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 language||American English|
|Journal||Proceedings of the National Academy of Sciences of the United States of America|
|State||Published - 27 Aug 2021|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS. We are grateful to all members of the Pancreatic β-Cell Consortium for providing the context in which this research was performed. In particular, we appreciated the discussions with Helen Berman and Peter Butler. We also acknowledge helpful comments by Keren Lasker, Trey Ideker, Marcus Covert, Eran Agmon, Reshef Mintz, Thomas L. Blundell, and Ken Dill. The work was funded by grants NIH National Institute of General Medical Science (NIGMS) R01GM083960, NIH/NIGMS P41GM109824, and NIH National Institute of Allergy and Infectious Diseases U19AI135990 (A.S.); Bridge Institute at University of Southern California (A.S., R.C.S., and K.L.W.); ShanghaiTech University (L.S., C.W., J.Z., and A.L.); Bridge Institute postdoctoral fellowship (K.B.); the Burroughs Wellcome Fund Travel Award (K.L.W.); and a starting grant from the Hebrew University of Jerusalem (B.R.). We also acknowledge the High Performance Computing Platform of Shang-haiTech University for computing time.
© 2021 National Academy of Sciences. All rights reserved.
- Bayesian metamodeling
- Integrative modeling
- Multiscale modeling
- Pancreatic β-cell
- Whole-cell modeling