When Everyone Can Build a Model: Mechanistic Inference in the Age of AI
Abstract
Modern deep learning and AI-assisted code generation are lowering the technical barrier to building mathematical models of biological systems that reproduce complex observations. This lets researchers with domain expertise build semi-mechanistic, hybrid models constrained by prior knowledge and augmented with learned structure without extensive training in modelling. However, easier construction does not make interpretation easier: predictive success of such so-called grey-box models does not establish that an inferred mechanism corresponds to the one that generated the data, and the learned component can absorb errors in the mechanistic part. Many biological modelling problems are underdetermined, so several, possibly infinitely many, models may reproduce the same data equally well. This ambiguity enters at two modelling choices: which variables can affect others, and which response function describes the underlying mechanism. For the first choice, causal inference provides a formal framework stating which causal relationships and intervention effects are identifiable from the data and available knowledge. For the second, ensemble analyses quantify the uncertainty and the range of behaviours still allowed by models that fit equally well. Together, they turn a single good fit into an explicit account of what is supported, what remains open, and which experiment would decide.
