Abstract

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input …

Date

2021

Authors

Barak Raveh, Liping Sun, Kate L White, Tanmoy Sanyal, Jeremy Tempkin, Dongqing Zheng, Kala Bharath, Jitin Singla, ChenXi Wang, Jihui Zhao, Angdi Li, Nicholas A Graham, Carl Kesselman, Raymond C Stevens, Andrej Sali

Journal

Proceedings of the National Academy of Sciences

Volume

118

Issue

35

Pages

e2104559118

Publisher

National Academy of Sciences