AMS-534 / BGE-534: Introduction to Systems Biology (Graduate, Spring semester, 3 credits)
A detailed introduction to essential concepts and computational skills for doing research in Systems Biology. The class will be centered upon two key programming languages: Matlab for modeling applications and the R language for statistical analysis and sequence manipulation. Examples will come from a broad range of biological applications ranging from theoretical population genetics, metabolic and gene network dynamics to analysis of high-throughput data. No prior knowledge of biology or mathematical/computational techniques is required.
AMS534/BGE534 Course outline (Spring 2013)
AMS 534 Lectures Spring 2012
AMS-333: Mathematical Biology (Undergraduate, 3 credits)
The goal of this course is to introduce students to the field of mathematical and computational biology, with an emphasis on its interdisciplinary nature. The course aims to draw students of mixed backgrounds (including both biology and applied mathematics majors) and to foster interdisciplinary interactions. Topics are drawn from a wide-range of problems in biology, including the modeling of populations, the dynamics of signal transduction and gene-regulatory networks. A computer laboratory component allows students to apply their knowledge to real-world problems. Pre-requisites: All students taking the course should have taken a two-semester sequence in calculus (differentiation and integration), such as MAT 131/132 or AMS 160/161, as well as an introductory course in molecular biology (BIO 202). Additionally, students should have deeper background in either biology or math. Thus, upper division standing (U3 or U4) is required. Students with experience in both quantitative methods and biology, but lacking the specific prerequisites, may request permission to enroll from the instructor.
AMS-537/PHY-599/CHE-599: Biological dynamics and networks (Graduate, 3 credits)
This course will provide a solid foundation in key theoretical concepts for the study of dynamics in biological systems and networks at different scales ranging from the molecular level to metabolic and gene regulatory networks. Topics will include: Statistical thermodynamics, biochemical networks and enzyme kinetics, Structure and evolution of networks, Modeling metabolic and gene regulatory networks.