Instructor: Vassily Hatzimanikatis
Office address: E136, E250 2145 Sheridan Rd., Evanston Campus 3120
Phone: 847-491-5357
E-mail: vassily@northwestern.edu

Time: MW 4:00 - 6:00 PM, M164

COURSE DESCRIPTION:
The course will introduce the students to the principles and applications of computational biology methods for the analysis and study of complex biological systems. Emphasis will be placed (i) on the technologies used for the quantitative, cell-wide monitoring of cellular processes and the associated mathematical and computational frameworks for the analysis of this information, and (ii) the application of systems engineering principles and methodologies on the study of complex, large-scale biological systems.

PREREQUISITES:
Basic knowledge of biology, linear algebra, and differential equations. The composition of the working groups will allow members of the group with partial knowledge in these areas to contribute according to their background.

TEACHING METHOD:
Lectures, article reading and discussion, computer laboratory.

EVALUATION METHOD:
Homework and project (Written report and oral presentation)

READING:

• Post-Genome Informatics, by Minoru Kanehisa;
• Case Studies in Mathematical Modeling in Ecology, Physiology, and Cell Biology, by Hans G. Othmer, Mark Lewis, and Fred Adler;
• Computational Modeling of Genetic and Biochemical Networks, by James M. Bower and Hamid Bolouri.
• Foundations of System Biology, by Hiraoki Kitano.
• Modeling Differentil Equations in Biology, by Clifford H. Taubes
• Biological Kinetics, edited by Lee A. Segel

Topics:
The course will include the following topics:

• Methods and technologies for monitoring cell-wide gene expression
• Mathematical and computational analysis of gene expression data
• Methods and technologies for monitoring cell-wide protein expression
• Mathematical and computational analysis of protein expression data
• Methods and technologies for identification of protein-protein interaction
• Mathematical and computational analysis of protein-protein interaction data
• Methods and technologies for identification of DNA-protein interaction
• Mathematical and computational analysis of DNA-protein interaction data
• Genetic networks
  • Mathematical methods for the identification of genetic regulatory networks
  • Modeling and Simulation of gene expression networks
• Translation networks
  • Modeling and Simulation of protein expression networks
• Methods and technologies for monitoring metabolic reaction networks
• Mathematical and computational analysis of metabolic reaction networks

The course will also offer computer laboratory.