Course Code: CS572
Course Name: Computational Systems Biology
Prerequisites: NIL
Syllabus: Cellular components interact with each other to carry out their specific functions. One way to understand cellular processes at system level is to model them as networks of interactions. The Objective of the course is to understand underlying computational challenges posed by such models. Algebraic graph theory, machine learning and statistics have been widely used for inference and analysis of such networks. This course aims to discuss state-of-the-art algorithms, demonstrate their use in understanding molecular mechanism at systems levels along with limitations.
The course would not require any biological background and all relevant biological concepts would be introduced in the course.
Introduction: Molecular Cell Biology, Systems Biology, Networks; Biological Networks: Transcriptional Regulatory Networks, Protein-protein interaction networks, Metabolic Networks, Genetic Networks, Disease Networks; Networks Measures; Inference of Networks: Graphical Models, Kernel based method, Regression based method, Information Theory based models; Network Analysis: Generic organizing principles of biological networks; Network integration; Application of networks in disease diagnosis and drug target prediction. Application of model verification and formal methods.
Texts: 1. Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, Hans Lehrach, and Ralf Herwig. Systems Biology: A Textbook, Wiley-Blackwell, 2009.
References: 1. Uri Alon. An Introduction to Systems Biology - Design Principles of Biological Circuits, CRC Press, 2007.
2. Mark E. J. Newman. Networks: An Introduction, Mark Newman, Oxford University Press, 2010.
3. Bruce Alberts, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, Peter Walter. Molecular Biology of the Cell, Garland Science (Taylor & Francis Group), 2007.