Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Bayesian metamodeling of complex biological systems across varying representations

View ORCID ProfileBarak Raveh, Liping Sun, Kate L. White, Tanmoy Sanyal, Jeremy Tempkin, Dongqing Zheng, View ORCID ProfileKala Bharat, Jitin Singla, ChenXi Wang, Jihui Zhao, Angdi Li, View ORCID ProfileNicholas A. Graham, View ORCID ProfileCarl Kesselman, View ORCID ProfileRaymond C. Stevens, View ORCID ProfileAndrej Sali
doi: https://doi.org/10.1101/2021.03.29.437574
Barak Raveh
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
2Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, 94158, USA
3School of Computer Science and Engineering, The Hebrew University of Jerusalem, 9190416, Israel
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Barak Raveh
Liping Sun
4iHuman Institute, ShanghaiTech University, Shanghai 201210, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kate L. White
7Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tanmoy Sanyal
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
2Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, 94158, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jeremy Tempkin
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
2Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, 94158, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dongqing Zheng
8Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kala Bharat
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
2Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, 94158, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kala Bharat
Jitin Singla
7Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089, USA
9Institute for Quantitative and Computational Biosciences, Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA 90095, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
ChenXi Wang
4iHuman Institute, ShanghaiTech University, Shanghai 201210, China
5School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
6University of Chinese Academy of Sciences, Beijing 100049, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jihui Zhao
4iHuman Institute, ShanghaiTech University, Shanghai 201210, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Angdi Li
4iHuman Institute, ShanghaiTech University, Shanghai 201210, China
5School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
6University of Chinese Academy of Sciences, Beijing 100049, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nicholas A. Graham
8Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Nicholas A. Graham
Carl Kesselman
8Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Carl Kesselman
Raymond C. Stevens
4iHuman Institute, ShanghaiTech University, Shanghai 201210, China
5School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
7Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Raymond C. Stevens
Andrej Sali
1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
2Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, 94158, USA
3School of Computer Science and Engineering, The Hebrew University of Jerusalem, 9190416, Israel
10Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Andrej Sali
  • For correspondence: sali@salilab.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

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 (i) converted to a standardized statistical representation relying on Probabilistic Graphical Models, (ii) coupled by modeling their mutual relations with the physical world, and (iii) 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 ß-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 ß-Cell Consortium.

Significance Statement Cells are the basic units of life, yet their architecture and function remain to be fully characterized. This work describes Bayesian metamodeling, a modeling approach that divides-and-conquers a large problem of modeling numerous aspects of the cell into computing a number of smaller models of different types, followed by assembling these models into a complete map of the cell. Metamodeling enables a facile collaboration of multiple research groups and communities, thus maximizing the sharing of expertise, resources, data, and models. A proof-of-principle is provided by a model of glucose-stimulated insulin secretion produced by the Pancreatic ß-Cell Consortium.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://github.com/salilab/metamodeling

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted March 29, 2021.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Bayesian metamodeling of complex biological systems across varying representations
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Bayesian metamodeling of complex biological systems across varying representations
Barak Raveh, Liping Sun, Kate L. White, Tanmoy Sanyal, Jeremy Tempkin, Dongqing Zheng, Kala Bharat, Jitin Singla, ChenXi Wang, Jihui Zhao, Angdi Li, Nicholas A. Graham, Carl Kesselman, Raymond C. Stevens, Andrej Sali
bioRxiv 2021.03.29.437574; doi: https://doi.org/10.1101/2021.03.29.437574
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Bayesian metamodeling of complex biological systems across varying representations
Barak Raveh, Liping Sun, Kate L. White, Tanmoy Sanyal, Jeremy Tempkin, Dongqing Zheng, Kala Bharat, Jitin Singla, ChenXi Wang, Jihui Zhao, Angdi Li, Nicholas A. Graham, Carl Kesselman, Raymond C. Stevens, Andrej Sali
bioRxiv 2021.03.29.437574; doi: https://doi.org/10.1101/2021.03.29.437574

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Cell Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4380)
  • Biochemistry (9571)
  • Bioengineering (7084)
  • Bioinformatics (24832)
  • Biophysics (12595)
  • Cancer Biology (9949)
  • Cell Biology (14344)
  • Clinical Trials (138)
  • Developmental Biology (7943)
  • Ecology (12095)
  • Epidemiology (2067)
  • Evolutionary Biology (15980)
  • Genetics (10915)
  • Genomics (14730)
  • Immunology (9862)
  • Microbiology (23636)
  • Molecular Biology (9472)
  • Neuroscience (50824)
  • Paleontology (369)
  • Pathology (1538)
  • Pharmacology and Toxicology (2678)
  • Physiology (4009)
  • Plant Biology (8653)
  • Scientific Communication and Education (1508)
  • Synthetic Biology (2389)
  • Systems Biology (6422)
  • Zoology (1345)