From Mechanistic Models to AI Synergy: A Roadmap for Predictive and Personalized Biology

Abstract

Simple Summary Biology currently runs on two modeling traditions. Mechanistic models write down the processes a biologist believes are at work, so their variables and parameters carry meaning, but they are slow to build and hard to fit to the volume of data that modern experiments produce. Deep learning finds patterns in that data with far less human guidance, yet its predictions rarely explain themselves, and it depends on large labeled datasets. This review asks how the two can be combined rather than pitted against each other. We organize the literature into three strategies: AI that accelerates mechanistic modeling, mechanistic knowledge that constrains AI, and hybrid designs in which the two exchange information in a loop. Across systems and synthetic biology, drug discovery, epidemiology, and neuroscience, the strongest cases test whether the mechanistic component adds value, not merely whether the combined model scores well. We then examine seven obstacles—interpretability, data sparsity, causality, identifiability, computational scale, multimodal integration, and validation—and sketch a roadmap toward digital twins, causal discovery engines, and automated science. A shared vocabulary and clear evidence standards would help make biological models both predictive and comprehensible, which is what personalized medicine ultimately requires.