Chennai, Sept 1:
IIT Madras will host a three-day national workshop on systems biology, metabolic engineering and constraint-based modelling from September 16 to 18, bringing together students, researchers, faculty and industry professionals for specialised lectures and hands-on training.
Titled ‘Metabolic Modelling – From Pathways to Predictions’, the in-person workshop is being organised by the Wadhwani School of Data Science and AI (WSAI) and the Centre for Outreach and Digital Education (CODE) at IIT Madras.
The programme will combine classroom-based lectures with practical training, including the use of computational tools such as COBRApy to analyse and interpret metabolic models of individual organisms as well as microbial communities.
The workshop is designed to provide participants with practical exposure to systems biology and metabolic engineering, particularly the use of computational approaches to understand biological systems and study their metabolic pathways.
Final-year undergraduate and postgraduate students, researchers, faculty members and industry professionals with a background in, or interest in, biology and metabolic modelling are eligible to apply.
Participants will be selected based on their statement of purpose, educational background and relevance of their academic or professional interests to the workshop’s focus.
The workshop is being convened by Karthik Raman of WSAI and Meiyappan Lakshmanan, Assistant Professor in the Department of Biotechnology at IIT Madras.
Faculty members, postdoctoral researchers and PhD scholars from the Centre for Integrative Biology and Systems Medicine (IBSE) and WSAI will conduct the sessions.
The organisers said the workshop is intended to bridge biological sciences and computational modelling by giving participants hands-on experience in building, analysing and interpreting metabolic models.
The last date for applications is September 4, 2026. Queries regarding the programme can be sent to [email protected].
The workshop comes as computational methods and data-driven approaches gain increasing importance in systems biology, metabolic engineering and the study of complex biological networks.