Course Descriptions (2010-11)

Each student must take the four courses below.

• Bioinformatics I (PHAR 201). Biological Data & Analysis (3 units)

  Bioinformatics is driven by the need to understand complex biological systems for which data are accumulating at exponential or near exponential rates. Such an understanding relies of the effective representation of these data and the ability to analyze these data. This is a broad topic and we focus on macromolecular structure data, which is suitably complex, to introduce the principles of formal data representation, reductionism, comparison, classification, visualization and biological inference. As such the course also serves as an introduction to Structural Bioinformatics. For details of what is covered in the course and more, refer to Structural Bioinformatics, 2nd Edition (2009), Editors Jenny Gu and Philip E. Bourne, Wiley & Sons.

• 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.

All students in years 1-2 must take the Colloquium every quarter it is offered (typically fall and winter).

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

  Weekly seminars by faculty and visiting bioinformaticists presenting their research.

All students must take one of the two ethics courses SOMI 226 or BIOM 219.

• SOMI 226. Scientific Ethics (1 unit)

  See BIOM 219 description.

• BIOM 219. Ethics in Scientific Research (1 unit)

  Overview of ethical issues in scientific research, conflicts of interest; national, statewide and campus issues and requirement; ethical issues in publications; authorship; retention of research records; tracing of research records; attribution; plagiarism; copyright considerations; primary, archival and meeting summary publications; ethical procedures and policies; NIH, NSF, California and UCSD; case studies and precedents in ethics.
  Prerequisite: consent of instructor.

• 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: BIPN 100 and 102 or consent of instructor. (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 213. Physical Chemistry of Biological Macromolecules (4 units)

  (Conjoined with Chem. 113.) A discussion of the physical principles governing biological macromolecular structure and function, and the physicochemical experiments used to probe their structure and function. Chem. 213 students will be required to complete an additional paper and/or exam beyond that expected of students in Chem. 113.
  Prerequisites: Chem. 140C or 140CH; and Chem. 127 or 131 (113); or graduate standing (213).

• 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.
  Prerequisite: graduate standing or consent of instructor. (May not be offered every year.)

• BICD 100. Genetics (4 units)

  An introduction to the principles of heredity in diploid organisms, fungi, bacteria, and viruses. Mendelian inheritance; population genetics; quantitative genetics; linkage; sex determination; meiotic behavior of chromosome aberrations, gene structure, regulation, and replication; genetic code. Three hours of lecture and one hour of recitation.
  Prerequisite: BILD 1 or the equivalent.

• BGGN 220. Graduate Molecular Biology (6 units)

  Provides a broad, advanced-level coverage of modern molecular biology for first-year graduate students. Topics include prokaryotic and eukaryotic gene structure and regulation, chromatin structure, DNA replication, translation, mechanisms of transcription, and an introduction to viruses. OPEN ONLY TO STUDENTS ENROLLED IN A GRADUATE DEGREE PROGRAM. (Letter grades only.) (F)

• BGGN 223. Graduate Genetics (6 units)

  Provides a broad and extensive advanced-level coverage of molecular and formal aspects of genetics for first-year graduate students. Topics covered include: bacterial genetics, recombination in prokaryotes and eukaryotes, mammalian somatic-cell genetics, developmental genetics, sex determination, dosage compensation, and immunogenetics. Extensive coverage of the use of model systems like Drosophila and C. elegans is included. General and specific aspects of cellular signalling mechanisms will be covered.
  Prerequisites: BGGN 220, 221 and 222. OPEN ONLY TO STUDENTS ENROLLED IN A GRADUATE DEGREE PROGRAM. (Letter grades only.) (S)

• BICD 110. Cell Biology (4 units)

  The structure and function of cells and cell organelles, cell growth and division, motility, cell differentiation and specialization. Three hours of lecture and one hour of recitation.
  Prerequisites: BIBC 100 or BIBC 102, and BICD 100.

• 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. Three hours of lecture and one hour of recitation.
  Prerequisites: upper-division standing; BICD 100; BIBC 100 or BIBC 102; BICD 110 strongly recommended, BIMM 100 strongly recommended.

• BGGN 222. Graduate Cell Biology (6 units)

  A coverage of modern cell biology for first year graduate students. There is an up-to-date discussion of topics such as: structure and function of membranes; ion pumps, ion channels, transmembrane signalling; receptor mediated endocytosis; protein targeting; the role of RER and Golgi apparatus; the biosynthesis of intracellular organelles in animal and plant cells; the cytoskeleton, motility, molecular motors, cell-cell interactions, mitosis; and the control of cell division. Also included are extensive coverage of cell signalling mechanisms and discussions on molecular approaches to cell biology.
  Prerequisites: BGGN 220 and 221. OPEN ONLY TO STUDENTS ENROLLED IN A GRADUATE DEGREE PROGRAM. (Letter grades only.) (W)

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

  The course will introduce students to a variety of signal transduction pathways and their function in the regulation of cellular processes. Special emphasis will be given to signaling cascades regulating immunological responses and alterations of signaling pathways during oncogenesis. (W)

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

  Design and analysis of efficient algorithms with emphasis of non-numerical algorithms such as sorting, searching, pattern matching, and graph and network algorithms. Measuring complexity of algorithms, time and storage. NP-complete problems. Credit not offered for both Math. 188 and CSE 101. Equivalent to Math 188.
  Prerequisites: CSE 12, CSE 21 or Math. 15B or Math. 100A or Math. 103A and CSE 100 or Math. 176. Majors only.

• 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.
  Prerequisite: 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.
  Prerequisite: 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.
  Prerequisite: CSE 202 preferred or consent of instructor.

• 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.
  Prerequisite: graduate standing or consent of instructor.

• CSE 250A. Artificial Intelligence: Search and Reasoning (4 units)

  Heuristic search algorithms including A*, constraint satisfaction algorithms including DPLL, randomized search, knowledge representation in first-order logic (FOL), resolution methods for reasoning in FOL, reasoning about action and planning, reasoning with Bayesian networks. CSE 101 recommended.
  Prerequisite: graduate standing in CSE or consent of instructor.

• CSE 250B. Artificial Intelligence: Learning (4 units)

  Classifier learning including linear separators, decision trees, and nearest neighbors. Generalization and overfitting; design of learning experiments; the PAC model. Possible topics include ensemble methods, boosting, kernel methods, online learning, and reinforcement learning.
  Prerequisite: graduate standing or consent of instructor.

• CSE 254. Statistical Learning (4 units)

  Learning algorithms based on statistics. Possible topics include minimum-variance unbiased estimators, maximum likelihood estimation, likelihood ratio tests, resampling methods, linear logistic regression, feature selection, regularization, dimensionality reduction, manifold detection. An upper-division undergraduate course on probability and statistics such as Math. 183 or 186, or any graduate course on statistics, pattern recognition, or machine learning is recommended.
  Prerequisite: graduate standing.

• 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.
  Prerequisite: BENG 230A or BIMM 100 or consent of instructor. (F)

• BENG 212. Systems Biology and Bioengineering II: Network Reconstruction (4 units)

  This course will cover the process of reconstructing complex biological reaction networks. Reconstruction of metabolic networks, regulatory networks and signaling networks. Bottom-up and top-down approaches. The use of collections of historical data. The principles underlying high-throughput experimental technologies and examples given on how this data is used for network reconstruction, consistency checking, and validation.
  Prerequisite: BENG 211 or consent of instructor. (W)

• BENG 227. Biomedical Transport Phenomena (4 units)

  Nonequilibrium thermodynamic analysis of transport phenomena. The osmotic effect. Diffusion and exchange in biological systems.
  Prerequisite: BENG 222 or consent of instructor. (W)

• 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 (4 units)

  Probability measures; Borel fields; conditional probabilities, sums of independent random variables; limit theorems; zero-one laws; stochastic processes. (This is a description of the full year Math 280A-B-C.)
  Prerequisites: advanced calculus and consent of instructor. (F)

• 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.

• PHYS 210A. Equilibrium Statistical Mechanics (4 units)

  Approach to equilibrium: BBGKY hierarchy; Boltzmann equation; H-theorem. Ensemble theory; thermodynamic potentials. Quantum statistics; Bose condensation. Interacting systems: Cluster expansion; phase transition via mean-field theory; the Ginzburg criterion.
  Prerequisites: Physics 140A–B, 152A, 200A–B, or equivalent; concurrent enrollment in Physics 212C. (S)

• 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 Focker-Planck equation; fluctuation-dissipation relation; multiplicative processes; dynamic field theory; Martin-Siggia-Rose formalism; dynamical scaling theory.
  Prerequisite: Physics 210A. (F)

• 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: grade of C– or better in BENG 122A or BENG 123; majors only or consent of instructor. (S)

• PHYS 239. Special Topics: Quantitative Molecular Biology (Terry Hwa) (1–3 units)

  Biology is undergoing a historical transformation from a component-centric focus on characterizing the parts to a system-level quest to understanding the rules of how a limited number of parts work together to perform complex functions. The new emphasis is significantly broadening the scope of biological physics, from the traditional focus on physical properties of molecular components to a fresh view of biological systems as information processing systems. Progress in this new emerging discipline requires a combination of expertise in biology, chemistry, engineering, and physics. In this course, I will try to present such an integrative approach, focusing primarily on gene regulation. The purpose of this course is two folds: One is to introduce to students of quantitative background a very important area of molecular biology which is ripe for quantitative study. The other is to introduce to students of biology background the power and limitation of theory/computation, demonstrating what it would take and what it may be like to make biology quantitative. See here for further information. Note: Course number PHYS 239 may be assigned to other special topics courses as well; only Quantitative Molecular Biology by Terry Hwa is approved for the Bioinformatics Graduate Program.

• BENG 213. Systems Biology and Bioengineering III: Building and Simulating Large-Scale In Silico Models (4 units)

  Mathematical models of reconstructed reaction networks and simulation of their emergent properties. Classical kinetic theory, stochastic simulation methods and constraints-based models. Methods that are scalable and integrate multiple cellular processes will be emphasized. Existing genome-scale models will be described and computations performed. Emphasis will be on studying the genotype-phenotype relationship in an in silico model driven fashion. Comparisons with phenotypic data will be emphasized.
  Prerequisite: BENG 212 or consent of instructor. (S)

• 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.

• MED 263. Bioinformatics Applications to Human Disease (3 units)

  Students will have background knowledge and practical skills for tackling the investigation of human disease using bioinformatics applications. Specifically, students will be able to: (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. (F)

• MED 264. Principles of Biomedical Informatics (3 units)

  Students will understand the main challenges of computing with phenotypes, how to integrate molecular data into electronic medical records and clinical trial records. They will get an introduction to medical decision making, consisting of introduction to decision theory, clinical decision support systems, clinical predictive models, as well as biomedical ontologies, standards, and data repositories. Students will know how to structure and query clinical data sets, and how the most commonly used privacy technologies can be used to avoid confidentiality breaches in de-identified disclosed datasets. (S)

• MED 265. Healthcare Systems: A Quantitative Perspective (3 units)

  We will introduce students to the basics of healthcare systems through a combination of direct experience and didactics. The direct experience comes from shadowing (with staff trained in direct observations) of patients through their encounter at various health care settings. This provides first and second year medical students as well as select graduate students with direct exposure to clinical environments from a new perspective: they are not learning specifics of history taking or physical exam, but rather understanding the overall system in which clinicians work. The didactics will include medical decision analysis, health care quality measures, overview of medical applications of process control, operations research, modeling and simulation techniques. The types of health care sites include Primary, Specialty, Emergency and Urgent Care sites, with the types of health encounters including well visits (adult and pediatric), follow-up visits (for chronic conditions), consultative and urgent/emergent care. They will also include inpatient care, ICUs, and ORs. Shadow sites will include UCSD locations that have IRB approval for patient observations by student learners. Our objective is to introduce students early in their career to the environments in which medicine is practiced in a way that cultivates critical thinking in terms of how healthcare processes can be optimized using quantitative methods. (W)

• MED 266. Biomedical Decision Support (3 units)

  The course is to serve as an introduction to data analysis using machine learning techniques in biomedicine. The objective is that a student after completing the course will be able to formulate and execute a study based on secondary data use, and be able to critically read biomedical informatics literature. In addition the student should be aware of issues regarding ethics in human subject research, in particular informed consent and privacy technology. In order to meet this goal, the course will discuss fundamentals of machine learning including probability theory, statistics, and symbolic reasoning. Further, selected machine learning approaches including supervised as well as unsupervised techniques are presented. The machine learning modeling approaches include artificial neural networks, regression and classification trees, support vector machines, fuzzy rule systems, k-means, hierarchical and EM clustering, and co-clustering. Complementing and supporting technologies and approaches for data acquisition and curation, visualization, and evaluation will be presented. Emphasis will be placed on presenting the above in a relevant biomedical context. In particular, a portion of the course is devoted to relevant aspects of human subjects research and information security. These are in particular aspects that impact secondary data use such as privacy, confidentiality, and anonymitiy, as these have real impact on applications of and research on machine learning methods in biomedicine.