Course Descriptions

Core classes must be completed in the first two years, and usually are completed the first year.

Each student in the Bioinformatics and Systems Biology (BISB) track must take four core courses:

• Bioinformatics II (BENG 202/CSE 282). Introduction to Bioinformatics Algorithms (4 units)

  (Formerly BENG 202/CSE 257A.) Introduction to methods for sequence analysis. Applications to genome and proteome sequences. Protein structure, sequence-structure analysis.
  Prerequisite: Pharm 201 or consent of instructor. (W)

• Bioinformatics III (BENG 203/CSE 283). Genomics, Proteomics, and Network Biology (4 units)

  Annotating genomes, characterizing functional genes, profiling, reconstructing pathways.
  Prerequisites: Pharm 201, BENG 202/CSE 282, or consent of instructor. (S)

• Bioinformatics IV (MATH 283). Statistical Methods in Bioinformatics (4 units)

  This course will cover material related to the analysis of modern genomic data; sequence analysis, gene expression/functional genomics analysis, and gene mapping/applied population genetics. The course will focus on statistical modeling and inference issues and not on database mining techniques.
  Prerequisites: one year of calculus, one statistics course or consent of instructor.

• One class selected from the “Fourth Core Class” options below (except BENG 203/CSE 283).

Each student in the Biomedical Informatics (BMI) track must take four core courses:

• Bioinformatics II (BENG 202/CSE 282). Introduction to Bioinformatics Algorithms (4 units)

  (Formerly BENG 202/CSE 257A.) Introduction to methods for sequence analysis. Applications to genome and proteome sequences. Protein structure, sequence-structure analysis.
  Prerequisite: Pharm 201 or consent of instructor. (W)

• MED 264. Principles of Biomedical Informatics (4 units)

  Students are introduced to the fundamental principles of BMI and to the problems that define modern healthcare. The extent to which BMI can address healthcare problems is explored. Topics covered include structuring of data, computing with phenotypes, integration of molecular, image and other non-traditional data types into electronic medical records, clinical decision support systems, biomedical ontologies, data and communication standards, data aggregation, and knowledge discovery. (F)

• Bioinformatics IV (MATH 283). Statistical Methods in Bioinformatics (4 units)

  This course will cover material related to the analysis of modern genomic data; sequence analysis, gene expression/functional genomics analysis, and gene mapping/applied population genetics. The course will focus on statistical modeling and inference issues and not on database mining techniques.
  Prerequisites: one year of calculus, one statistics course or consent of instructor.

• One class selected from the “Fourth Core Class” options below.

For the fourth core class, choose one of the following. In the event that a student completes two or more of these with suitable grades, one will count as core and the other(s) as electives. (If an additional fourth core class is not on the list of electives, it can count towards the required 16 units of electives, but not towards an elective field requirement like CS or BIO.) Some options may not be offered every year; choose from options available by your deadlines.

• CSE 280A. Algorithms in Computational Biology (4 units)

  (Formerly CSE 206B.) The course focuses on algorithmic aspects of modern bioinformatics and covers the following topics: computational gene hunting, sequencing, DNA arrays, sequence comparison, pattern discovery in DNA, genome rearrangements, molecular evolution, computational proteomics, and others.
  Prerequisites: CSE 202 preferred or consent of instructor.

• CSE 284. Personal Genomics for Bioinformaticians (4 units)

  This course provides an introduction to bioinformatics techniques for analyzing and interpreting human genomes. Topics covered include an introduction to medical and population genetics, ancestry, finding and interpreting disease-causing variants, genome-wide association studies, genetic risk prediction, analyzing next-generation sequencing data, how to scale current genomics techniques to analyze hundreds of thousands of genomes, and the social impact of the personal genomics revolution. Programming experience, familiarity with the UNIX command line, and a basic course in probability and statistics are strongly recommended. Students may not receive credit for CSE 284 and CSE 291 (E00) taught winter 2017 with the same subtitle.
  Prerequisites: graduate standing.

• ECE 208. Computational Evolutionary Biology (4 units)

  Evolutionary biology (e.g., the study of the tree of life) uses computational methods from statistics and machine learning. We cover methods of broad use in many fields and apply them to biology, focusing on scalability to big genomic data. Topics include dynamic programming, continuous time Markov models, hidden Markov models, statistical inference of phylogenies, sequence alignment, uncertainty (e.g., bootstrapping), and heterogeneity (e.g., phylogenetic mixture models).
  Prerequisites: graduate standing.

• BNFO 286. Network Biology and Biomedicine (4 units)

  (Cross-listed with MED 283.) Networks are pervasive in molecular biology and medicine. This course introduces biomolecular networks and their major analysis techniques and roles in biomedical research, including pathway-based genetic analysis. Recommended familiarity with bioinformatics programming; course examples are taught in Python.
  Prerequisites: Genetics (BICD 100, BGGN 223, or BIOM 252) and graduate-level statistics (MED 268, MATH 283, MATH 281A, MATH 281C, FMPH 221, or FMPH 222). Prerequisites may be waived with consent of instructor.

• Bioinformatics III (BENG 203/CSE 283). Genomics, Proteomics, and Network Biology (4 units) – This is core in the BISB track. In the BMI track, it may be taken as the 4th core class or as an elective.

  Annotating genomes, characterizing functional genes, profiling, reconstructing pathways.
  Prerequisites: Pharm 201, BENG 202/CSE 282, or consent of instructor. (S)

All students in years 1-2 must take the Colloquium for their track in fall, winter, and spring quarters: BNFO 281 for the BISB track, or MED 262 for the BMI track.

• BNFO 281. Seminar in Bioinformatics and Systems Biology (1 unit)

  Weekly seminars by faculty and visiting bioinformaticists presenting their research. S/U grades only. May be taken for credit nine times.

• MED 262. Current Trends in Biomedical Informatics (1 unit)

  Weekly talks by researchers introduce students to current research topics within BMI. Speakers are drawn from academia, health care organizations, industry, and government. (F,W,S)

All students in years 1-2 must take the Student Research Talks in fall, winter, and spring quarters.

• BNFO 283. Bioinformatics Student Research Talks (1 unit)

  Weekly presentations by bioinformatics and systems biology students about research projects that are proposed or completed. Faculty mentors are present to contribute critiques and suggestions. S/U grades only. May be taken for credit nine times.
  Prerequisites: bioinformatics and systems biology program graduate students only.

All students must take the ethics course BNFO 294 (previously SOMI 226 or BIOM 219) by the end of the second year. However, funding sources may require that it be taken the first year, so we recommend taking it the first year; in particular, all students on NIH training grants or other NIH funding are required to take ethics in their first year.

Special registration procedure: Students must register for the ethics course both via WebReg as well as via the “Scientific Ethics” registration form on ethics.ucsd.edu; please do both of these steps promptly when the courses open up, as they may fill quickly. The course is taken by many UCSD employees (not just students), so the Ethics Program uses its own registration form. Registration on WebReg is necessary for students to receive course credit. To take it in the summer, register on ethics.ucsd.edu, and give the “certificate of completion” to the BISB graduate coordinator, in lieu of BNFO 294 credit on your transcript.

• BNFO 294. Scientific Ethics (1 unit)

  Lectures, readings, and discussions about the responsible conduct and reporting of research, working with others in science, and social responsibilities; the course is designed as an option for meeting federal regulations for such training. S/U grades only.
  Prerequisites: Bioinformatics and Systems Biology graduate students.

Students must also take the Scientific Ethics Refresher Course every four years thereafter; e.g., students who take BNFO 294 in their first year should take the Refresher in years 5 and 9 (if still here). The Refresher course does not have a course number and will not appear on your transcript. Register for it on ethics.ucsd.edu only (not WebReg). Afterwards, give the “certificate of completion” to both the BISB Graduate Coordinator and your advisor (it may be needed for their grant report).

During the academic year, all students must be enrolled in the appropriate research course for their level. Students typically do three rotations in year 1 (BNFO 298) and then do research units (BNFO 299) with their thesis advisor in years 2 and later. BNFO 299 units may be varied to meet the full-time enrollment requirement of 12 units per quarter in fall, winter, and spring. During the summer, students are expected to do research as well, but should not enroll in BNFO 298 or BNFO 299. During all quarters and the summer, students are responsible for satisfying program requirements including proposals, reports, presentations, committee meetings, notifying the graduate coordinator when joining/changing labs, etc.; the only difference is that students do not enroll in BNFO 298 or BNFO 299 in the summer. In addition, each student will make periodic research presentations to the graduate program students/faculty. Students will also discuss their progress at the annual program meeting to be held each year.

• BNFO 298. Research Rotation (4 units)

  Laboratory research of special topics under the direction of a program faculty member. The purpose is to train students in specific research methodologies and identify a laboratory in which to pursue doctoral dissertation research. Three quarters are required for PhD candidates. May be taken for credit up to six times.
  Prerequisites: bioinformatics graduate students and consent of instructor and program.

• BNFO 299. Graduate Research (1–12 units)

  Independent work by graduate students engaged in research and writing theses. S/U grades only. May be taken for credit fifteen times.
  Prerequisites: bioinformatics and systems biology graduate students and consent of instructor.

All students must serve as a teaching assistant (TA) for at least two quarters. Please contact the BISB graduate coordinator by email in advance of each teaching assistantship to complete required paperwork and other administrative arrangements.

Students should enroll in BNFO 500 (Teaching Experience) or an equivalent course code in another department (BENG 501, BGGN 500, CHEM 500, CSE 500, MATH 500, etc.), during each quarter in which they are a teaching assistant. For summer teaching assistantships, please contact the BISB graduate coordinator to record it in lieu of BNFO 500 credit.

A typical teaching assistantship is 110 hours/quarter (25% load, 2 units of BNFO 500); however, this varies by class. 220 hours/quarter is a 50% load, 4 units.

Students are strongly encouraged to do only 25% teaching assistantships, due to the impact on time available for coursework and research. If you are considering a 50% position, please check if it can be split into two 25% positions. Please submit a petition to the BISB Curriculum Committee for any position with a load above 25%.

• BNFO 500. Teaching Assistantship (2–4 units)

  Teaching experience in an appropriate bioinformatics undergraduate or graduate course under direction of the faculty member in charge of the course. Each PhD candidate must complete two academic quarters of experience for S/U grade only. May be taken for credit four times.
  Prerequisites: graduate standing and consent of instructor (department stamp). (F,W,S)

• BENG 230A. Biochemistry (4 units)

  A graduate course in biochemistry especially tailored to the requirements and background of bioengineering graduate students. It will cover the important macro- and small molecules in cells that are the major constituents, or that function as signaling molecules or molecular machineries. The structures, pathways, interactions, methodologies, and molecular designs using recombinant DNA technology will be covered.
  Prerequisites: restricted to bioengineering graduate students with major code BE75. (F)

• CHEM 209. Macromolecular Recognition (4 units)

  Structures and functions of nucleic acids, folding and catalysis of nucleic acids, motifs and domains of proteins, principles of protein-protein interactions, chemistry of protein/DNA and protein/RNA interfaces, conformational changes in macromolecular recognition.
  Prerequisites: biochemistry background and graduate standing, or approval of instructor.

• CHEM 213A. Structure of Biomolecules and Biomolecular Assemblies (4 units)

  A discussion of structures of nucleic acids and proteins and their larger assemblies. The theoretical basis for nucleic acid and protein structure, as well as methods of structure determination including X-ray crystallography, cryoEM, and computational modeling approaches will be covered. Letter grades only.
  Prerequisites: graduate standing.

• CHEM 213B. Biophysical Chemistry of Macromolecules (4 units)

  Renumbered from CHEM 213. A discussion of the physical principles governing biomolecular structure and function. Experimental and theoretical approaches to understand protein dynamics, enzyme kinetics, and mechanisms will be covered. Students may only receive credit for one of the following: CHEM 213 or 213B. May be co-scheduled with CHEM 113.

  Note: Previously, CHEM 213 was a program elective. The syllabus has been split and expanded into two quarters, CHEM 213A and 213B. You may take either one, or may take both in either order. The courses will be offered in alternate years.

• CHEM 216. Chemical Biology (4 units)

  A discussion of current topics in chemical biology including mechanistic aspects of enzymes and cofactors, use of modified enzymes to alter biochemical pathways, chemical intervention in cellular processes, and natural product discovery.
  Prerequisites: graduate standing or consent of instructor. (May not be offered every year.)

• BICD 100. Genetics (4 units)

  An introduction to the principles of heredity emphasizing diploid organisms. Topics include Mendelian inheritance and deviations from classical Mendelian ratios, pedigree analysis, gene interactions, gene mutation, linkage and gene mapping, reverse genetics, population genetics, and quantitative genetics. Students may receive credit for one of the following: BICD 100 or BICD 100R.
  Prerequisites: BILD 1 and BILD 3.

• BGGN 206A. Concepts of Reasoning and Experimentation (CORE) I (4 units) – Request authorization from Biology to enroll using EAsy.

  This course focuses on key concepts and the methods and logic used to ask and answer challenging biological questions. Course work will be organized around fundamental topics in molecular and cell biology and focus on problem solving, research articles, and/or research seminars to examine best practices in making reasoned scientific arguments and using logical experimental design to tackle biological problems, particularly at the molecular and cellular level. Enrollment restricted to the following major codes: BI77 and BI78.

• BGGN 220. Graduate Molecular Biology (4 units)

  Provides a broad, advanced-level coverage of modern molecular biology for graduate students. Topics include gene structure and regulation, chromatin structure, mechanisms of transcription, RNA processing, translation, and turnover. The format includes lectures and discussion of selected papers. Letter grades only.

• BGGN 223. Graduate Genetics (4 units)

  Advanced coverage of classical and cutting-edge genetic technologies in a wide variety of organisms—bacteria, plants, insects, worms, fish, and mammals. The power of genetic approaches to provide fundamental insights into important questions in development, physiology, behavior, medicine, and evolution. Course format centered on discussion of research papers with genetic analysis at their core. Emphasis on exploring essential genetic concepts, principles, and mechanisms throughout biology. Letter grades only.

• BICD 110. Cell Biology (4 units)

  The structure and function of cells and cell organelles, cell growth and division, motility, cell differentiation and specialization.
  Prerequisites: BIBC 100 or BIBC 102 or CHEM 114A or CHEM 114B.

• BICD 130. Embryos, Genes, and Development (4 units)

  Developmental biology of animals at the tissue, cellular, and molecular levels. Basic processes of embryogenesis in a variety of invertebrate and vertebrate organisms. Cellular and molecular mechanisms that underlie cell fate determination and cell differentiation. More advanced topics such as pattern formation and sex determination are discussed. Open to upper-division students only.
  Recommended preparation: BICD 110 and BIMM 100. Prerequisites: upper-division standing; BICD 100 or BICD 100R and BIBC 100 or BIBC 102 or CHEM 114A or CHEM 114B.

• BGGN 222. Graduate Cell Biology (4 units)

  Coverage of modern cell biology. Topics will be chosen from the following: the structure and function of membranes; endocytosis; protein targeting; intracellular organelles, including the nucleus, ER, Golgi; the cytoskeleton and molecular motors; mitosis and cell division; autophagy; cell death; cell signaling; cell-cell interactions. The course will include discussions on molecular approaches to cell biology as well as dissecting interconnections between cell biology and disease. Letter grades only.

• CHEM 221 / BGGN 230. Signal Transduction (4 units)

  (Cross-listed with BGGN 230.) The aim of this course is to develop an appreciation for a variety of topics in signal transduction. We will discuss several historical developments while the focus will be on current issues. Both experimental approaches and results will be included in our discussions. Topics may vary from year to year.
  Prerequisites: biochemistry and molecular biology. (May not be offered every year.)

• CSE 101. Design and Analysis of Algorithms (4 units)

  Design and analysis of efficient algorithms with emphasis of nonnumerical algorithms such as sorting, searching, pattern matching, and graph and network algorithms. Measuring complexity of algorithms, time and storage. NP-complete problems.
  Prerequisites: CSE 21 or MATH 154 or MATH 158 or MATH 184 or MATH 188 and CSE 12 or DSC 30; restricted to undergraduates. Graduate students will be allowed as space permits.

• CSE 200. Computability and Complexity (4 units)

  Computability review, including halting problem, decidable sets, r.e. sets, many-one reductions; TIME(t(n)), SPACE(s(n)) and general relations between these classes; L, P, PSPACE, NP; NP—completeness; hierarchy theorems; RP, BPP.
  Prerequisites: CSE 105 or equivalent.

• CSE 202. Algorithm Design and Analysis (4 units)

  The basic techniques for the design and analysis of algorithms. Divide-and-conquer, dynamic programming, data structures, graph search, algebraic problems, randomized algorithms, lower bounds, probabilistic analysis, parallel algorithms.
  Prerequisites: CSE 101 or equivalent.

• CSE 280A. Algorithms in Computational Biology (4 units)

  (Formerly CSE 206B.) The course focuses on algorithmic aspects of modern bioinformatics and covers the following topics: computational gene hunting, sequencing, DNA arrays, sequence comparison, pattern discovery in DNA, genome rearrangements, molecular evolution, computational proteomics, and others.
  Prerequisites: CSE 202 preferred or consent of instructor.

• Bioinformatics III (BENG 203/CSE 283). Genomics, Proteomics, and Network Biology (4 units) – This is core in the BISB track. In the BMI track, it may be taken as the 4th core class or as an elective.

  Annotating genomes, characterizing functional genes, profiling, reconstructing pathways.
  Prerequisites: Pharm 201, BENG 202/CSE 282, or consent of instructor. (S)

• MATH 261A. Probabilistic Combinatorics and Algorithms (4 units)

  Introduction to the probabilistic method. Combinatorial applications of the linearity of expectation, second moment method, Markov, Chebyschev, and Azuma inequalities, and the local limit lemma. Introduction to the theory of random graphs.
  Prerequisites: graduate standing or consent of instructor.

• BNFO 285. Statistical Learning in Bioinformatics (4 units)

  (Cross-listed with ECE 204 and BENG 285.) A hallmark of bioinformatics is the computational analysis of complex data. The combination of statistics and algorithms produce statistical learning methods that automate the analysis of complex data. Such machine learning methods are widely used in systems biology and bioinformatics. This course provides an introduction to statistical learning and assumes familiarity with key statistical methods. Letter grades only. Students may not receive credit for BNFO 285 and ECE 204 and BENG 285. 
  Prerequisites: MATH 283 or ECE 271A or ECE 271B.

• CSE 250A. Principles of Artificial Intelligence: Probabilistic Reasoning and Learning (4 units)

  Methods based on probability theory for reasoning and learning under uncertainty. Content may include directed and undirected probabilistic graphical models, exact and approximate inference, latent variables, expectation-maximization, hidden Markov models, Markov decision processes, applications to vision, robotics, speech, and/or text.
  Recommended preparation: CSE 103 or similar course. Prerequisites: graduate standing in CSE or consent of instructor.

• CSE 251A. Principles of Machine Learning: Learning Algorithms (4 units)

  (Formerly CSE 250B.) Algorithms for supervised and unsupervised learning from data. Content may include maximum likelihood; log-linear models, including logistic regression and conditional random fields; nearest neighbor methods; kernel methods; decision trees; ensemble methods; optimization algorithms; topic models; neural networks; and backpropagation. Renumbered from CSE 250B. Students may not receive credit for CSE 251A and CSE 250B.
  Recommended preparation: CSE 103 or similar course. Prerequisites: graduate standing or consent of instructor.

• CSE 251B. Principles of Machine Learning: Neural Networks for Pattern Recognition (4 units)

  (Formerly CSE 253.) This course covers Hopfield networks, application to optimization problems layered perceptrons, recurrent networks, and unsupervised learning. Programming exercises explore model behavior with a final project or exam on a cognitive science, artificial intelligence, or optimization problem of the student’s choice. Renumbered from CSE 253. Students may not receive credit for CSE 251B and CSE 253.
  Recommended preparation: Knowledge of C. Prerequisites: graduate standing.

• CSE 255. Data Mining and Predictive Analytics (4 units)

  Learning methods for applications. Content may include data preparation, regression and classification algorithms, support vector machines, random forests, class imbalance, overfitting, decision theory, recommender systems and collaborative filtering, text mining, analyzing social networks and social media, protecting privacy, A/B testing.
  Recommended preparation: CSE 103 or similar. Prerequisites: graduate standing or consent of instructor.

• CSE 258. Recommender Systems and Web Mining (4 units)

  Current methods for data mining and predictive analytics. Emphasis is on studying real-world data sets, building working systems, and putting current ideas from machine learning research into practice.
  Recommended preparation: No previous background in machine learning is required, but students should be comfortable with programming (all example code will be in Python), and with basic optimization and linear algebra. Prerequisites: graduate standing.

• ECE 208. Computational Evolutionary Biology (4 units)

  Evolutionary biology (e.g., the study of the tree of life) uses computational methods from statistics and machine learning. We cover methods of broad use in many fields and apply them to biology, focusing on scalability to big genomic data. Topics include dynamic programming, continuous time Markov models, hidden Markov models, statistical inference of phylogenies, sequence alignment, uncertainty (e.g., bootstrapping), and heterogeneity (e.g., phylogenetic mixture models).
  Prerequisites: graduate standing.

• ECE 271A. Statistical Learning I (4 units)

  Bayesian decision theory; parameter estimation; maximum likelihood; the bias-variance trade-off; Bayesian estimation; the predictive distribution; conjugate and noninformative priors; dimensionality and dimensionality reduction; principal component analysis; Fisher’s linear discriminant analysis; density estimation; parametric vs. kernel-based methods; expectation-maximization; applications.
  Recommended preparation: ECE 109. Prerequisites: graduate standing.

• MATH 274. Numerical Methods for Physical Modeling (4 units)

  (Conjoined with MATH 174.) Floating point arithmetic, direct and iterative solution of linear equations, iterative solution of nonlinear equations, optimization, approximation theory, interpolation, quadrature, numerical methods for initial and boundary value problems in ordinary differential equations. Students may not receive credit for both MATH 174 and PHYS 105, AMES 153 or 154. (Students may not receive credit for MATH 174 if MATH 170A, B, or C has already been taken.) Graduate students will complete an additional assignment/exam.
  Prerequisites: MATH 20D or 21D, and either MATH 20F or MATH 31AH, or consent of instructor.

• MATH 280A. Probability Theory I (4 units)

  This is the first course in a three-course sequence in probability theory. Topics covered in the sequence include the measure-theoretic foundations of probability theory, independence, the Law of Large Numbers, convergence in distribution, the Central Limit Theorem, conditional expectation, martingales, Markov processes, and Brownian motion.
  Recommended preparation: completion of real analysis equivalent to MATH 140A-B strongly recommended. Prerequisites: graduate standing.

• MATH 281A. Mathematical Statistics (4 units)

  Statistical models, sufficiency, efficiency, optimal estimation, least squares and maximum likelihood, large sample theory.
  Prerequisites: advanced calculus and basic probability theory or consent of instructor.

• MATH 281B. Mathematical Statistics (4 units)

  Hypothesis testing and confidence intervals, one-sample and two-sample problems. Bayes theory, statistical decision theory, linear models and regression.
  Prerequisites: advanced calculus and basic probability theory or consent of instructor.

• MATH 281C. Mathematical Statistics (4 units)

  Nonparametrics: tests, regression, density estimation, bootstrap and jackknife. Introduction to statistical computing using S plus.
  Prerequisites: advanced calculus and basic probability theory or consent of instructor.

• MATH 282A. Applied Statistics I (4 units)

  General theory of linear models with applications to regression analysis. Ordinary and generalized least squares estimators and their properties. Hypothesis testing, including analysis of variance, and confidence intervals. Completion of courses in linear algebra and basic statistics are recommended prior to enrollment.
  Prerequisites: graduate standing or consent of instructor. (S/U grades permitted.)

• MATH 282B. Applied Statistics II (4 units)

  Diagnostics, outlier detection, robust regression. Variable selection, ridge regression, the lasso. Generalized linear models, including logistic regression. Data analysis using the statistical software R. Students who have not taken MATH 282A may enroll with consent of instructor.
  Prerequisites: MATH 282A or consent of instructor. (S/U grades permitted.)

• MATH 284. Lifetime Data Analysis (4 units)

  Survival analysis is an important tool in many areas of applications including biomedicine, economics, engineering. It deals with the analysis of time to events data with censoring. This course discusses the concepts and theories associated with survival data and censoring, comparing survival distributions, proportional hazards regression, nonparametric tests, competing risk models, and frailty models. The emphasis is on semiparametric inference, and material is drawn from recent literature. Students who have not completed listed prerequisites may enroll with consent of instructor.
  Prerequisites: MATH 282A. Students who have not completed listed prerequisite may enroll with consent of instructor.

• PHYS 210A. Equilibrium Statistical Mechanics (5 units)

  Statistical ensembles: microcanonical, canonical, and grand canonical formulations; principle of maximum entropy. Thermodynamics: thermodynamic potentials, phase equilibria, entropy of mixing. Quantum statistics: photon statistics; ideal Bose and Fermi gases. Interacting systems: Ising model, liquids and plasmas. Phase transitions: van der Waals system, mean field theory, Landau theory, global symmetries, fluctuations.
  Prerequisites: PHYS 200A, 212A-B.

• PHYS 210B. Nonequilibrium Statistical Mechanics (4 units)

  Transport phenomena; kinetic theory and the Chapman-Enskog method; hydrodynamic theory; nonlinear effects and the mode coupling method. Stochastic processes; Langevin and Fokker-Planck equation; fluctuation-dissipation relation; multiplicative processes; dynamic field theory; Martin-Siggia-Rose formalism; dynamical scaling theory.
  Prerequisites: PHYS 210A.

• BENG 211. Systems Biology and Bioengineering I: Biological Components (4 units)

  Components of biological systems, their biochemical properties and function. The technology used for obtaining component lists. Relationship within and integration of component lists. Structured vocabularies and component ontologies. Algorithms for comparative approaches in deciphering and mining component lists.
  Prerequisites: BENG 230A or BIMM 100, or consent of instructor. (F)

• BENG 212. Systems Biology and Bioengineering II: Large-Scale Data Analysis (4 units)

  Analysis of biological data can be performed at four levels: 1) statistical analysis, 2) integration of knowledge, 3) integration of networks, and 4) integration of biophysical laws. This course teaches the first two levels. The first level consists of specialized topics in statistical analysis of large-scale data sets and machine learning. The second level consists of leveraging prior knowledge for the analysis of diverse, multi-omic datasets and network reconstruction.
  Prerequisites: graduate standing. (W)

• BENG 227. Transport Phenomena in Living Systems (4 units)

  This course describes the movement of heat and chemical mass in biological systems. Diffusion, convection and biochemical reactions in a variety of biological and engineering examples are analyzed and modeled. Students that have taken BENG 222 cannot take BENG 227 for credit.
  Prerequisites: BENG 221, graduate standing, or consent of instructor. (S)

• BNFO 286. Network Biology and Biomedicine (4 units)

  (Cross-listed with MED 283.) Networks are pervasive in molecular biology and medicine. This course introduces biomolecular networks and their major analysis techniques and roles in biomedical research, including pathway-based genetic analysis. Recommended familiarity with bioinformatics programming; course examples are taught in Python.
  Prerequisites: Genetics (BICD 100, BGGN 223, or BIOM 252) and graduate-level statistics (MED 268, MATH 283, MATH 281A, MATH 281C, FMPH 221, or FMPH 222). Prerequisites may be waived with consent of instructor.

• BENG 125. Modeling and Computation in Bioengineering (4 units)

  Computational modeling of molecular bioengineering phenomena: excitable cells, regulatory networks, and transport. Application of ordinary, stochastic, and partial differential equations. Introduction to data analysis techniques: power spectra, wavelets, and nonlinear time series analysis.
  Prerequisites: BENG 122A or BENG 123 or consent of department. (S)

• BNFO 284. Nonlinear Dynamics in Quantitative Biology (4 units)

  Qualitative, analytical and computational mathematical modeling techniques applied to regulatory networks and signaling networks. Stability, bifurcations, oscillations, multistability, hysteresis, multiple timescales, and chaos. Introduction to experimental data analysis and control techniques. Applications to synthetic biology, cellular population dynamics, ad canonical signaling networks (inflammation, tumor suppression, metabolism). Letter grades only.
  Prerequisites: bioinformatics and systems biology graduate students only.

• PHYS 276. Quantitative Microbiology (4 units)

  A quantitative description of bacteria from molecular interactions through cellular and population level behaviors. Topics will vary yearly, covering processes including gene regulation, molecular signaling, genetic circuits, stochastic dynamics, metabolic control, cell division, cell growth control, stress response, chemotaxis, biofilm formation. May be coscheduled with PHYS 176.
  Recommended preparation: an introductory course in biology is helpful but not necessary.

• CHEM 220. Regulatory Circuits in Cells (4 units)

  Modulation cellular activity and influencing viral fate involve regulatory circuits. Emergent properties include dose response, cross regulation, dynamic, and stochastic behaviors. This course reviews underlying mechanisms and involves mathematical modeling using personal computer tools.
  Recommended: some background in biochemistry and/or cellular biology. Mathematical competence at the level of lower-division college courses.

• MED 263. Bioinformatics Applications to Human Disease (4 units)

  Students learn background knowledge and practical skills for investigating the biological basis for human disease. Using bioinformatics applications, they: (1) query biological and genetic sequence databases relevant to human health, (2) manipulate sequence data for alignment, recombination, selection, and phylogenetic analysis, (3) normalize microarray data and identify differentially expressed genes and biomarkers between patient groups, (4) annotate protein data and visualize protein structure, and (5) search the human genome and annotate genes relevant to human diseases. (W)

• MED 264. Principles of Biomedical Informatics (4 units) – This core class for the BMI track may be taken as an elective for the BISB track.

  Students are introduced to the fundamental principles of BMI and to the problems that define modern healthcare. The extent to which BMI can address healthcare problems is explored. Topics covered include structuring of data, computing with phenotypes, integration of molecular, image and other non-traditional data types into electronic medical records, clinical decision support systems, biomedical ontologies, data and communication standards, data aggregation, and knowledge discovery. (F)

• MED 265. Informatics in Clinical Environments (4 units)

  Students are introduced to the basics of healthcare systems through direct observation and classroom discussion. Students are introduced to medical language, disease processes, and health care practices to provide context prior to direct patient observation at primary, specialty, emergency, and inpatient sites in conjunction with clinical faculty affiliated with the training program. Students examine how clinicians use history-taking, physical examination and diagnostic testing to establish diagnoses and prognoses. Medical decision-making is introduced in the context of available informatics tools and clinical documentation and communication processes. Post-observation classroom discussions encourage students to think critically of the processes they observed and formulate hypotheses about how informatics solutions can modify the processes. Students who already have a clinical background can substitute this course. (W)

• MED 267. Modeling Clinical Data and Knowledge for Computation (4 units)

  This course describes existing methods for representing and communicating biomedical knowledge. The course describes existing health care standards and modeling principles required for implementing data standards, including biomedical ontologies, standardized terminologies, and knowledge resources. (S, offered alternate years.)

• MED 268. Statistics Concepts for Biomedical Research (4 units)

  This course introduces statistics methods for basic, pre-clinical, and clinical research. Topics include descriptive statistics, t-tests, ANOVA, linear and logistic regression, survival analysis, power and sample size, non-parametric methods, and factorial experiment design. Emphasis is on applications rather than theorems and proofs. Students will gain the ability to design efficient and informative basic research and clinical trials, to perform statistical analyses using the R statistics software, and to critique statistical results in published biomedical research. (F)
  Prerequisites: Medical or Graduate Student

• MED 276. Grant Proposal Writing Practicum (2 units)

  The focus of this course will be on grant writing and developing persuasive arguments. Previously submitted funded and non-funded grants will be used to illustrate revision and response to reviewers, as well as to provide source materials to perform mock study section reviews. This course will help students write their first grant proposal and understand the process of proposal scoring and reviewing. (S, offered alternate years.)

• MED 277. Introduction to Biomedical Natural Language Processing (4 units)

  Biomedical Natural Language Processing (BioNLP) is an essential tool in both biomedical research and clinical applications. Students taking this course will learn how to process free text data and their integration with other types of biomedical data with BioNLP. (F)

• BENG 226. Foundations of Bioengineering I: Tissue and Cell Properties (4 units)

  Modern development of biomechanics at an advanced mathematical level. Description of internal stresses and deformation in living tissues and fluids, thermodynamics. Mechanics of soft connective tissue, extracellular matrix, cells, membranes, and cytoskeleton. Mechanotransduction, migration, adhesion. Blood flow in microvessels. Biomechanical analysis of tissue injury.
  Recommended preparation: a previous background in biomechanics is strongly recommended prior to taking this course. Prerequisites: graduate standing or consent of instructor. (S)

• BENG 235. Molecular Imaging and Quantitation in Living Cells (4 units)

  This course will introduce quantitative fluorescence microscopy techniques for imaging, manipulating, and quantifying the spatiotemporal characteristics of molecular events in live cells. A laboratory component will be integrated with students organized into small teams for projects.
  Recommended preparation: basic optics at the level of ECE 181, introductory molecular and cellular biology at the level of BIMM 100 and BICD 110, respectively. Prerequisites: graduate standing or consent of instructor. (S)

• BGGN 214. Introduction to Q-Biology (4 units) – This may be applied to the BIO area elective requirement.

  The course goal is to discuss and work through examples where quantitative biology approaches were necessary to yield novel biological insights. Problems will be presented with a historic perspective to instill a philosophy for when, how, and why q-bio approaches are most effective. The course may also appeal to physics and engineering graduate students.
  Prerequisites: graduate standing or consent of instructor.

• BNFO 262/BIOM 262/BGGN 237/CMM 262. Quantitative Methods in Genetics (4 units)

  This advanced problem-oriented course will examine experimental design, laboratory methods, and quantitative analytical tools used in genetic and genomic research. Students will analyze supplied data using a variety of software packages.
  Prerequisites: BGGN 223, or BIOM 252 and BIOM 272, or equivalent.
  Note: This was approved as an elective by the BISB Curriculum Committee, but it's still undergoing approval by other campus entities as part of a larger set of changes, including a new course number BNFO 262. BISB students should enroll in it as BNFO 262, once that's available. BNFO 262 / BIOM 262 / BGGN 237 will be cross-listed. Previously also numbered CMM 262.

• MAE 263. Experimental Methods in Cell Mechanics (4 units)

  Methods to measure mechanical aspects of cellular nature and behavior such as intracellular rheology, intracellular force distribution and propagation, cell adhesion strength, generation of propulsive forces during locomotion, interaction with the extracellular matrix, and response to external mechanical stimuli.
  Prerequisites: MAE 209 or MAE 210A, and graduate standing.

• PHYS 273. Information Theory and Pattern Formation in Biological Systems (4 units)

  This course discusses how living systems acquire information on their environment and exploit it to generate structures and perform functions. Biological sensing of concentrations, reaction-diffusion equations, the Turing mechanism, and applications of information theory to cellular transduction pathways and animal behavior will be presented.
  Recommended preparation: familiarity with probabilities at the level of undergraduate statistical mechanics and major cellular processes; basic knowledge of information theory.

• PHYS 274. Stochastic Processes in Population Genetics (4 units)

  The course explores genetic diversity within biological populations. Genetics fundamentals, mutation/selection equilibria, speciation, Wright-Fisher model, Kimura’s neutral theory, Luria-Delbrück test, the coalescent theory, evolutionary games and statistical methods for quantifying genetic observables such as SNPs, copy number variations, etc., will be discussed. 
  Recommended preparation: familiarity with probabilities and PDEs at the undergraduate level; an introduction to basic evolutionary processes.

• PHYS 275. Biological Physics (4 units)

  The course teaches how a few fundamental models from statistical physics provide quantitative explanatory frameworks for many seemingly unrelated problems in biology. Case studies rotate from year to year and may include ion channel gating, cooperative binding, protein-DNA interaction, gene regulation, molecular motor dynamics, cytoskeletal assembly, biological electricity, population and evolutionary dynamics. May be coscheduled with PHYS 175. Students in PHYS 275 are expected to complete a report at the level of a research paper. 
  Recommended preparation: an introduction to statistical mechanics, at least at the level of PHYS 140A or CHEM 132.

• PHYS 277. Physics of the Cell (4 units)

  Exploration of the physics problems that must be solved by a living cell in order to survive. Theoretical ideas from nonequilibrium statistical mechanics and dynamical systems are used to establish the physical principles that underlie biological function, focusing on the organization and behavior of eukaryotic cells. Specific topics rotate from year to year and may include genome organization and dynamics, motility, sensing, and organelle interaction. May be coscheduled with PHYS 177. The graduate version will include a report at the level of a research paper.
  Recommended preparation: familiarity with statistical mechanics at the level of PHYS 140A or CHEM 132.