To enable the students to “understand” biological data, to represent, and analyze various datasets from a network perspective, to encourage network thinking applied to problems across disciplines, to understand various network models used to model real-world networks, to apply network analytics techniques to understand biological networks, to implement basic network analysis algorithms in Python, to learn different AI/ML problem formulations for biological data, and to apply AI/ML techniques for analysis of biological data using Python.
12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam.
For details of standard course structure and assessments, visit
Academics
page.
WEEK 1
Introduction to Biological Big Data. Information Flow in Biological Systems.
WEEK 2
Omics datasets: Various flavours of big biological datasets (genomic, transcriptomic, proteomic, metabolomic, etc.).
WEEK 3
Introduction to Graph theory. History. Types of graphs. Representing biological networks.
WEEK 4
Network structure: Key parameters, measures of centrality
WEEK 5
Key Network Models: Erdos-Renyi, Watts-Strogatz (small-world) and Barabasi-Albert (power-law models)
WEEK 6
Network clustering/community detection. Identifying motifs in networks. Studying network perturbations.
WEEK 7
Applications of network biology: Predicting drug targets, predicting drug molecules, synthesis of new molecules (chemoinformatics)
WEEK 8
Applications of network biology: Epidemiology, Centrality-lethality hypothesis.
WEEK 9
AI & ML for Biological Data Analysis. Introduction to AI & ML tasks in biological networks.
WEEK 10
Biological network reconstruction from omics and literature data
WEEK 11
Property prediction using network data. Node classification and link prediction.
WEEK 12
Analysis of heterogeneous and multi-layer/multiplex networks. Future Perspectives.
Assistant Professor,
Department of Biotechnology,
IIT Madras
Dr Nirav P Bhatt earned his Bachelor in Chemical Engineering from The M S University of Baroda, Masters in Chemical Engineering from IIT Madras, and Docteur es Science (DSc) from EPFL, Switzerland. Currently, He is Assistant Professor with Bio Tech Department in IIT Madras.
Assistant Professor,
Department of Biotechnology,
IIT Madras
Dr. Karthik Raman is an Assistant Professor at the Department of Biotechnology, Indian Institute of Technology Madras since April 2011. His research interests are in the areas of metabolic network analysis, with applications in metabolic engineering and drug target identification. He received his Ph.D. in 2009 from the lab of Prof. Nagasuma Chandra at the Supercomputer Education and Research Centre at the Indian Institute of Science, Bangalore. His Ph.D. thesis involved the computational analysis of metabolic networks and protein-protein interaction networks in Mycobacterium tuberculosis, for the prediction of potential drug targets. Following his Ph.D., Karthik was a post-doctoral researcher at the lab of Prof. Dr. Andreas Wagner, at the University of Zürich, Switzerland. Karthik’s post-doctoral research was involved the analysis of signalling circuits in yeast as well as synthetic logic circuits, for their robustness and evolvability.
Associate Professor,
Department of Biotechnology,
IIT Madras
Dr. Himanshu Sinha is an Associate Professor at the Indian Institute of Technology Madras in the Department of Biotechnology. He is also a Co-ordinator for the Initiative for Biological Systems Engineering. His research Area is Complex Genetics, Systems Biology.He received his Ph.D. in Department of Plant Science at the [Downing College, Cambridge University]. He carried out his postdoctorate at the Center for Microbial Pathogenesis, Department of Molecular Biology and Microbiology, Duke University Medical Center, Durham, NC USA. He was a Senior Postdoc at Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany. Following this he was a Reader at Department of Biological Sciences, TIFR, Mumbai, India.
Please use only the above methods for program queries.
Response time: 3 working days. During peak periods, Google
Meet links will be shared. Call wait times may be longer.
