Convex sets, functions, and basics of convex analysis. Convex optimization problems (linear and quadratic programming, second-order cone and semidefinite programming, geometric programming). Lagrange duality and optimality conditions. Applications of convex optimization. Unconstrained minimization methods. Interior-point and cutting-plane algorithms. Introduction to nonlinear programming. Letter grading.

**EL ENGR 236C. Optimization Methods for Large-Scale Systems**

Units: 4

Lecture, four hours; outside study, eight hours. Requisite: course 236B. First-order algorithms for convex optimization: subgradient method, conjugate gradient method, proximal gradient and accelerated proximal gradient methods, block coordinate descent. Decomposition of large-scale optimization problems. Augmented Lagrangian method and alternating direction method of multipliers. Monotone operators and operator-splitting algorithms. Second-order algorithms: inexact Newton methods, interior-point algorithms for conic optimization. Letter grading.

**M PHARMA 287. Business of Science**

Units: 2

Lecture, two hours. Designed for graduate students. (undergraduate students may enroll with consent of instructor). Introduction to principles of business and entrepreneurship in technology sectors. Basic business skills taught to effectively perform in commercial environment and within academic environment. Application of course material by performing feasibility studies that have potential to receive funding and become actual companies. Exploration of entrepreneurship, particularly formation and operation of new business ventures. Presentations by and questioning of successful technology entrepreneurs, identifying and evaluating new venture opportunities, development of financing, and entry and exit strategies. S/U or letter grading.

**MIMG C222. Mouse Molecular Genetics**

Units: 2

(Formerly numbered CM222.) Seminar, two hours. Enforced requisite: Life Sciences 4. Designed for students doing research with mice. During past 25 years, molecular revolution has greatly increased power and scope of mouse genetics, and today mouse is primary experimental model in virtually all fields of biology and biomedicine. Seminar forum for in-depth discussion of tools and technologies of mouse genetics and their application to functional genomics, complex traits, stem cell biology, developmental biology, epigenetics, and genetic dissection of diseases. Concurrently scheduled with course C122. S/U or letter grading.

**PATH M272. Stem Cell Biology and Regenerative Medicine**

Units: 4

(Same as **Molecular, Cell, and Developmental Biology M272**.) Lecture, two hours; discussion, two hours. Designed for graduate students. Presentation of current knowledge of embryonic and adult stem cells and factors that regulate their growth and development. Major emphasis on how advances in cell and molecular biology and tissue engineering can be applied to use of stem cells in regenerative medicine. Bioethical and legal issues related to stem cell research. S/U or letter grading.

**PHYSCI M200. Advanced Experimental Statistics**

Units: 4

(Formerly numbered M200.) Lecture, four hours; laboratory, one hour. Introduction to statistics with focus on computer simulation instead of formulas. Bootstrap and Monte Carlo methods used to analyze physiological data. S/U or letter grading.

**STATS 200A. Applied Probability**

Units: 4

Lecture, three hours. Requisite: course 100A or Mathematics 170A. Limited to graduate statistics students. Simulation, renewal theory, martingale, and selected topics from queuing, reliability, speech recognition, computational biology, mathematical finance, epidemiology. S/U or letter grading.

**STATS 200B. Theoretical Statistics**

Units: 4

Lecture, three hours. Sufficiency, exponential families, least squares, maximum likelihood estimation, Bayesian estimation, Fisher information, Cramér/Rao inequality, Stein’s estimate, empirical Bayes, shrinkage and penalty, confidence intervals. Likelihood ratio test, p-value, false discovery, nonparametrics, semi-parametrics, model selection, dimension reduction. S/U or letter grading.

**STATS M231. Pattern Recognition and Machine Learning**

Units: 4

(Same as **Computer Science M276A**.) Lecture, three hours. Designed for graduate students. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Topics include Bayesian decision theory, parametric and nonparametric learning, clustering, complexity (VC-dimension, MDL, AIC), PCA/ICA/TCA, MDS, SVM, boosting. S/U or letter grading.

**STATS C236. Introduction to Bayesian Statistics**

Units: 4

Lecture, three hours; discussion, one hour. Recommended requisite: course 200A or 200B. Designed for graduate students. 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. Examples of applications vary according to interests of students. Concurrently scheduled with course C180. S/U or letter grading.

**STATS C261. Introduction to Pattern Recognition and Machine Learning**

Units: 4

Lecture, three hours. Requisites: course 100B, Mathematics 33A. Introduction to pattern analysis and machine intelligence designed for advanced undergraduate and graduate students. Concurrently scheduled with course C161. S/U or letter grading.