Bayesian metamodeling of complex biological systems across varying representations

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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 models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.

Original languageAmerican English
Article numbere2104559118
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number35
DOIs
StatePublished - 31 Aug 2021

Bibliographical note

Publisher Copyright:
© 2021 National Academy of Sciences. All rights reserved.

Keywords

  • Bayesian metamodeling
  • Integrative modeling
  • Multiscale modeling
  • Pancreatic β-cell
  • Whole-cell modeling

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