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Biophysical models describing complex, cellular phenomena typically includesystems of nonlinear differential equations with many free parameters. Whileexperimental measurements can fix some parameters, those describing internalcellular processes frequently remain inaccessible. Hence, a proliferation offree parameters risks overfitting the data, limiting the model's predictivepower. In this study, we develop robust methods, applying statistical analysisand dynamical-systems theory, to reduce a biophysical model's complexity. Wedemonstrate our techniques on an elaborate computational model designed todescribe active, mechanical motility of auditory hair cells. Specifically, weuse two statistical measures, the total-effect and PAWN indices, to rank eachfree parameter by its influence on selected, core properties of the model. Withthe resulting ranking, we fix most of the less influential parameters, yieldinga low-parameter model with optimal predictive power. We validate thetheoretical model with experimental recordings of active hair-bundle motility,specifically by using Akaike and Bayesian information criteria after obtainingmaximum-likelihood fits. As a result, we determine the system's mostinfluential parameters, which illuminate its key biophysical elements of thecell's overall features. While we demonstrated our techniques on a concreteexample, they provide a general framework, applicable to other biophysicalsystems.

Developing Parameter-Reduction Methods on a Biophysical Model of Auditory Hair Cells