Alpita Kulkarni in The Scientist:
Most biology labs today—particularly smaller or early-career groups and those outside major funding hubs—face a tough reality: shrinking budgets, rising costs, limited access to cutting-edge technologies, and mounting pressures to publish. The challenge isn’t just keeping pace with innovation—it’s staying in the game at all. In this climate, simulation-first approaches are a powerful equalizer. By allowing scientists to test biological hypotheses computationally before committing to costly experiments, these tools enable smart science even on a tight budget.
Constraint often drives creativity. One strategy gaining momentum is AI-guided predictive modeling, which relies on data-driven tools that simulate biological behavior to unlock new insights. Built using advanced techniques like generative modeling (which simulates how a cell might respond to genetic or environmental changes) and probabilistic inference (which estimates the likelihood of different outcomes), these models are reshaping how we interpret complex data.
More here.
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