Courses

Below we provide a non-exhaustive list of courses either specifically related to computational biology (graduate-level courses, mostly), or courses necessary for a solid foundation in computational biology’s core disciplines (primarily undergraduate courses). Courses are listed by the offering department.

Bioengineering

Bioengineering 131. Introduction to Computational Molecular and Cell Biology. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: Biology 1A, Mathematics 53 and 54, and either Engineering 77, Computer Science 61A, or Computer Science 61B; or consent of instructor. Topics include computational approaches and techniques to gene structure and finding, sequence alignment using dynamic programming, protein folding and structure prediction, protein-drug interactions, genetic and biochemical pathways and networks, and microarray analysis. Various case studies in these areas are reviewed and web-based computational biology tools will be used by students. Computational biology research connections to biotechnology will be explored. (F,SP)

Bioengineering 231. Introduction to Computational Molecular and Cellular Biology. (4)
Students will receive no credit for 231 after taking 131. Three hours of lecture and one hour of discussion per week. Prerequisites: Mathematics 53 and 54, and either Computer Science 61A or 61B or Engineering 77. Topics include computational approaches and techniques to gene structure and finding, sequence alignment using dynamic programming, protein folding and structure prediction, protein-drug interactions, genetic and biochemical pathways and networks, and microarray analysis. Various case studies in these areas are reviewed and web-based computational biology tools will be used by students. Computational biology research connections to biotechnology will be explored. Bioengineering content: fulfills biological and statistical requirement. Bioengineering Breadth, Core B (Informatics and Genomics) and Core D (Computational Biology). (F,SP)

Bioengineering C141/Statistics C141. Statistics for Bioinformatics. (4)
Three hours of lecture and two hours of laboratory per week. Prerequisites: Computer Science 9C or 9E or Engineering 77 or equivalent; Math 53, 54. Study of bioinformatics problems such as DNA pattern finding, gene expression data analysis, molecular evolution models, and biomolecular sequence database searching. Introduction of the necessary probability and statistics: events, (conditional) probability, random variables, estimation, testing, and linear regression. (F,SP)

Bioengineering 142. Programming and Algorithm Design for Computational Biology and Genomics Applications. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: Math 54 and Molecular and Cell Biology 102; Engineering 77, or Computer Science 61A, or Science 61B or consent of instructor. This course will introduce students to structured software development and select principles of computer science with applications in computational biology and allied disciplines. The principle language used for instruction will be Java with a course module on Perl. Examples and tutorials will draw from problems in computational biology. The course will require one significant programming project, preferably biologically oriented. (F,SP)

Bioengineering 143. Computational Methods in Biology. (4)
Three hours of lecture per week. Prerequisites: Math 53 and Math 54; programming experience preferred but not required. An introduction to biophysical simulation methods and algorithms, including molecular dynamics, Monte Carlo, mathematical optimization, and “non-algorithmic” computation such as neural networks. Various case studies in applying these areas in the areas of protein folding, protein structure prediction, drug docking, and enzymatics will be covered. Core Specialization: Core B (Informatics and Genomics); Core D (Computational Biology); BioE Content: Biological. (F,SP)

Bioengineering C144/Plant and Microbial Biology C144. Introduction to Protein Informatics. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: C100A or 102 or similar background in Molecular Biology. This course will introduce students to the fundamentals of molecular biology, and to the bioinformatics tools and databases used for the prediction of protein function and structure. It is designed to impart both a theoretical understanding of popular computational methods, as well as some experience with protein sequence analysis methods applied to real data. This class includes no programming, and no programming background is required. (F)

Bioengineering C144L. Protein Informatics Laboratory. (2)
Six hours of laboratory per week. Prerequisites: C144 (may be taken concurrently, not required) or consent of instructor. This course is intended to introduce students to a variety of bioinformatics techniques that are used to predict protein function and structure. It is designed to be taken concurrently with C144 (which provides the theoretical foundations for the methods used in the laboratory class), although students can petition to take this laboratory course separately. No programming is performed in this class, and no prior programming experience is required. (F,SP)

Biostatistics/Statistics

Statistics 133. Concepts in Computing with Data. (3)
Three hours of lecture and one hour of laboratory per week. An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results. (F,SP)

Statistics 134. Concepts of Probability. (3)
Students will not receive credit for 134 after taking 101. Three hours of lecture per week. Prerequisites: One year of calculus. An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions. (F,SP)

Statistics 135. Concepts of Statistics. (4)
Students will not receive credit for 135 after taking 102. Three hours of lecture and two hours of laboratory per week. Prerequisites: Math 54 and either Statistics 101 or 134. 133 recommended. A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, goodness-of-fit tests, analysis of variance, and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering. (F,SP)

Statistics C141/Bioengineering C141. Statistics for Bioinformatics. (4)
Three hours of lecture and two hours of laboratory per week. Prerequisites: Computer Science 9C or 9E or Engineering 77 or equivalent; Math 53, 54. Study of bioinformatics problems such as DNA pattern finding, gene expression data analysis, molecular evolution models, and biomolecular sequence database searching. Introduction of the necessary probability and statistics: events, (conditional) probability, random variables, estimation, testing, and linear regression. (F,SP)

Statistics 200A-200B. Introduction to Probability and Statistics at an Advanced Level. (4;4)
Three hours of lecture and two hours of laboratory per week. Prerequisites: Two years of calculus and one semester of linear algebra. Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. (F,SP)

Statistics 210A-210B. Theoretical Statistics. (4;4)
Three hours of lecture per week. Prerequisites: A year of upper division probability and statistics; a course in linear algebra. A survey of mathematical statistics: in particular both small and large sample theorems of hypothesis testing, point estimation, and confidence intervals with applications to topics such as exponential families, univariate and multivariate linear models and nonparametric inference. (F,SP)

Statistics 215A-215B. Statistical Models: Theory and Application. (4;4)
Three hours of lecture and two hours of laboratory per week. The techniques of applied statistics. Data types and structures. Model formulation, fitting and validation. The principal models. Planning and design. Difficulties that arise. Usage of statistical computer packages. Presentation of conclusions. (F,SP)

Statistics C241A/Computer Science C281A. Statistical Learning Theory. (3)
Three hours of lecture per week. Prerequisites: Linear algebra, calculus, basic probability, and statistics, algorithms. Recommended Computer Science 289. Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. (F)

Statistics 243. Introduction to Statistical Computing. (4)
Course may be repeated for credit. Three hours of lecture and two hours of laboratory per week. Prerequisites: Graduate standing. The structure and use of statistical languages and packages. Use of graphical displays in data analysis. Statistical data base management. (F)

Statistics 246. Statistical Genetics. (4)
Three hours of lecture and two hours of laboratory per week. Modelling meiosis, linkage mapping, pedigree analysis, genetic epidemiology. Clone libraries, physical mapping of chromosomes. Radiation hybrid mapping. DNA and protein sequence analysis, molecular evolution, sequence alignment, database searching. Analysis of microarray expression data. (SP)

Public Health C240A. Biostatistical Methods: Advanced Categorical Data Analysis. (4)
Three hours of lecture and two hours of laboratory per week. Prerequisites: Statistics 200A (may be taken concurrently). This course focuses on statistical methods for discrete data collected in public health, clinical and biological studies. Lectures topics include proportions and counts, contingency tables, logistic regression models, Poisson regression and log-linear models, models for polytomous data and generalized linear models. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications. Also listed as Statistics C245A. Offered odd-numbered years. (F) Staff

Public Health C240B. Biostatistical Methods: Survival Analysis and Causality. (4)
Three hours of lecture and two hours of laboratory per week. Prerequisites: Statistics 200B (may be taken concurrently). Analysis of survival time data using parametric and non-parametric models, hypothesis testing, and methods for analyzing censored (partially observed) data with covariates. Topics include marginal estimation of a survival function, estimation of a generalized multivariate linear regression model (allowing missing covariates and/or outcomes), estimation of a multiplicative intensity model (such as Cox proportional hazards model) and estimation of causal parameters assuming marginal structural models. General theory for developing locally efficient estimators of the parameters of interest in censored data models. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications. Also listed as Statistics C245B. Offered odd-numbered years. (SP)

Public Health C240C-D/Statistics C245C-D. Computational Statistics with Applications in Biology and Medicine I and II. (4)
Three hours of lecture and two hours of laboratory per week. Prerequisites: Statistics 200A-200B (may be taken concurrently) or consent of instructor. These two courses provide an introduction to computational statistics, with emphasis on statistical methods and software for addressing high-dimensional inference problems that arise in current biological and medical research. The following topics are surveyed in terms of both statistical methodology and software implementation: numerical and graphical summaries of data; loss-based estimation with cross-validation: parametric and non-parametric density estimation and regression (e.g., maximum likelihood estimation, class prediction), variable selection; optimization; the expectation-maximization (EM) algorithm; smoothing: robust local regression, kernel density estimation, splines; cross-validation; the bootstrap; Monte-Carlo procedures: Markov chain Monte-Carlo (MCMC), importance sampling; hidden Markov models (HMM); cluster analysis; prediction: e.g., classification and regression trees, nearest neighbor predictors, ensemble methods; multiple hypothesis testing; simulation; the design of in silico experiments; and reproducible research. The courses also discusses statistical computing resources, with emphasis on the R language and environment (www.r-project.org). Programming topics to be discussed include: data structures, functions, statistical models, graphical procedures, designing an R package, object-oriented programming, inter-system interfaces. The statistical and computational methods are motivated by and illustrated on data structures that arise in current high-dimensional inference problems in biology and medicine. Neither course is a pre-requisite for the other.

Public Health C240E-F/Statistics C245E-F: Statistical Genomics I and II.
Three hours of lecture and one hour of discussion per week. Prerequisites: Statistics 200A and 200B or equivalent (may be taken concurrently). A course in algorithms and knowledge of at least one computing language (e.g., R, matlab) is recommended. Genomics is one of the fundamental areas of research in the biological sciences and is rapidly becoming one of the most important application areas in statistics. PB HLTH C240E/STAT C245E provides an introduction to statistical and computational methods for the analysis of meiosis, population genetics, and genetic mapping. PB HLTH C240F/STAT C245F focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. Neither course is a pre-requisite for the other.

Public Health 243A-243B. Special Topics in Biostatistics. (1-3;1-3)
One to three hours of lecture/discussion per week. Current issues in biostatistics research. Topics will vary from term to term depending on student demand and faculty availability. Possible topics are bioassay, meta-analysis, compartmental models, biostatistical consulting, covariance structure models, bootstrap and jackknife methods, artificial intelligence techniques in biostatistics. (F,SP)

Public Health C246C. Multiple Testing and Loss Function Based Estimation: Applications in Biological Sciences. (3)
Three hours of lecture per week. Prerequisites: 240D or consent of instructor. Statistical computer-intensive methods have become an integral part of the analysis of cross-sectional and longitudinal studies involving the collection of genomic data such as gene expression, single nucleotide polymorphism, and comparative genomic hybridization measurements across the whole genome. These data structures are extremely high dimensional and the characteristics (parameters of interest) of the population are complex (high dimensional), and outcomes such as survival are often subject to censoring. In addition, one often aims to learn and test many univariate characteristics simultaneously (e.g., regression coefficient for each gene). This course will present (1) a unified loss-function-based approach to learning from the data such characteristics which relies on general cross-validation methodology to select among candidate estimators, (2) resampling-based multiple testing methods controlling type I errors, and (3) clustering methods embedded into a statistical framework. Also listed as Statistics C249C. (F)

Public Health 292, section 103. Statistics and Genomics Seminar. (1-2)

http://www.stat.berkeley.edu/users/sandrine/Teaching/PH292.F08/index.html

Please visit http://www.stat.berkeley.edu/users/sandrine/Teaching/statGenSem.html for a list of seminar speakers Fall 2001 through the present.

Statistics C241B. Advanced Topics in Learning and Decision Making. (3)
Three hours of lecture per week. Prerequisites: C241A, Computer Science C281A. Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning. Also listed as Computer Science C281B. (SP)

Statistics 244. Statistical Computing. (4)
Three hours of lecture and two hours of laboratory per week. Prerequisites: Knowledge of a higher level programming language. Algorithms in statistical computing: random number generation, generating other distributions, random sampling and permutations. Matrix computations in linear models. Non-linear optimization with applications to statistical procedures. Other topics of current interest, such as issues of efficiency, and use of graphics. (SP)

Statistics 298, section 30. Statistical Phylogenetics. (1)

http://fisher.berkeley.edu/~rasmus/STAT298.html

In this course we will mix lectures and student led discussions of current topics in statistical phylogenetics. The objective of phylogenetics is to estimate evolutionary trees, typically from DNA sequence data, or other genetic data. The course will start with a series of lectures aimed at introducing the field of phylogenetics for students of the mathematical sciences. We will thereafter move to student led discussions of current papers in the field. Topics will include model choice and hypothesis testing, MCMC methods, interpretations of the bootstrap in phylogenetics, the star-phylogeny paradox, and more. The course is open to all graduate students, and we will assume no prior knowledge regarding genetics. Familiarity with basic probability theory and theory of statistics will be assumed. During each meeting, except for the first three weeks, one (or sometimes two) students give a presentation. These presentations should provide sufficient background on the subject being covered, review the current state of knowledge in the area, and then critically analyze, in detail, the assigned paper. All students in the class are expected to read the specific paper that will be presented at each session. All presentations should leave at least 20 minutes in the end of the class for discussion. Regular attendance and active participation in discussions is required of all students in the seminar in order to receive a passing grade.

Chemistry

Chemistry 220A. Thermodynamics and Statistical Mechanics. (3)
Three hours of lecture per week. Prerequisites: 120B. A rigorous presentation of classical thermodynamics followed by an introduction to statistical mechanics with the application to real systems. (SP)

Chemistry 223A. Chemical Kinetics. (3)
Three hours of lecture per week. Prerequisites: 220A (may be taken concurrently). Deduction of mechanisms of complex reactions. Collision and transition state theory. Potential energy surfaces. Unimolecular reaction rate theory. Molecular beam scattering studies. (F)

Electrical Engineering and Computer Science

Computer Science 174. Combinatorics and Discrete Probability. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: 170. Permutations, combinations, principle of inclusion and exclusion, generating functions, Ramsey theory. Expectation and variance, Chebychev’s inequality, Chernov bounds. Birthday paradox, coupon collector’s problem, Markov chains and entropy computations, universal hashing, random number generation, random graphs and probabilistic existence bounds. (F,SP)

Computer Science 270. Combinatorial Algorithms and Data Structures. (3)
Three hours of lecture and one hour of discussion per week. Prerequisites: 170. Design and analysis of efficient algorithms for combinatorial problems. Network flow theory, matching theory, matroid theory; augmenting-path algorithms; branch-and-bound algorithms; data structure techniques for efficient implementation of combinatorial algorithms; analysis of data structures; applications of data structure techniques to sorting, searching, and geometric problems.

Computer Science C281A/Statictics C241A. Statistical Learning Theory. (3)
Three hours of lecture per week. Prerequisites: Linear algebra, calculus, basic probability, and statistics, algorithms. Recommended 289. Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. (F)

Computer Science CS 294 section 26/Statistics 260. Computational and Mathematical Population Genetics. (3)

http://www.eecs.berkeley.edu/~yss/courses/sp08-stat260/

Population genetics is the study of the evolutionary forces that produce and maintain genetic variation within species. In addition to being an important branch of biology that has been studied for many decades, it is tightly linked to many areas of mathematics, including probability theory, stochastic processes, combinatorics, and graph theory. In this course, we will explore some computational and mathematical aspects of population genetics closely related to many important biological questions that arise today. Two main themes that will run through this course are Coalescent Theory and Forensic DNA Analysis.

Computer Science C281B. Advanced Topics in Learning and Decision Making. (3)
Three hours of lecture per week. Prerequisites: C281A, Statistics C241A. Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning. Also listed as Statistics C241B. (SP)

Electrical Engineering 221A. Linear System Theory. (4)
Three hours of lecture and two hours of recitation per week. Prerequisites: 120; Mathematics 110 recommended. Basic system concepts; state-space and I/O representation. Properties of linear systems. Controllability, observability, minimality, state and output-feedback. Stability. Observers. Characteristic polynomial. Nyquist test. (F,SP)

Electrical Engineering 222. Nonlinear Systems–Analysis, Stability and Control. (3)
Three hours of lecture per week. Prerequisites: 221A (may be taken concurrently). Basic graduate course in non-linear systems. Second Order systems. Numerical solution methods, the describing function method, linearization. Stability – direct and indirect methods of Lyapunov. Applications to the Lure problem – Popov, circle criterion. Input-Output stability. Additional topics include: bifurcations of dynamical systems, introduction to the “geometric” theory of control for nonlinear systems, passivity concepts and dissipative dynamical systems. (SP)

Electrical Engineering 227A. Introduction to Convex Optimization. (3)
Three hours of lecture per week. Convex optimization is a class of nonlinear optimization problems where the objective to be minimized, and the constraints, are both convex. Contrarily to the more classical linear programming framework, convex programs often go unrecognized, and this is a pity since a large class of convex optimization problems can now be efficiently solved. In addition, it is possible to address hard, nonconvex problems (such as “combinatorial optimization” problems) using convex approximations that are more efficient than classical linear ones. The 3-unit course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, modelling and estimation, finance, and operations research. (F)

Integrative Biology

Integrative Biology 160. Evolution. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: Biology 1B. An analysis of the patterns and processes of organic evolution. History and philosophy of evolutionary thought; the different lines of evidence and fields of inquiry that bear on the understanding of evolution. The major features and processes of evolution through geologic times; the generation of new forms and new lineages; extinction; population processes of selection, adaptation, and other forces; genetics, genomics, and the molecular basis of evolution; evolutionary developmental biology; sexual selection; behavorial evolution; applications of evolutionary biology to medical, agricultural, conservational, and anthropological research. (F)

Integrative Biology 161. Population and Evolutionary Genetics. (4)
Course may be repeated for credit. Three hours of lecture and two hours of computer and/or discussion per week. Prerequisites: Biology 1B and Mathematics 16A or equivalent. Population genetics provides the theoretical foundation for modern evolutionary thinking. It also provides a basis for understanding genetic variation within populations. We will study population genetic theory and use it to illuminate a number of different topics, including the existence of sex, altruism and cooperation, genome evolution speciation, and human genetic variation and evolution. (SP)

Integrative Biology 206. Statistical Phylogenetics. (3)
Three hours of lecture per week. Prerequisites: College level course in calculus. This course is aimed at students who wish to understand the evolutionary models and methods for estimating phylogenies (which are trees representing how organisms are related to one another). Topics include continuous-time Markov chains as applied in phylogenetics; maximum likelihood estimation; Bayesian estimation; the combinatorics of evolutionary trees; Markov chain Monte Carlo; distance and parsimony methods for estimating trees; optimization strategies for finding best trees. Students will learn to write computer programs that implement many of the methods discussed in class, and apply their knowledge in a research project. (F)

Mathematics

Mathematics 127. Mathematical and Computational Methods in Molecular Biology. (4)
Three hours of lecture per week. Prerequisites: 53, 54, and 55; Statistics 20 recommended. Introduction to mathematical and computational problems arising in the context of molecular biology. Theory and applications of combinatorics, probability, statistics, geometry, and topology to problems ranging from sequence determination to structure analysis. (F,SP)

Mathematics 172. Combinatorics. (4)
Three hours of lecture per week. Prerequisites: 55. Basic combinatorial principles, graphs, partially ordered sets, generating functions, asymptotic methods, combinatorics of permutations and partitions, designs and codes. Additional topics at the discretion of the instructor. (F,SP)

Mathematics 239. Discrete Mathematics for the Life Sciences. (4)
Three hours of lecture per week. Prerequisites: Statistics 134 or equivalent introductory probability theory course, or consent of instructor. Introduction to algebraic statistics and probability, optimization, phylogenetic combinatorics, graphs and networks, polyhedral and metric geometry. (F,SP)

Mathematics 249. Algebraic Combinatorics. (4)
Three hours of lecture per week. Prerequisites: 250A or consent of instructor. (I) Enumeration, generating functions and exponential structures, (II) Posets and lattices, (III) Geometric combinatorics, (IV) Symmetric functions, Young tableaux, and connections with representation theory. Further study of applications of the core material and/or additional topics, chosen by instructor. (F,SP)

Molecular and Cell Biology

Molecular and Cell Biology C112/Plant and Microbial Biology C112. General Microbiology. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: C100A/Chemistry C130 or 102 or consent of instructor. Formerly 112. This course will explore the molecular bases for physiological and biochemical diversity among members of the two major domains, Bacteria and Archaea. The ecological significance and evolutionary origins of this diversity will be discussed. Molecular, genetic, and structure-function analyses of microbial cell cycles, adaptive responses, metabolic capability, and macromolecular syntheses will be emphasized. (F)

Molecular and Cell Biology C112L/Plant and Microbial Biology C112L. General Microbiology Laboratory. (2)
Four hours of laboratory and one hour of discussion per week. Prerequisites: C112 or Molecular and Cell Biology C112 (may be taken concurrently). Experimental techniques of microbiology designed to accompany the lecture in C112 and C148. The primary emphasis in the laboratory will be on the cultivation and physiological and genetic characterization of bacteria. Laboratory exercises will include the observation, enrichment, and isolation of bacteria from selected environments. Also listed as Plant and Microbial Biology C112L. (F)

Molecular and Cell Biology 130. Cell Biology. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: 102, and Biology 1A, 1AL. An introductory survey of cell and developmental biology. The assembly of supramolecular structures; membrane structure and function; the cell surface; cytoplasmic membranes; the cytoskeleton and cell motility; the eukaryotic genome, chromatin, and gene expression; the cell cycle; organelle biogenesis, differentiation, and morphogenesis. (F,SP)

Molecular and Cell Biology 130L. Cell and Developmental Biology Laboratory. (4)
One hour of lecture and seven hours of laboratory per week. Prerequisites: May be taken concurrently with 130. Experimental analyses of central problems in cell biology using modern techniques, including biochemical analysis of DNA and proteins, fluorescence microscopy of the cytoskeleton and organelles, DNA transfection of cultured mammalian cells, analysis of organelle functions, reporter assays of signal transduction pathways, and analysis of cell cycle progression and apoptosis. (F,SP)

Molecular and Cell Biology 132. Biology of Human Cancer. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: 102 or 110 (may be taken concurrently); Biology 1A, 1AL, 1B. Formerly 135G. The course is designed for students interested in learning about the molecular and cell biology of cancer and how this knowledge is being applied to the prevention, diagnosis and therapy of cancer. Topics covered include tumor pathology and epidemiology; tumor viruses and oncogenes; intracellular signaling; tumor suppressors; multi-step carcinogenesis and tumor progression; genetic instability in cancer; tumor-host interactions; invasion and metastasis; tumor immunology; cancer therapy. (F)

Molecular and Cell Biology 140. General Genetics. (4)
Students will receive 1 unit of credit for 140 after taking C142 or Integrative Biology C163. Three hours of lecture and one hour of discussion per week. Prerequisites: C100A/Chemistry C130 and 110 or consent of instructor. (110 may taken concurrently.).In-depth introduction to genetics, including mechanisms of inheritance; gene transmission and recombination; transposable DNA elements; gene structure, function, and regulation; and developmental genetics. Some exams may be given in the evening. (F,SP)

Molecular and Cell Biology 140L. Genetics Laboratory. (4)
Six hours of laboratory and two hours of lecture per week. Prerequisites: 140. May be taken concurrently. Experimental techniques in classical and molecular genetics. (SP)

Molecular and Cell Biology C146/Bioengineering C146/Plant and Microbial Biology C146. Topics in Computational Biology and Genomics. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: Bioengineering 142, Computer Science 61A, or equivalent ability to write programs in Java, Perl, C, or C++; 100, 102, or equivalent; or consent of instructor. Instruction and discussion of topics in genomics and computational biology. Working from evolutionary concepts, the course will cover principles and application of molecular sequence comparison, genome sequencing and functional annotation, and phylogenetic analysis. (SP)

Molecular and Cell Biology 200. Advanced Biochemistry and Molecular Biology. (4)
Three hours of lecture and one hour of discussion per week. Prerequisites: 110 or equivalent. General course for first-year graduate students. Recent advances in the study of structural, functional, and genetic characteristics of prokaryotic and eukaryotic cells and their viruses, macromolecular syntheses, regulation of gene expression, chromosome organization, cell signaling, proliferation, and differentiation. Admission to the course requires formal consent of the instructors, except for MCB graduate students and graduate students in the laboratories of MCB faculty. Enrollment is restricted to 45. Auditors are not permitted in the discussion sessions. (F)

Molecular and Cell Biology 206. Physical Biochemistry. (3)
Three hours of lecture per week. Prerequisites: Year courses in organic chemistry and physical chemistry. 100 recommended. Application of modern physical concepts and experimental methods to the analysis of the structure, function, and interaction of large molecules of biological interest. (F)

Molecular and Cell Biology 247. Genome Project Laboratory. (4)
Two hours of lecture and six hours of laboratory per week. Prerequisites: Consent of instructor. The course will require the use of UNIX operating systems and simple computer scripting. Students without these skills will receive bootcamp training in the first week of class. This course will provide hands-on experience with the sequencing and interpretation of a complex genome. Students will be taught the conceptual underpinnings of genome assembly, annotation, and analysis. They will be provided with unassembled output of automated DNA sequencers, and will produce a fully assembled and annotated genome by the end of the semester. Preference will be given to Molecular and Cell Biology graduate students. (SP)

Plant and Microbial Biology

Plant and Microbial Biology 200B. Genomics and Computational Biology. (1.5)
Three hours of lecture and one and one-half hours of discussion for five weeks. Prerequisites: Consent of instructor. Principles of computational and genomic biology. Covers evolutionary, algorithmic, and statistical foundations of sequence analysis, allowing students to understand concepts underlying modern computational methods. Practical applications wil be pursued in student-coordinated sessions. Combined lecture with 220B. (F)