Bayesian Occam's Razor to Optimize Models for Complex Systems

Abstract

Modeling complex biological systems necessitates the integration of vast and multifaceted information spanning various aspects of these systems, and is expected to yield more insights into the system than any of the inputs. Metamodeling, a specialized form of integrative modeling, addresses this by integrating existing models. Developing and optimizing metamodels pose challenges due to the complexities introduced by diverse input models and their inherent uncertainties. In this study, we employ Bayesian formalism to rigorously analyze the propagation of probability throughout the metamodeling process and propose quantitative assessments for it. Building on this, we introduce a method for optimizing metamodeling that adheres to the Bayesian Occams razor rationale, by (i) minimizing model uncertainty; (ii) maximizing model consistency; and (iii) reducing model complexity. To illustrate the benefits of this method, we apply it to the dynamic system of glucose-stimulated insulin secretion in pancreatic {beta}-cells. The optimized metamodel delivers more accurate estimates of impaired {beta}-cell dynamics and function in T2D subjects compared to the non-optimized one, underscoring the critical role of optimization in enhancing both model reliability and applicability. This method is implemented through the Integrative Modeling Platform (IMP), facilitating the development of accurate, precise, and sufficiently simple models for a variety of complex systems.