Talk Title: An introduction of molecular network analysis



Networks with molecular components represented as nodes and their direct or indirect interactions represented as links provide a robust (mathematical) framework to interrogate intracellular molecular networks. Different types of molecular networks including protein-protein interaction (PPI), signal transduction, metabolic (enzyme-substrate), and gene regulatory networks have been studied using network models. In the first half of my talk, taking PPI networks as an example I will systematically walk through the basics of analysing molecular networks, covering their topological properties and theoretical models, interaction scoring (denoising), network visualization, and network clustering. In the second half of my talk, I will demonstrate an application of network analysis using the example of our EU FP7 project called PRIMES (conducted within the Lynn EMBL-Australia group) where we analysed “rewiring” of the EGFR signalling network resulting from oncogenic KRAS activity in HCT116 colorectal cancer cells. The talk is intended as a basic introduction to molecular network analysis, to help students understand and appreciate ‘systems level’ analysis in biology.

Sriganesh Srihari


Dr Sriganesh Srihari, Senior Research Fellow, South Australian Health and Medical Research Institute

Sriganesh Srihari is a Senior Research Fellow with the Lynn EMBL-Australia group at the South Australian Health and Medical Research Institute (SAHMRI), and a Senior Research Officer with the Ragan Group at The University of Queensland (UQ), Australia. He has a background in computer science (PhD in 2012 from National University of Singapore) and has worked extensively on graph (network) and combinatorial algorithms and in applying these to omics datasets in biomedicine. Much of his research has focused on biological network clustering algorithms and their applications to protein complex prediction, and he has co-authored a comprehensive book (ACM Books (New York) and Morgan & Claypool (California)) on the topic. More recently, he has been working on computational methods to predict synthetic lethal (SL) targets in cancers. He won an American Association for Cancer Research (AACR) – Susan G. Komen for the Cure® Award at the San Antonio Breast Cancer Symposium 2015 (San Antonio, Texas, USA) for his method MutExSL that predicts SL targets based on mutual exclusivity of genetic alterations in cancers. In collaborations with QIMR-Berghofer, NUS, Politecnico di Milano, and Dompé Pharma (Milano) he’s now working on improving and validating the predictions from MutExSL. He has co-authored ~30 publications in leading journals and conferences in the bioinformatics field, with 18 in the last 5 years, >50% of which are as first or joint-first author. He serves on the Editorial Board of Scientific Reports (Nature publication) and is a guest editor for Methods (Elsevier).

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