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Course Criteria
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3.00 Credits
PQ: BIOS 20200. This course meets one of the requirements of the microbiology specialization. Phage are the most abundant and fastest growing biological entities, and they are involved in many natural microbiological processes. This course examines a series of bacteriophage that have been instrumental in our understanding of genetics and molecular biology, with an emphasis on their properties and the methods for which they are used in current and potential biological studies and in biotechnology. M. Casadaban. Spring.
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3.00 Credits
PQ: A Fundamentals Sequence (BIOS 20180s or 20190s, or AP 5 sequence). Suggested for students who are planning postgraduate study in medicine or in biological sciences. This course focuses on translational research in biomedical sciences with an emphasis on cancer research. In the scientific world, translation is the application of fundamental discoveries in basic science to clinical medicine, with the goal of developing new treatments for debilitating diseases. We use specific examples to cover the relationship between basic and translational research, the process of drug discovery, preclinical development and clinical testing, and choice of animal models for translational research. H. R. Xing. Spring.
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3.00 Credits
PQ: BIOS 25256. This course presents biological, technical, ethical, and economic issues associated with organ transplantation. We sharply focus the immunologic knowledge from BIOS 25256 onto the biologic barriers to organ acceptance and the ultimate goal of immunologic tolerance. We also address principles of organ preservation and the mechanisms of ischemia/reperfusion injury. The technical aspects and physiology of organ transplantation (i.e., kidney, liver, heart, lung, pancreas, islet, intestinal) are covered. The social, economic, and ethical issues raised in transplantation (i.e., allografts, xenografts, living donation) are also discussed. This course is offered in alternate years. A. Chong. Winter. Not offered 2009 C10; will be offered 201 0 -11.
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3.00 Credits
PQ: MATH 15300 or equivalent. This course focuses on ordinary differential equations as models for biological processes changing with time. The emphasis is on dynamical systems theory, stability analysis, and different phase portraits, including limit cycles and chaos. Linear algebra concepts are introduced and developed. Numerous biological models are analyzed, and labs introduce numerical methods in MATLAB. D. Kondrashov. Autumn. L.
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3.00 Credits
PQ: BIOS 26210. This course continues the study of time-dependent biological processes and introduces discrete-time systems, studying period-doubling, and onset of chaos. Fourier transform methods are used to analyze temporal and spatial variation, leading to the study of partial differential equations. The diffusion, convection, and reaction-diffusion equations are all used to model biological systems. Finally, common optimization methods are introduced. In labs, computational techniques are used to analyze sample data and study models. D. Kondrashov. Winter. L.
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3.00 Credits
This course covers basic mathematical probability, probability distributions, correlation, principal and independent component analysis, and stochastic processes. Stochastic behavior is ubiquitous at all levels of biology, and examples range from electrophysiology to bioinformatics. In labs, students use stochastic models to model and analyze these systems. D. Kondrashov. Spring. L.
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3.00 Credits
PQ: BIOS 20181-20183 or 20191-20193, and 20200. Cells in the body communicate with each other by a variety of extracellular signals (e.g., hormones, neurotransmitters) and processes such as vision and olfaction, as well as diseases such as cancer, all involve aspects of such signaling processes. The subject matter of this course considers molecular mechanism of the wide variety of intracellular mechanisms that, when activated, change cell behavior. Both general and specific aspects of intracellular signaling are covered, with an emphasis on the structural basis of cell signaling. W.-J. Tang. Spring.
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3.00 Credits
PQ: BIOS 20182 or 20192, or MATH 15100, or consent of instructor. This course introduces the concepts, purposes, tools, skills, and resources of bioinformatics. It includes a description of GenBank and other sequence databases; genetic and physical mapping databases; and structure databases. It also explains definitions such as homology, similarity, and gene families. Other topics include the basic principles and computational skills of comparative and phylogenetic analyses of DNA and protein sequence data, computer skills in database searching and information retrieval, predictive methods using DNA sequences, predictive methods using protein sequences, and comparative genomics. W. Li. Spring.
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3.00 Credits
PQ: Consent of instructor. Advanced standing and background in cell biology, genetics, protein chemistry, mathematics, physics, and chemistry. Among the most challenging concepts in biology involve understanding the fundamental mechanisms that underlie self-assembly and complexity in systems that vary from simple multi-protein molecular machines to cellular systems (e.g., signal transduction) to multi-cellular systems (e.g., immune system) or even whole organism (e.g., development). Systems biology aims at a holistic understanding of the dynamics of biological systems by combining approaches from system sciences, life sciences, and information sciences. Fundamental concepts and cutting-edge approaches are introduced at the interface of the biological and physical sciences. P. Nash. Spring.
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3.00 Credits
PQ: STAT 23400 or Statistics in the Biomath Sequence. This lecture course explores the technologies that enable high-throughput collection of genomic-scale data, including sequencing, genotyping, gene expression profiling, assays of copy number variation, protein expression and protein-protein interaction. We also cover study design and statistical analysis of large data sets, as well as how data from different sources can be used to understand regulatory networks (i.e., systems). Statistical tools introduced include linear models, likelihood-based inference, supervised and unsupervised learning techniques, methods for assessing quality of data, hidden Markov models, and controlling for false discovery rates in large data sets. Readings are drawn from the primary literature. Y. Gilad, D. Nicolae, R. Jones. Spring.
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