Best Place To BuildHow Computational Microbiology drives disease research & treatment | Prof Karthik Raman | BP2B S2 E8
CHAPTERS
Why systems biology exists: engineering thinking for living cells
The conversation opens by reframing biology as an engineering discipline: instead of isolated discoveries (one gene/protein at a time), the goal is a holistic understanding of how cellular components work together. Prof. Raman positions systems biology as the bridge between biology and quantitative/model-based reasoning needed to predict and manipulate complex living systems.
What “modelling” means (and why all models are approximations)
Prof. Raman explains modelling as building simplified representations of reality that preserve the features needed to answer specific questions. He highlights that models range from very simple abstractions to high-fidelity simulations, and that usefulness—not perfection—is the standard for a good model.
Pandemic math: SIR models, R₀, and iterative improvement
Using COVID as a familiar example, the discussion shows how epidemiological models quantify spread and inform resource planning (beds, oxygen, policy). Prof. Raman introduces the SIR model structure and emphasizes how models evolve when reality reveals missing factors (like asymptomatic infections).
From “spherical cows” to digital twins: the spectrum of fidelity
The episode contrasts playful, extreme simplifications (“assume a spherical cow”) with modern ambitions like digital twins that behave like the real system. The point: simple models are often necessary starting points, while digital twins represent an advanced endpoint for simulation and decision-making.
Microbiology modelling in practice: targeting TB and avoiding side effects
Shifting from populations to single cells, Prof. Raman describes how models help identify drug targets in pathogens like Mycobacterium tuberculosis. The challenge is selecting essential microbial genes/proteins while minimizing harm to human proteins and beneficial microbes.
DBTL for biology: design–build–test–learn and “debugging” models
The discussion maps the engineering DBTL cycle directly onto computational biology workflows. Model predictions are tested against data, deviations are treated as clues, and the model is iteratively improved—similar to debugging a complex engineered system.
Omics primer: genomics, transcriptomics, proteomics, metabolomics
Prof. Raman introduces omics as whole-system measurement technologies that catalog the cell at multiple layers—from DNA to RNA to proteins to small molecules. Computational systems biology then focuses on assembling these parts into coherent, predictive models of function and regulation.
Microbiomes across environments: gut, deep-sea extremophiles, and the ISS
The episode broadens from the gut microbiome to environmental microbiomes in extreme and engineered settings. Studying diverse ecosystems helps discover exotic metabolisms and pathways that can be repurposed for industrial and medical applications.
Why exotic microbes matter: green manufacturing and metabolic engineering
Prof. Raman connects environmental microbiology to sustainable industry: microbes can be engineered to manufacture drugs and chemicals. He describes metabolic engineering as rerouting cellular pathways toward a “molecule of interest,” including using co-cultures for division of labor.
Networks as the unifying language: from pathways to Google Maps for metabolism
Networks/graphs are presented as the recurring conceptual tool across the lab’s work. From microbe–microbe interactions to metabolite reaction networks, network algorithms enable pathfinding and optimization—similar to routing on Google Maps, but inside cells.
IBSE at IIT Madras: interdisciplinary center and national-scale genomics
Prof. Raman explains the origin and evolution of IBSE into a center focused on integrative biology and systems medicine. He highlights major initiatives including Genome India (sequencing/analysis) and translational projects like improved gestational-age models for Indian populations.
Metagenomics & city-scale surveillance: from subway swabs to sewage signals
The conversation explores metagenomics as sequencing the genetic material from all organisms in a place, enabling microbial “signatures” of environments. Projects like MetaSub extend to Chennai, and the discussion points to future genomic surveillance via water/sewage and even airline waste to detect outbreaks early.
Gut microbiome reality check: antibiotics, recovery, and supplement marketing
Prof. Raman reframes the gut as a major metabolic organ and explains how antibiotics can disrupt it like a “forest fire,” with recovery taking months to years. He discusses why off-the-shelf probiotic claims can be oversimplified, and why modelling is needed for more systematic, personalized interventions.
Reverse engineering life: “Can a biologist fix a radio?” and ethics
The episode contrasts engineering’s build-from-known-components approach with biology’s reverse-engineering reality. Modelling helps explore “what-if” questions when direct experimentation can be impractical or unethical, especially in higher organisms.
Career path and the future: dynamics, invasion, and designed microbial communities
Prof. Raman traces his transition from chemical/food engineering to computational science and academia, arguing that computation becomes most exciting when applied to a rich domain like biology. He closes with the lab’s forward-looking agenda: modelling microbiome dynamics, designing minimal functional communities, and creating targeted probiotic-like interventions for humans and even corals.
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