Can model reduction replace expert intuition for modeling complex biological systems?

One of the challenges to modeling biological systems is the overwhelming complexity. Mathematical models that account for all the known interactions would have an unwieldy number of components and parameters. Traditionally, real-world models have been based on expert intuition that judiciously relate only those components believed to be relevant to a behavior of interest. These models typically reflect intuition and physical insights that are difficult to rigorously justify or convey. Here, we consider whether recent advances in model reduction may be able to automatically construct comparable simplified models in a semi-automatic, data-driven way. We report on a comparative study of model reduction of the Wnt signaling pathway, comparing automatic methods with expert intuition. Automatic model reduction is done using the Manifold Boundary Approximation Method (MBAM), based on information geometry and "sloppy model" analysis. We find that MBAM leads to simplified models that closely resemble those proposed based on expert intuition. Our results suggest that data-driven methods of model reduction may be a viable alternative to expert-derived models, and can be used to extract comparable physical insights into the behavior of complex biological systems.