Course Descriptions (2009-10)

See Also BISB 2009-10

Curriculum Overview


Sample Schedules

BISB in UCSD Catalog


New policy enacted Fall 2009

Applies to students admitted Fall 2009 and later. Select other years from the menu on the left.

A list of projected course offerings for 2009-10 can be found here.

Core Requirements

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). Sequence and Structure Analysis—Methods and Applications (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). Functional Genomics (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).

Program Electives

Each student will select from four of the seven elective fields below. One must be from the biology field and one from the computer science field. For each elective, multiple course options currently available are listed.

Elective 1: Biochemistry

  • 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. Chemistry of Biological Macromolecules (4 units)

    (Conjoined with Chem. 113.) A discussion of the structural principles governing biological macromolecules, the techniques used in their study, and how their functional properties depend on three-dimensional structure. Chem. 213 students will be required to complete additional course work beyond that expected of students in Chem. 113.
    Prerequisites: elementary physical and organic chemistry. (May not be offered every year.)

  • CHEM 216. Chemistry of Enzyme Catalyzed Reactions (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.)

Elective 2: Molecular Genetics

  • 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)

Elective 3: Cell Biology

  • 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)

Elective 4: Algorithms

  • 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.

  • 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.

Elective 5: Machine Learning and Data Mining

  • 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.

Elective 6: Bioinformatics and Systems Biology

  • 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 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)

  • BENG 253. Biomedical Transport Phenomena (4 units)

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

  • 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.

Elective 7: Mathematics and Statistics

  • 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 with a grade of C– or better and Math. 20F with a grade of C– or better, or consent of instructor.

  • MATH 280A. Probability Theory (4 units)

    (MATH 280A is the Bioinformatics elective; this catalog description applies to the three-quarter Math Department course MATH 280A-B-C.)Probability measures; Borel fields; conditional probabilities, sums of independent random variables; limit theorems; zero-one laws; stochastic processes.
    Prerequisites: advanced calculus and consent of instructor. (F,W,S)

  • 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 probablilty 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 probablilty 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)