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.
- •Systems biology aims for a “whole-cell” view rather than single-pathway explanations
- •Analogy to engineering: understanding components and what happens when you change/remove them
- •Perturbation as a core biological method: change one element and observe system-wide effects
- •Systems biology sits at the intersection of biology with engineering and quantitative methods
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.
- •Models mimic selected characteristics of real systems (e.g., ball-and-stick chemistry models)
- •Models are built to answer specific questions, not to replicate reality in full detail
- •Different abstraction levels exist depending on the decision you’re trying to make
- •“All models are wrong, but some are useful” (Box) as a guiding philosophy
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).
- •R₀/basic reproductive rate as a key concept in epidemic growth
- •SIR model: susceptible–infectious–recovered compartments
- •Differential equations describe transitions and enable prediction
- •Model refinement via added variables (e.g., asymptomatic) when data doesn’t fit
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.
- •Spherical cow as shorthand for simplifying assumptions to get started
- •Digital twin as a high-fidelity computational mirror of a real system
- •Even space mission calculations sometimes use point-mass approximations
- •Choosing model complexity depends on the question and available data
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.
- •TB pathogen has ~4000 genes—models help prioritize targets
- •Genes → proteins → reactions: understanding which reactions are essential
- •Selectivity matters: avoid cross-reactivity with humans or helpful bacteria
- •A systems perspective is needed to anticipate cascading downstream effects
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.
- •DBTL applies to modelling: hypothesize/design → simulate/build → test → learn
- •Testing against data reveals deviations that guide refinement
- •Example: SIR failing leads to adding compartments/parameters
- •Models reduce experimental burden and make experiments more targeted
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.
- •Central dogma: DNA → RNA → protein (proteins do most cellular work)
- •Genomics = all DNA; transcriptomics = all RNA; proteomics = all proteins
- •Metabolomics covers small molecules involved in metabolism
- •Key challenge: turning “parts lists” into system understanding
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.
- •Microbiome as an ecosystem of microbes in an environment (skin, gut, etc.)
- •Extremophiles in hydrothermal vents have unusual pathways due to harsh conditions
- •ISS microbes can produce valuable compounds (e.g., surfactants for cosmetics)
- •Microbiome data often starts as an abundance vector of organisms
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.
- •Green manufacturing seeks microbial routes to fuels, chemicals, and drugs
- •Example: transferring artemisinin pathway from plants to microbes for scale
- •Metabolic engineering = tuning cells to overproduce target compounds
- •Co-cultures can share tasks and improve production efficiency
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.
- •Network basics: nodes and edges (social networks analogy)
- •Networks at multiple scales: microbes, metabolites, reactions, even atoms/bonds
- •Stoichiometry and reaction catalogs enable network construction
- •Pathfinding analogy: best route from molecule A to product B in a metabolic network
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.
- •IBSE began in 2015 to leverage quantitative talent for biology using available data
- •Evolved toward systems medicine focus; interdisciplinary faculty across departments
- •Genome India: ~10,000 Indian genomes sequenced/analyzed for population insights
- •Garbhini project: India-specific gestational age models for preterm birth research
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.
- •Metagenomics differs from genomics: mixed-organism sequencing from environments
- •MetaSub: microbial signature mapping of urban transit systems (NYC → Chennai)
- •Environmental datasets (drinking water, sewage) can track resistant microbes
- •Wastewater surveillance as an emerging public-health early warning approach
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.
- •Gut microbes produce metabolites humans can’t synthesize (e.g., some vitamins)
- •Antibiotics can strongly perturb microbiomes; recovery can take 6–24 months
- •People vary in organisms, but functional outputs can be similar across guts
- •Probiotic products often focus on “helpful microbes,” but rigorous design is harder
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.
- •Biology often proceeds by reverse engineering rather than full design knowledge
- •Perturbation experiments: remove one component and observe outcomes
- •Ethical limits increase with organism complexity; models reduce invasive testing
- •Models help generate sharper hypotheses for lab validation
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.
- •Undergrad exposure to process control helped bridge into systems biology thinking
- •Computational training (HPC, drug discovery, structural biology) shaped the approach
- •Research direction: microbiome dynamics, stability, invasion, and state transitions
- •Goal: systematically designed microbial consortia (human gut, coral probiotics, environments)
