Simulating is not always understanding: When model complexity obscures biology
In cell biology, computational models of biological systems range from minimal representations with a handful of parameters to whole-cell simulations tracking thousands of molecular species across a complete cell cycle. While these models span a continuum of detail, increasing complexity changes what they capture and are able to explain, what they can predict, and how they can fall short. A model contributes to understanding only when it makes novel predictions, reveals an unexpected coupling between processes, or fails in a way that identifies missing parameters. We contend that what is important for understanding is not the number of components or spatial dimensions a model contains, but the ratio of free parameters to the experimental constraints available to pin them down, and whether we can see why it produces the behaviors it does. Large-scale agent-based models of cytoskeletal dynamics or tissue mechanics that are built on a small number of physically grounded rules can reveal rich self-organization behavior precisely because their parameter spaces are small enough to explore systematically. By contrast, when free parameters grow faster than the data available to constrain them, models become progressively harder to interpret -- and even disprove --regardless of their biological scope. We argue that the field needs to reconsider the goal of complex models. We should move away from trying to include as many parameters as possible and instead aim for systematic comparisons with simpler representations, dynamical analysis, and explicit model hierarchies that trace how cellular behavior emerges from its parts.