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Course Criteria
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1.00 Credits
This course covers topics spanning the technological and computational areas of modern gene expression analysis, developing computational methods in important and current problems of clinical and physiological phenotyping, including custom computation and algorithmic development. Prerequisites: Statistics 213, or 214 or 216. Instructor: Staff
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1.00 - 3.00 Credits
Hands-on experience on using and developing advanced technology platforms for genomics and proteomics research. Experiments may include nucleic acid amplification and quantification, lab-on-chip, bimolecular separation and detection, DNA sequencing, SNP genotyping, microarrays, and synthetic biology techniques. Laboratory exercises and designing projects are combined with lectures and literature reviews. Prior knowledge in molecular biology and biochemistry is required. Instructor consent required. Instructor: Tian
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3.00 Credits
Course will integrate empirical and computational perspectives on immunology and host defense. Students are expected to have significant preparation in either biomedicine or a quantitative science. Topics covered are intended to provide an entree into the use of computational methods for research and practice in immunology and infectious disease, from basic science to medical applications. Consent of instructor required. Instructors: Kepler and Cowell
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1.00 Credits
This course is designed to train and carry out a quantatative differential expression proteomics experiment. The course materials will provide an overview of the fundamentals of protein chemistry and mass spectrometry, as well as detailed information on LC/MS/MS methods for both open platform ('omic) proteomics experiments for biomarker discovery, and targeted LC/MS/MS methods (Mass Spec "Westerns") for biomarker verification/validation. Emphasis will be placed QC metrics and commercial and open source bioinformatics tools for bioinformatic data interpretation. Instructor: Moseley
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2.00 Credits
Advances in the biological sciences are often the result of multi-disciplinary teams of investigators. Successful collaboration requires effective communication, which in turn is facilitated by the construction of a hierarchical "concept map" that spans both disciplines and can be used as the basis of new shared insights and analysis. This course will use important publications that resulted from the successful alignment of biological and computational investigations to help students develop such concept maps and use them to enhance their cross-disciplinary communication. At each session, two faculty representing the appropriate disciplines will be present. Instructor: Staff
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3.00 Credits
Research seminar on mathematical methods for modeling biological systems. Exact content based on research interests of students. Review methods of differential equations and probability. Discuss use of mathematical techniques in development of models in biology. Student presentations and class discussions on individual research projects. Presentation of a substantial individual modeling project to be agreed upon during the first weeks of the course. May serve as capstone course for MBS certificate. Not open to students who have had MBS 200S. Prerequisites: Mathematics 107 or 131 or consent of instructor
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3.00 Credits
Data-Intensive Computing Systems. Principles and techniques for making intelligent use of the massive amounts of data being generated in commerce, industry, science, and society. Topics include indexing, query processing, and optimization in large databases, data mining and warehousing, new abstractions and algorithms for parallel and distributed data processing, fault-tolerant and self-tuning data management for cloud computing, and information retrieval and extraction for the Web. Prerequisites: Computer Science 116 or an introductory database course or consent of instructor. Instructor: Babu or J. Yang
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3.00 Credits
Models of computation and lower-bound techniques; storing and manipulating orthogonal objects; orthogonal and simplex range searching, convex hulls, planar point location, proximity problems, arrangements, linear programming and parametric search technique, probabilistic and incremental algorithms. Prerequisite: Computer Science 230 or equivalent. Instructor: Agarwal or Edelsbrunner
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3.00 Credits
Mechanisms, probability models and statistical analysis in examples of classical and population genetics, aimed at covering the basic quantitative concepts and tools for biological scientists. This module will serve as a primer in basic statistics for genomics, also involving computing and computation using standard languages. Instructor: Staff
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3.00 Credits
Introduction to theory and computation of macromolecular structure. Principles of biopolymer structure: computer representations and database search; molecular dynamics and Monte Carlo simulation; statistical mechanics of protein folding; RNA and protein structure prediction (secondary structure, threading, homology modeling); computer-aided drug design; proteomics; statistical tools (neural networks, HMMs, SVMs). Prerequisites: basic knowledge algorithmic design (Computational Biology and Bioinfomatics 230 or equivalent), probability and statistics (Statistics 213 and 244 or equivalent), molecular biology (Biology 118 or equivalent), and computer programming. Alternatively, consent of instructor. Instructor: Schmidler
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