Note, this is one of many sections of ITS 102, the freshman seminar, each of which is on a separate topic. The class meets once a week for 55 minutes, as is limited to a 20 student enrollement.
Biology has traditionally been largely a descriptive science, differing in many ways from the quantitative nature of physics and engineering. However, great advances in biology, including the successful sequencing of the human genome, have led us to the threshold of a new approach to biology. In this "new" biology, computer models of complex biological systems are combined with advanced biological experiments to create an unprecedented level of understanding. As quantitative models are defined, we also become able to apply the principles of engineering to design new systems. In this course we will discuss some of the key successes that have been made in the recent past, as well as the major challenges that are open to be solved, in fields including: protein design and engineering, systems biology, biological computing, and synthetic biology. We will also discuss the challenges of bridging traditionally separate fields, and how to become effectively "multidisciplinary".
Pre-requisites: The course is open to students admitted to the ITS Undergraduate College.
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, and the simulation of protein structure and dynamics. 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.
Note, this course is part of the core preparation for the Ph.D. qualifying exam in Applied Math & Statistics.
Review of techniques of multivariate calculus, convergence and limits, matrix analysis, vector space basics, and Lagrange multipliers. This course is a review of topics in linear algebra and advanced calculus that students should have encountered in their undergraduate studies. The methods and concepts covered are essential for more advanced work in applied mathematics.
Pre-requisites: A course in linear algebra and in multivariate calculus.
This is a two semester course in which students spend at least eight weeks in each of three different laboratories actively participating in the research of participating Computational Biology faculty. Participants will additionally attend weekly Journal Club meetings, where they will engage in active discussion of papers taken from the current scientific literature. Note, the laboratory rotations are required of first year Ph.D. students from the Computational Biology track in AMS; PhD students from other tracks in AMS are additionally welcome on engage in rotations with participating faculty. Masters students may sign up for the Journal Club, but do not partake in rotations.
Pre-requisites: None.
This class will survey many of the key techniques used in diverse aspects of computational biology. We will focus on how to successfully formulate a statement of the problem to be solved, and how that formulation can guide in selecting the most suitable computational approach. A set of problems from a diverse range of problems in biology will be used as examples. Note: Informatic methods for genomic analysis (such as data mining and analysis of nucleic acid and protein sequences) will not be covered. These topics are covered thoroughly in CSE 549.
Pre-requisites: None. Some familiarity with basic concepts in linear algebra and calculus will be needed; extra help on these topics will be available. See the instructor for more information.