Course Descriptions (2015-16)
See here for a list of projected course offerings for 2015-16.
Each student in the Biomedical Informatics Track must take the four courses below.
(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)
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)
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.
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.
Prerequisites: Math 283.
All required Core and Elective courses for the degree must be taken for a letter grade. Students must obtain a “B” or better in courses taken for the degree. If you receive a “B-” or lower in a Core or Elective class, or you take it on S/U grading, it does not fulfill your requirements. Please see the Grades FAQ if you have any grades of “B-” or lower or “U”.
All students in years 1-2 must take the Colloquium for their track in fall, winter, and spring quarters.
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.
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.
Prerequisites: Bioinformatics and Systems Biology Graduate Students only. (S/U grades only.)
All students must take one of the two ethics courses, 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.
Special registration procedure: Students must register for the ethics course both via TritonLink as well as via the registration form on ethics.ucsd.edu. The course is taken by many UCSD employees (not just students), so the Ethics Program uses its own registration form. Registration on TritonLink is necessary for students to receive course credit.
SOMI 226. Scientific Ethics (1 unit)
See BIOM 219 description.
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 UC San Diego; case studies and precedents in ethics.
Prerequisites: consent of instructor.
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 (4–8 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 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. Students should enroll in BNFO 500 (Teaching Experience) or an equivalent course code in another department, during each quarter in which they are a teaching assistant. 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.
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 Ph.D. candidate must complete two academic quarters of experience for S/U grade only. May be taken for credit four times. (F,W,S)
Note: The department in which the teaching assistantship is held may require use of their corresponding course number instead of BNFO 500, e.g., BENG 501, BGGN 500, CHEM 500, CSE 500, MATH 500, etc.
Prerequisites: Graduate standing and consent of instructor (department stamp). (S/U grades only.)
Each student in the Biomedical Informatics track will select 16 units of Elective Courses from four distinct Elective Fields (BIO, CS, SB, BMI) delineated below, with at least 4 units from the CS series and 8 units from the BMI series. BMI students without a healthcare background should take MED 265 as one of their electives.
It is the general policy of the program to be as adaptable as possible to the needs of the individual student. The curriculum committee is receptive to students petitioning to satisfy an Elective requirement by taking a course not listed among the Electives.
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.
(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.
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.
Prerequisites: BILD 1.
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 signaling 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.
Prerequisites: BIBC 100 or BIBC 102.
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.
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 signaling; 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 signaling 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)
CHEM 221 / BGGN 230. Signal Transduction (4 units)
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.)
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. 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.
CSE 200. Computability and Complexity (4 units)
CSE 200. Computability and Complexity (4)
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.
(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.
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.
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.
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. Recommended preparation: CSE 103 or similar course.
Prerequisites: graduate standing or consent of instructor.
(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.
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 200A–B. Corequisites: Physics 212C. (S)
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: Physics 210A. (F)
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)
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.
Prerequisites: BENG 211 or consent of instructor. (W)
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)
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)
Qualitative, analytical and computational mathematical modeling techniques applied to regulatory networks and signaling networks. Stability, bifurcations, oscillations, multistability, hysteresis, multiple time-scales, 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 Molecular Biology (4 units)
A quantitative approach to gene regulation, including transcriptional and posttranscriptional control of gene expression, as well as feedback and stochastic effects in genetic circuits. These topics will be integrated into the control of bacterial growth and metabolism. Recommended preparation: an introductory course in biology is helpful but not necessary. (W)
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.
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)
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)
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. (F, offered alternate years.)
Introduction of statistics methods including descriptive statistics, t-tests, ANOVA, linear and logistic regression, survival analysis, power and sample size, non-parametric methods, and factorial experiment design. Students perform statistical analysis using R statistics software and critique statistical resuls in published research. (F)
Prerequisites: Medical or Graduate Student
Students learn about modeling knowledge to facilitate improved decision-making. The course includes review and discussion of case studies of specific health-related decision-support systems. Through discussions, assignments, and group projects, students learn the analytic techniques behind decision support systems as well as topics within decision-making under uncertainty, decision analysis, and evaluation of decision support systems. (W)
Students learn writing techniques for communicating scientific and engineering knowledge to audiences ranging from specialist to general communities. Starting with a review of grammar and sentence structure, the course will lead into different forms of science and engineering writing, including popular pieces, blogs, specifications, reviews, and research papers. Additionally, techniques to improve scientific presentation skills in oral form will be offered. (W, offered alternate years.)
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.)
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.
Note: This is a proposed new course currently undergoing the approval process. It is expected to be offered in 2015-16 if it is approved in time.