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Online Course

Computational Systems Biology

Indian Institute of Technology Madras and NPTEL via Swayam

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Overview

Every living cell is the result beautifully concerted interplay of metabolic, signalling and regulatory networks. Systems biology has heralded a systematic quantitative approach to study these complex networks, to understand, predict and manipulate biological systems. Systems biology has had a positive impact on metabolic engineering as well as the pharmaceutical industry. This course seeks to introduce key concepts of mathematical modelling, in the context of different types biological networks. The course will cover important concepts from network biology, modelling of dynamic systems and parameter estimation, as well as constraint-based metabolic modelling. Finally, we will also touch upon some of the cutting-edge topics in the field. The course has a significant hands-on component, emphasizing various software tools and computational methods for systems biology.

INTENDED AUDIENCE : Interested learners PREREQUISITES : Basic knowledge of a high-level programming language (preferably MATLAB) INDUSTRY SUPPORT : Bioprocess industries / Computational Biology Companies, e.g. MedGenome, Vantage Research

Syllabus

COURSE LAYOUT Week 1 : Introduction to Mathematical Modelling Week 2 : Introduction to Static Networks Week 3 : Network Biology and Applications Week 4 : Reconstruction of Biological Networks Week 5 : Dynamic Modelling of Biological Systems: Introduction, Solving ODEs & Parameter Estimation Week 6 : Evolutionary Algorithms, Guest Lectures on Modelling in Drug Development Week 7 : Constraint-based approaches to Modelling Metabolic Networks Week 8 : Perturbations to Metabolic Networks Week 9 : Elementary Modes, Applications of Constraint-based Modelling Week 10: Constraint-based Modelling Recap, 13C Metabolic Flux Analysis Week 11: Modelling Regulation, Host-pathogen interactions, Robustness of Biological Systems Week 12: Advanced topics: Robustness and Evolvability, Introduction to Synthetic Biology, Perspectives & Challenges

Taught by

Prof. Karthik Raman

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