Course Descriptions (2018-19)

Each student in the Bioinformatics and Systems Biology Track must take four core courses. The three on this first list are required for all students:

• 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.

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.

• 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.

• 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/MED 283. Network Biology and Biomedicine (4 units)

  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.

All students in years 1-2 must take the Colloquium for their track in fall, winter, and spring quarters.

• 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.

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.

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. 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.

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 six times.
  Prerequisites: Bioinformatics and Systems Biology 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 (BENG 501, BGGN 500, CHEM 500, CSE 500, MATH 500, etc.), 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 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.
  Prerequisites: BILD 1.

BGGN 220DEF are three consecutive 3.3 week classes, usually taken together, but that's not required.

• BGGN 220D. Chromatin Structure and Transcriptional Regulation (2 units)

  The course covers chromatin structure and dynamics as well as the regulation of transcription initiation by RNA polymerase II. The format includes lectures and discussion of selected papers.

• BGGN 220E. Post-Transcriptional Gene Regulation (2 units)

  The course covers mechanisms of gene regulation at the post-transcriptional level, including RNA processing, translation, and mRNA turnover. The format includes lectures and discussion of selected papers.

• BGGN 220F. Shaping Cellular Function through Post-Translational Regulation (2 units)

  The course will cover post-translational control mechanisms governing cellular activity. The course will traverse molecular and systems-level approaches aimed at understanding the governing principles of post-translation regulation and the consequences of improper regulation. The format includes lectures and discussion of selected papers.

• 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; open only to students enrolled in a doctoral degree program. 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.

• 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.
  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)

  Provides 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; open only to students enrolled in a doctoral degree program. Letter grades only.

• 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.)

• 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. Credit not offered for both Math 188 and CSE 101. Equivalent to Math 188.
  Prerequisites: CSE 100 or Math 176; 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.

• 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.

• 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 250B. Principles of Artificial Intelligence: Learning Algorithms (4 units)

  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.

• 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.

• 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.

• 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 284. Survival 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.
  Prerequisites: Math 282A or consent of instructor.

• PHYS 210A. Equilibrium Statistical Mechanics (5 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 200A-B. Corequisites: Physics 212C.

• 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: Physics 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: 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.
  Prerequisites: BENG 211 or consent of instructor. (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/MED 283. Network Biology and Biomedicine (4 units)

  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 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.

• 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 269. Clinical Decision Support Systems at the Point of Care (4 units)

  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. (S)

• MED 273. Communicating Biomedical Informatics (4 units)

  Students learn communication strategies for writing and presenting scientific and engineering knowledge to audiences ranging from specialist to general communities. The course will help equip students with a wide range of core communication skills necessary as a researcher, including writing a review to a systematic review, writing popular pieces, research papers, and grant proposals. Further, the course will cover speaking and design techniques to improve research presentation. (S)

• 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 Biomechanics (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. Students that have taken BENG 222 cannot take BENG 226 for credit.
  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.

• 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 or MAE 131A, or consent of instructor.

• 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. Fundamentals of Biological Physics (4 units)

  This course teaches how quantitative models derived from statistical physics can be used to build quantitative, intuitive understanding of biological phenomena. Case studies include ion channels, cooperative binding, gene regulation, protein folding, molecular motor dynamics, cytoskeletal assembly, and biological electricity.
  Recommended preparation: an introduction to statistical mechanics, at least at the level of Physics 140A or Chemistry 132.

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

  The use of dynamic systems and nonequilibrium statistical mechanics to understand the biological cell. Topics chosen from chemotaxis as a model system, signal transduction networks and cellular information processing, mechanics of the membrane, cytoskeletal dynamics, nonlinear Calcium waves. The graduate version will include a report at the level of a research paper. May be scheduled with Physics 177.
  Recommended preparation: an introductory course in biology is helpful but not necessary.