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How Computational Microbiology drives disease research & treatment | Prof Karthik Raman | BP2B S2 E8

How do you go from analysing online datasets to sequencing 10,000 Indian genomes? 🧬 In this episode of Best Place to Build, we sit down with Prof. Karthik Raman, faculty at the Wadhwani School of Data Science and AI (WSAI), IIT Madras, to explore the incredible journey of the Center for Integrative Biology and Systems Medicine (IBSE). IBSE began in 2015 as the Initiative for Biological Systems Engineering under the mentorship of Prof. Ashok Venkitaraman (Cambridge). The goal: bring bright, quantitatively skilled minds at IIT Madras into the world of biological systems engineering and bioinformatics. By leveraging freely available biological data online, IBSE pioneered work in systems biology long before generating its own datasets. Today, IBSE is a key player in Genome India — India’s ambitious national project to sequence and analyse the genetic data of thousands of people. With over 10,000 genomes sequenced, the center is uncovering critical insights into genetic diversity, disease mutations, personalized medicine, and healthcare innovation. 💡 In this conversation, Prof. Raman discusses: * The origins of IBSE and its transformation into a center for integrative biology and medicine * Why bioinformatics and data science are central to the future of genomics research * The role of IIT Madras and WSAI in shaping India’s contribution to global science * How genome sequencing can impact public health, diagnostics, and personalized medicine This episode is not just about the science of genomics — it’s about the future of healthcare in India and beyond. 🔔 Don’t forget to subscribe for more conversations on innovation, technology, and research from IIT Madras. Jump straight to what makes you curious here: 00:00 – Intro 00:41 - Welcome to the best place to build 01:13 - Introducing Prof. Karthik Raman 04:37 - What exactly is modelling? 06:53 - The math behind a pandemic 08:25 - What’s a digital twin? 09:00 - You wanna start with a spherical cow 15:19 - Introduction to the omic studies 22:32 - Scope of Prof. Karthik Raman’s work 30:20 - Levels of microbiology 34:05 - IBSE & Genome India 46:00 - Debunking gut microbe supplement marketing 49:00 - Prof Karthik Raman’s journey from Chemical Engineering to Computational Engineering 52:53 - The intersection of maths and biology 55:00 - AI in microbiology 58:00 - Closing thoughts & reflections

Karthik Ramanguest
Sep 11, 20251h 9mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Computational systems microbiology: modeling microbes to understand disease and health

  1. Systems biology uses simplified mathematical models to understand and manipulate complex biological systems, much like engineering models, while acknowledging that all models are imperfect but still useful.
  2. Computational systems biology combines large-scale experimental “omics” measurements (genomics, transcriptomics, proteomics, metabolomics) with algorithms to connect molecular parts into predictive system-level behavior.
  3. Systems microbiology applies these ideas to microbes and microbiomes—ranging from single pathogens like tuberculosis to communities in the gut, cities, oceans, and the International Space Station—to study interactions and functions.
  4. Network representations (graphs) unify many problems in the lab, from metabolic pathways and microbe–microbe interactions to finding “best routes” for producing commercially valuable molecules.
  5. The discussion critiques hype in gut-supplement marketing and argues for more systematic, model-driven design of probiotics and microbiome interventions, including future goals like personalized or environment-specific “concoctions.”

IDEAS WORTH REMEMBERING

5 ideas

Modeling is selective simplification, not perfect replication.

Raman emphasizes that models mimic only key characteristics needed to answer a question (e.g., hospital beds for COVID), starting simple and adding complexity as mismatches appear.

Start with “spherical cows,” then iterate toward reality.

He defends crude assumptions as a necessary entry point, then recommends incremental refinement through the DBTL cycle when data reveal missing mechanisms (e.g., asymptomatic compartments beyond SIR).

Omics produces parts lists; systems biology builds the ‘engine.’

Genomics/transcriptomics/proteomics/metabolomics catalog what’s present, but computation is needed to infer interactions, control, and emergent behavior from those components.

Networks are the common language across scales in biology.

From microbe–microbe ecology to metabolite conversions and even atom-bond graphs of chemicals, graph abstractions enable reuse of algorithms (pathfinding, interaction inference, community design).

Drug and pathogen targeting requires a systems perspective.

Using TB as an example, choosing targets among ~4,000 genes demands understanding essentiality and off-target effects, including impacts on host proteins and beneficial microbes.

WORDS WORTH SAVING

5 quotes

All models are wrong, but some are useful.

— Prof. Karthik Raman (citing statistician George Box)

You wanna start with a spherical cow in a vacuum.

— Prof. Karthik Raman

Can we tame biology as an engineering discipline?

— Prof. Karthik Raman (citing Tom Knight)

Biology is too important to be left to the biologists.

— Prof. Karthik Raman (attributed to a major science-agency leader; described as controversial)

It’s like a forest fire… your gut nicely recovers after an antibiotic administration.

— Prof. Karthik Raman

Systems biology vs bioinformatics vs computational biologyMathematical modeling and abstraction (SIR, R0, spherical cow)Digital twins and model usefulness (Box quote)DBTL loop: design–build–test–learn in biologyOmics: genomics, transcriptomics, proteomics, metabolomicsMicrobiomes: gut, skin, deep sea extremophiles, ISS, coralsNetworks/graphs in metabolism, communities, and molecule synthesisMetabolic engineering and “molecule of interest” optimizationIBSE/Center for Integrative Biology and Systems MedicineGenome India, MetaSub Chennai, genomic surveillance (sewage/water)Antibiotics, dysbiosis, recovery dynamics, functional redundancyAI/data science intersection with microbiology

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