When Everyone Can Build a Model: Mechanistic Inference in the Age of AI

Modern deep learning and AI-assisted code generation are lowering the technical barrier to building mathematical models of biological systems, enabling researchers with domain expertise to create semi-mechanistic, hybrid models that combine prior knowledge with learned structure. However, the predictive success of these grey-box models does not guarantee that the inferred mechanisms truly reflect the underlying biological processes, as the learned components can mask errors in the mechanistic part. To address this ambiguity, the paper proposes using causal inference to identify which causal relationships are discernible from data and knowledge, and ensemble analyses to quantify uncertainty across models that fit the data equally well, thereby clarifying what is supported, what remains uncertain, and which experiments could resolve the remaining questions.

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