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Integrative modeling of the cell. Acta Biochim Biophys Sin (Shanghai) 2022; 54:1213-1221. [PMID: 36017893 PMCID: PMC9909318 DOI: 10.3724/abbs.2022115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
A whole-cell model represents certain aspects of the cell structure and/or function. Due to the high complexity of the cell, an integrative modeling approach is often taken to utilize all available information including experimental data, prior knowledge and prior models. In this review, we summarize an emerging workflow of whole-cell modeling into five steps: (i) gather information; (ii) represent the modeled system into modules; (iii) translate input information into scoring function; (iv) sample the whole-cell model; (v) validate and interpret the model. In particular, we propose the integrative modeling of the cell by combining available (whole-cell) models to maximize the accuracy, precision, and completeness. In addition, we list quantitative predictions of various aspects of cell biology from existing whole-cell models. Moreover, we discuss the remaining challenges and future directions, and highlight the opportunity to establish an integrative spatiotemporal multi-scale whole-cell model based on a community approach.
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Agmon E, Spangler RK, Skalnik CJ, Poole W, Peirce SM, Morrison JH, Covert MW. Vivarium: an interface and engine for integrative multiscale modeling in computational biology. Bioinformatics 2022; 38:1972-1979. [PMID: 35134830 PMCID: PMC8963310 DOI: 10.1093/bioinformatics/btac049] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 12/14/2021] [Accepted: 01/28/2022] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION This article introduces Vivarium-software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists. RESULTS Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs and run with Vivarium's discrete-event simulation engine. Vivarium's utility is demonstrated by building composite models that combine several modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics and spatial diffusion. This demonstrates just the beginning of what is possible-Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales. AVAILABILITY AND IMPLEMENTATION The specific models, simulation pipelines and notebooks developed for this article are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on Python Package Index. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely available Vivarium processes and composites, including the processes used in Section 3. Supplementary Materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ryan K Spangler
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | - William Poole
- Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA
| | - Shayn M Peirce
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Jerry H Morrison
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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Raveh B, Sun L, White KL, Sanyal T, Tempkin J, Zheng D, Bharath K, Singla J, Wang C, Zhao J, Li A, Graham NA, Kesselman C, Stevens RC, Sali A. Bayesian metamodeling of complex biological systems across varying representations. Proc Natl Acad Sci U S A 2021; 118:e2104559118. [PMID: 34453000 PMCID: PMC8536362 DOI: 10.1073/pnas.2104559118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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.
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Affiliation(s)
- Barak Raveh
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190416, Israel
| | - Liping Sun
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
| | - Tanmoy Sanyal
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Jeremy Tempkin
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Dongqing Zheng
- Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Kala Bharath
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Jitin Singla
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
- Epstein Department of Industrial and Systems Engineering, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
- Information Science Institute, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Chenxi Wang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jihui Zhao
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Angdi Li
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Nicholas A Graham
- Mork Family Department of Chemical Engineering and Materials Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Carl Kesselman
- Epstein Department of Industrial and Systems Engineering, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
- Information Science Institute, The Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089
| | - Raymond C Stevens
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
- Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA 90089
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158;
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
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Matthews ML, Marshall-Colón A. Multiscale plant modeling: from genome to phenome and beyond. Emerg Top Life Sci 2021; 5:231-237. [PMID: 33543231 PMCID: PMC8166335 DOI: 10.1042/etls20200276] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 01/08/2023]
Abstract
Plants are complex organisms that adapt to changes in their environment using an array of regulatory mechanisms that span across multiple levels of biological organization. Due to this complexity, it is difficult to predict emergent properties using conventional approaches that focus on single levels of biology such as the genome, transcriptome, or metabolome. Mathematical models of biological systems have emerged as useful tools for exploring pathways and identifying gaps in our current knowledge of biological processes. Identification of emergent properties, however, requires their vertical integration across biological scales through multiscale modeling. Multiscale models that capture and predict these emergent properties will allow us to predict how plants will respond to a changing climate and explore strategies for plant engineering. In this review, we (1) summarize the recent developments in plant multiscale modeling; (2) examine multiscale models of microbial systems that offer insight to potential future directions for the modeling of plant systems; (3) discuss computational tools and resources for developing multiscale models; and (4) examine future directions of the field.
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Affiliation(s)
- Megan L Matthews
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
| | - Amy Marshall-Colón
- Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
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Sali A. From integrative structural biology to cell biology. J Biol Chem 2021; 296:100743. [PMID: 33957123 PMCID: PMC8203844 DOI: 10.1016/j.jbc.2021.100743] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/09/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.
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Affiliation(s)
- Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
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