1
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Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
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Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
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2
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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3
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Mutsuddy A, Erdem C, Huggins JR, Salim M, Cook D, Hobbs N, Feltus FA, Birtwistle MR. Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format. BIOINFORMATICS ADVANCES 2023; 3:vbad039. [PMID: 37020976 PMCID: PMC10070034 DOI: 10.1093/bioadv/vbad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/25/2023] [Accepted: 03/22/2023] [Indexed: 04/05/2023]
Abstract
Summary Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks. Availability and implementation Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).
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Affiliation(s)
- Arnab Mutsuddy
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Cemal Erdem
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Jonah R Huggins
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
- School of Computing, Clemson University, Clemson, SC, USA
| | | | | | | | - F Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
- Department of Bioengineering, Clemson University, Clemson, SC, USA
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4
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Surveying membrane landscapes: a new look at the bacterial cell surface. Nat Rev Microbiol 2023:10.1038/s41579-023-00862-w. [PMID: 36828896 DOI: 10.1038/s41579-023-00862-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/30/2023] [Indexed: 02/26/2023]
Abstract
Recent studies applying advanced imaging techniques are changing the way we understand bacterial cell surfaces, bringing new knowledge on everything from single-cell heterogeneity in bacterial populations to their drug sensitivity and mechanisms of antimicrobial resistance. In both Gram-positive and Gram-negative bacteria, the outermost surface of the bacterial cell is being imaged at nanoscale; as a result, topographical maps of bacterial cell surfaces can be constructed, revealing distinct zones and specific features that might uniquely identify each cell in a population. Functionally defined assembly precincts for protein insertion into the membrane have been mapped at nanoscale, and equivalent lipid-assembly precincts are suggested from discrete lipopolysaccharide patches. As we review here, particularly for Gram-negative bacteria, the applications of various modalities of nanoscale imaging are reawakening our curiosity about what is conceptually a 3D cell surface landscape: what it looks like, how it is made and how it provides resilience to respond to environmental impacts.
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5
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Stevens JA, Grünewald F, van Tilburg PAM, König M, Gilbert BR, Brier TA, Thornburg ZR, Luthey-Schulten Z, Marrink SJ. Molecular dynamics simulation of an entire cell. Front Chem 2023; 11:1106495. [PMID: 36742032 PMCID: PMC9889929 DOI: 10.3389/fchem.2023.1106495] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/09/2023] [Indexed: 01/19/2023] Open
Abstract
The ultimate microscope, directed at a cell, would reveal the dynamics of all the cell's components with atomic resolution. In contrast to their real-world counterparts, computational microscopes are currently on the brink of meeting this challenge. In this perspective, we show how an integrative approach can be employed to model an entire cell, the minimal cell, JCVI-syn3A, at full complexity. This step opens the way to interrogate the cell's spatio-temporal evolution with molecular dynamics simulations, an approach that can be extended to other cell types in the near future.
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Affiliation(s)
- Jan A. Stevens
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Fabian Grünewald
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - P. A. Marco van Tilburg
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Melanie König
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Benjamin R. Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Champaign, IL, United States
| | - Troy A. Brier
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Champaign, IL, United States
| | - Zane R. Thornburg
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Champaign, IL, United States
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Champaign, IL, United States
| | - Siewert J. Marrink
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands,*Correspondence: Siewert J. Marrink,
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6
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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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7
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Ye C, Wei X, Shi T, Sun X, Xu N, Gao C, Zou W. Genome-scale metabolic network models: from first-generation to next-generation. Appl Microbiol Biotechnol 2022; 106:4907-4920. [PMID: 35829788 DOI: 10.1007/s00253-022-12066-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 06/24/2022] [Accepted: 07/02/2022] [Indexed: 11/26/2022]
Abstract
Over the last two decades, thousands of genome-scale metabolic network models (GSMMs) have been constructed. These GSMMs have been widely applied in various fields, ranging from network interaction analysis, to cell phenotype prediction. However, due to the lack of constraints, the prediction accuracy of first-generation GSMMs was limited. To overcome these limitations, the next-generation GSMMs were developed by integrating omics data, adding constrain condition, integrating different biological models, and constructing whole-cell models. Here, we review recent advances of GSMMs from the first generation to the next generation. Then, we discuss the major application of GSMMs in industrial biotechnology, such as predicting phenotypes and guiding metabolic engineering. In addition, human health applications, including understanding biological mechanisms, discovering biomarkers and drug targets, are also summarized. Finally, we address the challenges and propose new trend of GSMMs. KEY POINTS: •This mini-review updates the literature on almost all published GSMMs since 1999. •Detailed insights into the development of the first- and next-generation GSMMs. •The application of GSMMs is summarized, and the prospects of integrating machine learning are emphasized.
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Affiliation(s)
- Chao Ye
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China.
| | - Xinyu Wei
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Tianqiong Shi
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Xiaoman Sun
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, 210023, China
| | - Nan Xu
- College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, 225009, China
| | - Cong Gao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, China
| | - Wei Zou
- College of Bioengineering, Sichuan University of Science & Engineering, Yibin, 644005, China.
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8
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Ramos JRC, Bissinger T, Genzel Y, Reichl U. Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model. Metabolites 2022; 12:metabo12030239. [PMID: 35323683 PMCID: PMC8950586 DOI: 10.3390/metabo12030239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/04/2022] [Accepted: 03/09/2022] [Indexed: 11/21/2022] Open
Abstract
Cell cultured-based influenza virus production is a viable option for vaccine manufacturing. In order to achieve a high concentration of viable cells, is requirement to have not only optimal process conditions, but also an active metabolism capable of intracellular synthesis of viral components. Experimental metabolic data collected in such processes are complex and difficult to interpret, for which mathematical models are an appropriate way to simulate and analyze the complex and dynamic interaction between the virus and its host cell. A dynamic model with 35 states was developed in this study to describe growth, metabolism, and influenza A virus production in shake flask cultivations of suspension Madin-Darby Canine Kidney (MDCK) cells. It considers cell growth (concentration of viable cells, mean cell diameters, volume of viable cells), concentrations of key metabolites both at the intracellular and extracellular level and virus titers. Using one set of parameters, the model accurately simulates the dynamics of mock-infected cells and correctly predicts the overall dynamics of virus-infected cells for up to 60 h post infection (hpi). The model clearly suggests that most changes observed after infection are related to cessation of cell growth and the subsequent transition to apoptosis and cell death. However, predictions do not cover late phases of infection, particularly for the extracellular concentrations of glutamate and ammonium after about 12 hpi. Results obtained from additional in silico studies performed indicated that amino acid degradation by extracellular enzymes resulting from cell lysis during late infection stages may contribute to this observed discrepancy.
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Affiliation(s)
- João Rodrigues Correia Ramos
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
- Correspondence:
| | - Thomas Bissinger
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
| | - Yvonne Genzel
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
| | - Udo Reichl
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; (T.B.); (Y.G.); (U.R.)
- Institute of Process Engineering, Faculty of Process & Systems Engineering, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
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9
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Tandon G, Yadav S, Kaur S. Pathway modeling and simulation analysis. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00007-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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10
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A Petri nets-based framework for whole-cell modeling. Biosystems 2021; 210:104533. [PMID: 34543693 DOI: 10.1016/j.biosystems.2021.104533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/04/2021] [Accepted: 09/06/2021] [Indexed: 11/22/2022]
Abstract
Whole-cell modeling aims to incorporate all main genes and processes, and their interactions of a cell in one model. Whole-cell modeling has been regarded as the central aim of systems biology but also as a grand challenge, which plays essential roles in current and future systems biology. In this paper, we analyze whole-cell modeling challenges and requirements and classify them into three aspects (or dimensions): heterogeneous biochemical networks, uncertainties in components, and representation of cell structure. We then explore how to use different Petri net classes to address different aspects of whole-cell modeling requirements. Based on these analyses, we present a Petri nets-based framework for whole-cell modeling, which not only addresses many whole-cell modeling requirements, but also offers a graphical, modular, and hierarchical modeling tool. We think this framework can offer a feasible modeling approach for whole-cell model construction.
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11
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Oftadeh O, Salvy P, Masid M, Curvat M, Miskovic L, Hatzimanikatis V. A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics. Nat Commun 2021; 12:4790. [PMID: 34373465 PMCID: PMC8352978 DOI: 10.1038/s41467-021-25158-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 07/22/2021] [Indexed: 02/07/2023] Open
Abstract
Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.
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Affiliation(s)
- Omid Oftadeh
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pierre Salvy
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Cambrium GmbH, Berlin, Germany
| | - Maria Masid
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maxime Curvat
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Quotient Suisse SA, Eysins, Switzerland
| | - Ljubisa Miskovic
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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12
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Yadav V, Ullah Irshad I, Kumar H, Sharma AK. Quantitative Modeling of Protein Synthesis Using Ribosome Profiling Data. Front Mol Biosci 2021; 8:688700. [PMID: 34262940 PMCID: PMC8274658 DOI: 10.3389/fmolb.2021.688700] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
Quantitative prediction on protein synthesis requires accurate translation initiation and codon translation rates. Ribosome profiling data, which provide steady-state distribution of relative ribosome occupancies along a transcript, can be used to extract these rate parameters. Various methods have been developed in the past few years to measure translation-initiation and codon translation rates from ribosome profiling data. In the review, we provide a detailed analysis of the key methods employed to extract the translation rate parameters from ribosome profiling data. We further discuss how these approaches were used to decipher the role of various structural and sequence-based features of mRNA molecules in the regulation of gene expression. The utilization of these accurate rate parameters in computational modeling of protein synthesis may provide new insights into the kinetic control of the process of gene expression.
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Affiliation(s)
- Vandana Yadav
- Department of Physics, Indian Institute of Technology Madras, Chennai, India
| | | | - Hemant Kumar
- School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
| | - Ajeet K Sharma
- Department of Physics, Indian Institute of Technology Jammu, Jammu, India
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13
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Covert MW, Gillies TE, Kudo T, Agmon E. A forecast for large-scale, predictive biology: Lessons from meteorology. Cell Syst 2021; 12:488-496. [PMID: 34139161 PMCID: PMC8217727 DOI: 10.1016/j.cels.2021.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/01/2021] [Accepted: 05/18/2021] [Indexed: 11/19/2022]
Abstract
Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.
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Affiliation(s)
- Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Taryn E Gillies
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Takamasa Kudo
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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14
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Production of proteins and commodity chemicals using engineered Bacillus subtilis platform strain. Essays Biochem 2021; 65:173-185. [PMID: 34028523 DOI: 10.1042/ebc20210011] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/02/2021] [Accepted: 05/06/2021] [Indexed: 12/19/2022]
Abstract
Currently, increasing demand of biochemicals produced from renewable resources has motivated researchers to seek microbial production strategies instead of traditional chemical methods. As a microbial platform, Bacillus subtilis possesses many advantages including the generally recognized safe status, clear metabolic networks, short growth cycle, mature genetic editing methods and efficient protein secretion systems. Engineered B. subtilis strains are being increasingly used in laboratory research and in industry for the production of valuable proteins and other chemicals. In this review, we first describe the recent advances of bioinformatics strategies during the research and applications of B. subtilis. Secondly, the applications of B. subtilis in enzymes and recombinant proteins production are summarized. Further, the recent progress in employing metabolic engineering and synthetic biology strategies in B. subtilis platform strain to produce commodity chemicals is systematically introduced and compared. Finally, the major limitations for the further development of B. subtilis platform strain and possible future directions for its research are also discussed.
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15
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Marucci L, Barberis M, Karr J, Ray O, Race PR, de Souza Andrade M, Grierson C, Hoffmann SA, Landon S, Rech E, Rees-Garbutt J, Seabrook R, Shaw W, Woods C. Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology. Front Bioeng Biotechnol 2020; 8:942. [PMID: 32850764 PMCID: PMC7426639 DOI: 10.3389/fbioe.2020.00942] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 07/21/2020] [Indexed: 01/03/2023] Open
Abstract
Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.
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Affiliation(s)
- Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.,School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom.,Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.,Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, United Kingdom.,Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Jonathan Karr
- Icahn Institute for Data Science and Genomic Technology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Oliver Ray
- Department of Computer Science, University of Bristol, Bristol, United Kingdom
| | - Paul R Race
- Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom.,School of Biochemistry, University of Bristol, Bristol, United Kingdom
| | - Miguel de Souza Andrade
- Brazilian Agricultural Research Corporation/National Institute of Science and Technology - Synthetic Biology, Brasília, Brazil.,Department of Cell Biology, Institute of Biological Sciences, University of Brasília, Brasília, Brazil
| | - Claire Grierson
- Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom.,School of Biological Sciences, University of Bristol, Bristol, United Kingdom
| | - Stefan Andreas Hoffmann
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
| | - Sophie Landon
- Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.,Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom
| | - Elibio Rech
- Brazilian Agricultural Research Corporation/National Institute of Science and Technology - Synthetic Biology, Brasília, Brazil
| | - Joshua Rees-Garbutt
- Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom.,School of Biological Sciences, University of Bristol, Bristol, United Kingdom
| | - Richard Seabrook
- Elizabeth Blackwell Institute for Health Research (EBI), University of Bristol, Bristol, United Kingdom
| | - William Shaw
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Christopher Woods
- Bristol Centre for Synthetic Biology (BrisSynBio), University of Bristol, Bristol, United Kingdom.,School of Chemistry, University of Bristol, Bristol, United Kingdom
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16
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Goodsell DS, Olson AJ, Forli S. Art and Science of the Cellular Mesoscale. Trends Biochem Sci 2020; 45:472-483. [PMID: 32413324 PMCID: PMC7230070 DOI: 10.1016/j.tibs.2020.02.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 02/12/2020] [Accepted: 02/27/2020] [Indexed: 12/22/2022]
Abstract
Experimental information from microscopy, structural biology, and bioinformatics may be integrated to build structural models of entire cells with molecular detail. This integrative modeling is challenging in several ways: the intrinsic complexity of biology results in models with many closely packed and heterogeneous components; the wealth of available experimental data is scattered among multiple resources and must be gathered, reconciled, and curated; and computational infrastructure is only now gaining the capability of modeling and visualizing systems of this complexity. We present recent efforts to address these challenges, both with artistic approaches to depicting the cellular mesoscale, and development and application of methods to build quantitative models.
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Affiliation(s)
- David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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17
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Validation of Modeling and Simulation Methods in Computational Biology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020. [PMID: 32468548 DOI: 10.1007/978-3-030-32622-7_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
In recent years, a highly sophisticated array of modeling and simulation tools in all areas of biological and biomedical research has been developed. These tools have the potential to provide new insights into biological mechanisms integrating subcellular, cellular, tissue, organ, and potentially whole organism levels. Current research is focused on how to use these methods for translational medical research, such as for disease diagnosis and understanding, as well as drug discovery. In addition, these approaches enhance the ability to use human-derived data and to contribute to the refinement of high-cost experimental-based research. Additionally, the conflicting conceptual frameworks and conceptions of modeling and simulation methods from the broad public of users could have a significant impact on the successful implementation of aforementioned applications. This in turn could result in successful collaborations across academic, clinical, and industrial sectors. To that end, this study provides an overview of the frameworks and disciplines used for validation of computational methodologies in biomedical sciences.
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18
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Physically-plausible modelling of biomolecular systems: A simplified, energy-based model of the mitochondrial electron transport chain. J Theor Biol 2020; 493:110223. [PMID: 32119969 DOI: 10.1016/j.jtbi.2020.110223] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 01/21/2020] [Accepted: 02/27/2020] [Indexed: 11/20/2022]
Abstract
Advances in systems biology and whole-cell modelling demand increasingly comprehensive mathematical models of cellular biochemistry. Such models require the development of simplified representations of specific processes which capture essential biophysical features but without unnecessarily complexity. Recently there has been renewed interest in thermodynamically-based modelling of cellular processes. Here we present an approach to developing of simplified yet thermodynamically consistent (hence physically plausible) models which can readily be incorporated into large scale biochemical descriptions but which do not require full mechanistic detail of the underlying processes. We illustrate the approach through development of a simplified, physically plausible model of the mitochondrial electron transport chain and show that the simplified model behaves like the full system.
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19
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Abstract
Comprehensive data about the composition and structure of cellular components have enabled the construction of quantitative whole-cell models. While kinetic network-type models have been established, it is also becoming possible to build physical, molecular-level models of cellular environments. This review outlines challenges in constructing and simulating such models and discusses near- and long-term opportunities for developing physical whole-cell models that can connect molecular structure with biological function.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA;
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
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20
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Millar AJ, Urquiza U, Freeman PL, Hume A, Plotkin GD, Sorokina O, Zardilis A, Zielinski T. Practical steps to digital organism models, from laboratory model species to 'Crops in silico. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2403-2418. [PMID: 30615184 DOI: 10.1093/jxb/ery435] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 11/28/2018] [Indexed: 05/20/2023]
Abstract
A recent initiative named 'Crops in silico' proposes that multi-scale models 'have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts' in plant science, particularly directed to crop species. To that end, the group called for 'a paradigm shift in plant modelling, from largely isolated efforts to a connected community'. 'Wet' (experimental) research has been especially productive in plant science, since the adoption of Arabidopsis thaliana as a laboratory model species allowed the emergence of an Arabidopsis research community. Parts of this community invested in 'dry' (theoretical) research, under the rubric of Systems Biology. Our past research combined concepts from Systems Biology and crop modelling. Here we outline the approaches that seem most relevant to connected, 'digital organism' initiatives. We illustrate the scale of experimental research required, by collecting the kinetic parameter values that are required for a quantitative, dynamic model of a gene regulatory network. By comparison with the Systems Biology Markup Language (SBML) community, we note computational resources and community structures that will help to realize the potential for plant Systems Biology to connect with a broader crop science community.
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Affiliation(s)
- Andrew J Millar
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Uriel Urquiza
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Alastair Hume
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
- EPCC, Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Gordon D Plotkin
- Laboratory for the Foundations of Computer Science, School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Oxana Sorokina
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Argyris Zardilis
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Tomasz Zielinski
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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21
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Shin S, Venturelli OS, Zavala VM. Scalable nonlinear programming framework for parameter estimation in dynamic biological system models. PLoS Comput Biol 2019; 15:e1006828. [PMID: 30908479 PMCID: PMC6467427 DOI: 10.1371/journal.pcbi.1006828] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 04/16/2019] [Accepted: 01/30/2019] [Indexed: 12/31/2022] Open
Abstract
We present a nonlinear programming (NLP) framework for the scalable solution of parameter estimation problems that arise in dynamic modeling of biological systems. Such problems are computationally challenging because they often involve highly nonlinear and stiff differential equations as well as many experimental data sets and parameters. The proposed framework uses cutting-edge modeling and solution tools which are computationally efficient, robust, and easy-to-use. Specifically, our framework uses a time discretization approach that: i) avoids repetitive simulations of the dynamic model, ii) enables fully algebraic model implementations and computation of derivatives, and iii) enables the use of computationally efficient nonlinear interior point solvers that exploit sparse and structured linear algebra techniques. We demonstrate these capabilities by solving estimation problems for synthetic human gut microbiome community models. We show that an instance with 156 parameters, 144 differential equations, and 1,704 experimental data points can be solved in less than 3 minutes using our proposed framework (while an off-the-shelf simulation-based solution framework requires over 7 hours). We also create large instances to show that the proposed framework is scalable and can solve problems with up to 2,352 parameters, 2,304 differential equations, and 20,352 data points in less than 15 minutes. The proposed framework is flexible and easy-to-use, can be broadly applied to dynamic models of biological systems, and enables the implementation of sophisticated estimation techniques to quantify parameter uncertainty, to diagnose observability/uniqueness issues, to perform model selection, and to handle outliers. Constructing and validating dynamic models of biological systems spanning biomolecular networks to ecological systems is a challenging problem. Here we present a scalable computational framework to rapidly infer parameters in complex dynamic models of biological systems from large-scale experimental data. The framework was applied to infer parameters of a synthetic microbial community model from large-scale time series data. We also demonstrate that this framework can be used to analyze parameter uncertainty, to diagnose whether the experimental data are sufficient to uniquely determine the parameters, to determine the model that best describes the data, and to infer parameters in the face of data outliers.
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Affiliation(s)
- Sungho Shin
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ophelia S. Venturelli
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Victor M. Zavala
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- * E-mail:
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22
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An energetic reformulation of kinetic rate laws enables scalable parameter estimation for biochemical networks. J Theor Biol 2019; 461:145-156. [DOI: 10.1016/j.jtbi.2018.10.041] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 09/20/2018] [Accepted: 10/19/2018] [Indexed: 11/18/2022]
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23
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Kamm RD, Bashir R, Arora N, Dar RD, Gillette MU, Griffith LG, Kemp ML, Kinlaw K, Levin M, Martin AC, McDevitt TC, Nerem RM, Powers MJ, Saif TA, Sharpe J, Takayama S, Takeuchi S, Weiss R, Ye K, Yevick HG, Zaman MH. Perspective: The promise of multi-cellular engineered living systems. APL Bioeng 2018; 2:040901. [PMID: 31069321 PMCID: PMC6481725 DOI: 10.1063/1.5038337] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/18/2018] [Indexed: 12/31/2022] Open
Abstract
Recent technological breakthroughs in our ability to derive and differentiate induced pluripotent stem cells, organoid biology, organ-on-chip assays, and 3-D bioprinting have all contributed to a heightened interest in the design, assembly, and manufacture of living systems with a broad range of potential uses. This white paper summarizes the state of the emerging field of "multi-cellular engineered living systems," which are composed of interacting cell populations. Recent accomplishments are described, focusing on current and potential applications, as well as barriers to future advances, and the outlook for longer term benefits and potential ethical issues that need to be considered.
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Affiliation(s)
- Roger D. Kamm
- Massachusetts Institute of Technology, Boston, Massachusetts 02139, USA
| | - Rashid Bashir
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61820, USA
| | - Natasha Arora
- Massachusetts Institute of Technology, Boston, Massachusetts 02139, USA
| | - Roy D. Dar
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61820, USA
| | | | - Linda G. Griffith
- Massachusetts Institute of Technology, Boston, Massachusetts 02139, USA
| | - Melissa L. Kemp
- Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | | | | | - Adam C. Martin
- Massachusetts Institute of Technology, Boston, Massachusetts 02139, USA
| | | | - Robert M. Nerem
- Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Mark J. Powers
- Thermo Fisher Scientific, Frederick, Maryland 21704, USA
| | - Taher A. Saif
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61820, USA
| | - James Sharpe
- EMBL Barcelona, European Molecular Biology Laboratory, Barcelona 08003, Spain
| | | | | | - Ron Weiss
- Massachusetts Institute of Technology, Boston, Massachusetts 02139, USA
| | - Kaiming Ye
- Binghamton University, Binghamton, New York 13902, USA
| | - Hannah G. Yevick
- Massachusetts Institute of Technology, Boston, Massachusetts 02139, USA
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24
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Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 2018; 173:11-19. [PMID: 29570991 PMCID: PMC6014618 DOI: 10.1016/j.cell.2018.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 11/25/2017] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.
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Affiliation(s)
- Jitin Singla
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kyle M McClary
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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25
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Banos DT, Trébulle P, Elati M. Integrating transcriptional activity in genome-scale models of metabolism. BMC SYSTEMS BIOLOGY 2017; 11:134. [PMID: 29322933 PMCID: PMC5763306 DOI: 10.1186/s12918-017-0507-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype. Results We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods. Conclusions Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.
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Affiliation(s)
- Daniel Trejo Banos
- UMR 8030 Génomique Métabolique / Laboratoire iSSB CEA-CNRS-UEVE, Genopole campus 1, 5 rue Henri Desbruères, Cedex Évry, 91030, France
| | - Pauline Trébulle
- UMR 8030 Génomique Métabolique / Laboratoire iSSB CEA-CNRS-UEVE, Genopole campus 1, 5 rue Henri Desbruères, Cedex Évry, 91030, France.,Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, 78350, France
| | - Mohamed Elati
- UMR 8030 Génomique Métabolique / Laboratoire iSSB CEA-CNRS-UEVE, Genopole campus 1, 5 rue Henri Desbruères, Cedex Évry, 91030, France. .,Université Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Lille, F-59000, France.
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26
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Goldberg AP, Szigeti B, Chew YH, Sekar JA, Roth YD, Karr JR. Emerging whole-cell modeling principles and methods. Curr Opin Biotechnol 2017; 51:97-102. [PMID: 29275251 DOI: 10.1016/j.copbio.2017.12.013] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 12/07/2017] [Accepted: 12/11/2017] [Indexed: 11/16/2022]
Abstract
Whole-cell computational models aim to predict cellular phenotypes from genotype by representing the entire genome, the structure and concentration of each molecular species, each molecular interaction, and the extracellular environment. Whole-cell models have great potential to transform bioscience, bioengineering, and medicine. However, numerous challenges remain to achieve whole-cell models. Nevertheless, researchers are beginning to leverage recent progress in measurement technology, bioinformatics, data sharing, rule-based modeling, and multi-algorithmic simulation to build the first whole-cell models. We anticipate that ongoing efforts to develop scalable whole-cell modeling tools will enable dramatically more comprehensive and more accurate models, including models of human cells.
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Affiliation(s)
- Arthur P Goldberg
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Balázs Szigeti
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yin Hoon Chew
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John Ap Sekar
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yosef D Roth
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan R Karr
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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27
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Szigeti B, Roth YD, Sekar JAP, Goldberg AP, Pochiraju SC, Karr JR. A blueprint for human whole-cell modeling. ACTA ACUST UNITED AC 2017; 7:8-15. [PMID: 29806041 DOI: 10.1016/j.coisb.2017.10.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Whole-cell dynamical models of human cells are a central goal of systems biology. Such models could help researchers understand cell biology and help physicians treat disease. Despite significant challenges, we believe that human whole-cell models are rapidly becoming feasible. To develop a plan for achieving human whole-cell models, we analyzed the existing models of individual cellular pathways, surveyed the biomodeling community, and reflected on our experience developing whole-cell models of bacteria. Based on these analyses, we propose a plan for a project, termed the Human Whole-Cell Modeling Project, to achieve human whole-cell models. The foundations of the plan include technology development, standards development, and interdisciplinary collaboration.
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Affiliation(s)
- Balázs Szigeti
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Yosef D Roth
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - John A P Sekar
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Arthur P Goldberg
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Saahith C Pochiraju
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Jonathan R Karr
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
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28
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Bardini R, Politano G, Benso A, Di Carlo S. Multi-level and hybrid modelling approaches for systems biology. Comput Struct Biotechnol J 2017; 15:396-402. [PMID: 28855977 PMCID: PMC5565741 DOI: 10.1016/j.csbj.2017.07.005] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 06/28/2017] [Accepted: 07/31/2017] [Indexed: 01/27/2023] Open
Abstract
During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.
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Affiliation(s)
| | | | | | - S. Di Carlo
- Politecnico di Torino, Department of Control and Computer Engineering, 10129 Torino, Italy
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29
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Babtie AC, Stumpf MPH. How to deal with parameters for whole-cell modelling. J R Soc Interface 2017; 14:20170237. [PMID: 28768879 PMCID: PMC5582120 DOI: 10.1098/rsif.2017.0237] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 06/22/2017] [Indexed: 11/12/2022] Open
Abstract
Dynamical systems describing whole cells are on the verge of becoming a reality. But as models of reality, they are only useful if we have realistic parameters for the molecular reaction rates and cell physiological processes. There is currently no suitable framework to reliably estimate hundreds, let alone thousands, of reaction rate parameters. Here, we map out the relative weaknesses and promises of different approaches aimed at redressing this issue. While suitable procedures for estimation or inference of the whole (vast) set of parameters will, in all likelihood, remain elusive, some hope can be drawn from the fact that much of the cellular behaviour may be explained in terms of smaller sets of parameters. Identifying such parameter sets and assessing their behaviour is now becoming possible even for very large systems of equations, and we expect such methods to become central tools in the development and analysis of whole-cell models.
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Affiliation(s)
- Ann C Babtie
- Department of Life Sciences, Imperial College London, London, UK
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30
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Gábor A, Villaverde AF, Banga JR. Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems. BMC SYSTEMS BIOLOGY 2017; 11:54. [PMID: 28476119 PMCID: PMC5420165 DOI: 10.1186/s12918-017-0428-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 04/25/2017] [Indexed: 01/13/2023]
Abstract
Background Kinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges. Methods We present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph. Results Our contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub (https://github.com/gabora/visid). Conclusions Our approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0428-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Attila Gábor
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.,JRC-COMBINE, RWTH Aachen University, Photonics Cluster, Level 4, Campus-Boulevard 79, Aachen, 52074, Germany
| | | | - Julio R Banga
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.
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31
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Biomedical applications of cell- and tissue-specific metabolic network models. J Biomed Inform 2017; 68:35-49. [DOI: 10.1016/j.jbi.2017.02.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 02/21/2017] [Accepted: 02/23/2017] [Indexed: 12/17/2022]
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32
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Chiappino-Pepe A, Pandey V, Ataman M, Hatzimanikatis V. Integration of metabolic, regulatory and signaling networks towards analysis of perturbation and dynamic responses. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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33
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Menon G, Krishnan J. Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules. J Chem Phys 2017; 145:035103. [PMID: 27448907 DOI: 10.1063/1.4953914] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.
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Affiliation(s)
- Govind Menon
- Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - J Krishnan
- Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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34
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Penas DR, González P, Egea JA, Doallo R, Banga JR. Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy. BMC Bioinformatics 2017; 18:52. [PMID: 28109249 PMCID: PMC5251293 DOI: 10.1186/s12859-016-1452-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 12/24/2016] [Indexed: 12/02/2022] Open
Abstract
Background The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. Results The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. Conclusions The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1452-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- David R Penas
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain
| | - Patricia González
- Computer Architecture Group, Universidade da Coruña, Campus de Elviña s/n, Coruña, 15071 A, Spain
| | - Jose A Egea
- Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, c/ Dr. Fleming s/n, Cartagena, 30202, Spain
| | - Ramón Doallo
- Computer Architecture Group, Universidade da Coruña, Campus de Elviña s/n, Coruña, 15071 A, Spain
| | - Julio R Banga
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.
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35
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Kim M, Rai N, Zorraquino V, Tagkopoulos I. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nat Commun 2016; 7:13090. [PMID: 27713404 PMCID: PMC5059772 DOI: 10.1038/ncomms13090] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 09/01/2016] [Indexed: 12/20/2022] Open
Abstract
A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery. Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coli proteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.
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Affiliation(s)
- Minseung Kim
- Department of Computer Science, University of California, Davis, California 95616, USA.,Genome Center, University of California, Davis, California 95616, USA
| | - Navneet Rai
- Genome Center, University of California, Davis, California 95616, USA
| | | | - Ilias Tagkopoulos
- Department of Computer Science, University of California, Davis, California 95616, USA.,Genome Center, University of California, Davis, California 95616, USA
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36
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Waltemath D, Karr JR, Bergmann FT, Chelliah V, Hucka M, Krantz M, Liebermeister W, Mendes P, Myers CJ, Pir P, Alaybeyoglu B, Aranganathan NK, Baghalian K, Bittig AT, Burke PEP, Cantarelli M, Chew YH, Costa RS, Cursons J, Czauderna T, Goldberg AP, Gomez HF, Hahn J, Hameri T, Gardiol DFH, Kazakiewicz D, Kiselev I, Knight-Schrijver V, Knupfer C, Konig M, Lee D, Lloret-Villas A, Mandrik N, Medley JK, Moreau B, Naderi-Meshkin H, Palaniappan SK, Priego-Espinosa D, Scharm M, Sharma M, Smallbone K, Stanford NJ, Song JH, Theile T, Tokic M, Tomar N, Toure V, Uhlendorf J, Varusai TM, Watanabe LH, Wendland F, Wolfien M, Yurkovich JT, Zhu Y, Zardilis A, Zhukova A, Schreiber F. Toward Community Standards and Software for Whole-Cell Modeling. IEEE Trans Biomed Eng 2016; 63:2007-14. [PMID: 27305665 PMCID: PMC5451320 DOI: 10.1109/tbme.2016.2560762] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. METHODS We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. RESULTS Our analysis revealed several challenges to representing WC models using the current standards. CONCLUSION We, therefore, propose several new WC modeling standards, software, and databases. SIGNIFICANCE We anticipate that these new standards and software will enable more comprehensive models.
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Somogyi E, Sluka JP, Glazier JA. Formalizing Knowledge in Multi-Scale Agent-Based Simulations. MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS : ... INTERNATIONAL CONFERENCE, MODELS ... : PROCEEDINGS. MODELS (CONFERENCE) 2016; 16:115-122. [PMID: 29338063 PMCID: PMC5761068 DOI: 10.1145/2976767.2976790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused.
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Affiliation(s)
- Endre Somogyi
- Department of Computer Science, Biocomplexity Institute Indiana University Bloomington, IN 47405
| | - James P Sluka
- Department of Intelligent Systems Engineering Biocomplexity Institute Indiana University Bloomington, IN 47405
| | - James A Glazier
- Department of Intelligent Systems Engineering Biocomplexity Institute Indiana University Bloomington, IN 47405
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38
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Sarma GP, Jacobs TW, Watts MD, Ghayoomie SV, Larson SD, Gerkin RC. Unit testing, model validation, and biological simulation. F1000Res 2016; 5:1946. [PMID: 27635225 PMCID: PMC5007758 DOI: 10.12688/f1000research.9315.1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/02/2016] [Indexed: 11/24/2022] Open
Abstract
The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in
OpenWorm, an open-science project aimed at modeling
Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models.
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Affiliation(s)
- Gopal P Sarma
- School of Medicine, Emory University, Atlanta, USA.,OpenWorm Foundation, Boston, USA
| | - Travis W Jacobs
- Department of Bioengineering, Imperial College London, London, UK.,OpenWorm Foundation, Boston, USA
| | - Mark D Watts
- The University of Texas at Austin, Austin, USA.,OpenWorm Foundation, Boston, USA
| | - S Vahid Ghayoomie
- Laboratory of Systems Biology and Bioinformatics, University of Tehran, Tehran, Iran.,OpenWorm Foundation, Boston, USA
| | | | - Richard C Gerkin
- School of Life Sciences, Arizona State University, Tempe, USA.,OpenWorm Foundation, Boston, USA
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39
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Medley JK, Goldberg AP, Karr JR. Guidelines for Reproducibly Building and Simulating Systems Biology Models. IEEE Trans Biomed Eng 2016; 63:2015-20. [PMID: 27429432 DOI: 10.1109/tbme.2016.2591960] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
OBJECTIVE Reproducibility is the cornerstone of the scientific method. However, currently, many systems biology models cannot easily be reproduced. This paper presents methods that address this problem. METHODS We analyzed the recent Mycoplasma genitalium whole-cell (WC) model to determine the requirements for reproducible modeling. RESULTS We determined that reproducible modeling requires both repeatable model building and repeatable simulation. CONCLUSION New standards and simulation software tools are needed to enhance and verify the reproducibility of modeling. New standards are needed to explicitly document every data source and assumption, and new deterministic parallel simulation tools are needed to quickly simulate large, complex models. SIGNIFICANCE We anticipate that these new standards and software will enable researchers to reproducibly build and simulate more complex models, including WC models.
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40
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Niu F, Wang DC, Lu J, Wu W, Wang X. Potentials of single-cell biology in identification and validation of disease biomarkers. J Cell Mol Med 2016; 20:1789-95. [PMID: 27113384 PMCID: PMC4988278 DOI: 10.1111/jcmm.12868] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Accepted: 03/10/2016] [Indexed: 12/23/2022] Open
Abstract
Single-cell biology is considered a new approach to identify and validate disease-specific biomarkers. However, the concern raised by clinicians is how to apply single-cell measurements for clinical practice, translate the message of single-cell systems biology into clinical phenotype or explain alterations of single-cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single-cell gene sequencing in the identification and development of disease-specific biomarkers, the definition and significance of single-cell biology and single-cell systems biology in the understanding of single-cell full picture, the development and establishment of whole-cell models in the validation of targeted biological function and the figure and meaning of single-molecule imaging in single cell to trace intra-single-cell molecule expression, signal, interaction and location. We headline the important role of single-cell biology in the discovery and development of disease-specific biomarkers with a special emphasis on understanding single-cell biological functions, e.g. mechanical phenotypes, single-cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi-dimensional, multi-layer, multi-crossing and stereoscopic single-cell biology definitely benefits the discovery and development of disease-specific biomarkers.
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Affiliation(s)
- Furong Niu
- Huzhou Central Hospital, Huzhou, Zhejiang Province, China
| | - Diane C Wang
- Department of Pulmonary Medicine, The First affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Jiapei Lu
- Department of Pulmonary Medicine, The First affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Wei Wu
- Huzhou Central Hospital, Huzhou, Zhejiang Province, China
| | - Xiangdong Wang
- Huzhou Central Hospital, Huzhou, Zhejiang Province, China
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41
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Gasser RB, Schwarz EM, Korhonen PK, Young ND. Understanding Haemonchus contortus Better Through Genomics and Transcriptomics. ADVANCES IN PARASITOLOGY 2016; 93:519-67. [PMID: 27238012 DOI: 10.1016/bs.apar.2016.02.015] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Parasitic roundworms (nematodes) cause substantial mortality and morbidity in animals globally. The barber's pole worm, Haemonchus contortus, is one of the most economically significant parasitic nematodes of small ruminants worldwide. Although this and related nematodes can be controlled relatively well using anthelmintics, resistance against most drugs in common use has become a major problem. Until recently, almost nothing was known about the molecular biology of H. contortus on a global scale. This chapter gives a brief background on H. contortus and haemonchosis, immune responses, vaccine research, chemotherapeutics and current problems associated with drug resistance. It also describes progress in transcriptomics before the availability of H. contortus genomes and the challenges associated with such work. It then reviews major progress on the two draft genomes and developmental transcriptomes of H. contortus, and summarizes their implications for the molecular biology of this worm in both the free-living and the parasitic stages of its life cycle. The chapter concludes by considering how genomics and transcriptomics can accelerate research on Haemonchus and related parasites, and can enable the development of new interventions against haemonchosis.
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Affiliation(s)
- R B Gasser
- The University of Melbourne, Parkville, VIC, Australia
| | - E M Schwarz
- The University of Melbourne, Parkville, VIC, Australia; Cornell University, Ithaca, NY, United States
| | - P K Korhonen
- The University of Melbourne, Parkville, VIC, Australia
| | - N D Young
- The University of Melbourne, Parkville, VIC, Australia
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42
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Smith C, Pechuan X, Puzio RS, Biro D, Bergman A. Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection towards hierarchical architectures. J R Soc Interface 2016; 12:20150179. [PMID: 26040595 PMCID: PMC4528583 DOI: 10.1098/rsif.2015.0179] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Constraints placed upon the phenotypes of organisms result from their interactions with the environment. Over evolutionary time scales, these constraints feed back onto smaller molecular subnetworks comprising the organism. The evolution of biological networks is studied by considering a network of a few nodes embedded in a larger context. Taking into account this fact that any network under study is actually embedded in a larger context, we define network architecture, not on the basis of physical interactions alone, but rather as a specification of the manner in which constraints are placed upon the states of its nodes. We show that such network architectures possessing cycles in their topology, in contrast to those that do not, may be subjected to unsatisfiable constraints. This may be a significant factor leading to selection biased against those network architectures where such inconsistent constraints are more likely to arise. We proceed to quantify the likelihood of inconsistency arising as a function of network architecture finding that, in the absence of sampling bias over the space of possible constraints and for a given network size, networks with a larger number of cycles are more likely to have unsatisfiable constraints placed upon them. Our results identify a constraint that, at least in isolation, would contribute to a bias in the evolutionary process towards more hierarchical -modular versus completely connected network architectures. Together, these results highlight the context dependence of the functionality of biological networks.
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Affiliation(s)
- Cameron Smith
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA
| | - Ximo Pechuan
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA
| | - Raymond S Puzio
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA
| | - Daniel Biro
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA
| | - Aviv Bergman
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA Department of Pathology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY 10461, USA Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
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43
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Abstract
Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.
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Affiliation(s)
- Michael A Henson
- Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003, U.S.A.
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44
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Gábor A, Banga JR. Robust and efficient parameter estimation in dynamic models of biological systems. BMC SYSTEMS BIOLOGY 2015; 9:74. [PMID: 26515482 PMCID: PMC4625902 DOI: 10.1186/s12918-015-0219-2] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2015] [Accepted: 10/08/2015] [Indexed: 11/16/2022]
Abstract
Background Dynamic modelling provides a systematic framework to understand function in biological systems. Parameter estimation in nonlinear dynamic models remains a very challenging inverse problem due to its nonconvexity and ill-conditioning. Associated issues like overfitting and local solutions are usually not properly addressed in the systems biology literature despite their importance. Here we present a method for robust and efficient parameter estimation which uses two main strategies to surmount the aforementioned difficulties: (i) efficient global optimization to deal with nonconvexity, and (ii) proper regularization methods to handle ill-conditioning. In the case of regularization, we present a detailed critical comparison of methods and guidelines for properly tuning them. Further, we show how regularized estimations ensure the best trade-offs between bias and variance, reducing overfitting, and allowing the incorporation of prior knowledge in a systematic way. Results We illustrate the performance of the presented method with seven case studies of different nature and increasing complexity, considering several scenarios of data availability, measurement noise and prior knowledge. We show how our method ensures improved estimations with faster and more stable convergence. We also show how the calibrated models are more generalizable. Finally, we give a set of simple guidelines to apply this strategy to a wide variety of calibration problems. Conclusions Here we provide a parameter estimation strategy which combines efficient global optimization with a regularization scheme. This method is able to calibrate dynamic models in an efficient and robust way, effectively fighting overfitting and allowing the incorporation of prior information. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0219-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Attila Gábor
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.
| | - Julio R Banga
- BioProcess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.
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45
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Schindler D, Waldminghaus T. Synthetic chromosomes. FEMS Microbiol Rev 2015; 39:871-91. [DOI: 10.1093/femsre/fuv030] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2015] [Indexed: 12/22/2022] Open
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46
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Karr JR, Takahashi K, Funahashi A. The principles of whole-cell modeling. Curr Opin Microbiol 2015; 27:18-24. [PMID: 26115539 DOI: 10.1016/j.mib.2015.06.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 05/25/2015] [Accepted: 06/05/2015] [Indexed: 11/17/2022]
Abstract
Whole-cell models which comprehensively predict how phenotypes emerge from genotype promise to enable rational bioengineering and precision medicine. Here, we outline the key principles of whole-cell modeling which have emerged from our work developing bacterial whole-cell models: single-cellularity; functional, genetic, molecular, and temporal completeness; biophysical realism including temporal dynamics and stochastic variation; species-specificity; and model integration and reproducibility. We also outline the whole-cell model construction process, highlighting existing resources. Numerous challenges remain to achieving fully complete models including developing new experimental tools to more completely characterize cells and developing a strong theoretical understanding of hybrid mathematics. Solving these challenges requires collaboration among computational and experimental biologists, biophysicists, biochemists, applied mathematicians, computer scientists, and software engineers.
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Affiliation(s)
- Jonathan R Karr
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Koichi Takahashi
- RIKEN Quantitative Biology Center, RIKEN, Osaka 565-0874, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan
| | - Akira Funahashi
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan
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47
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Wätzig H, Oltmann-Norden I, Steinicke F, Alhazmi HA, Nachbar M, El-Hady DA, Albishri HM, Baumann K, Exner T, Böckler FM, El Deeb S. Data quality in drug discovery: the role of analytical performance in ligand binding assays. J Comput Aided Mol Des 2015; 29:847-65. [DOI: 10.1007/s10822-015-9851-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 06/02/2015] [Indexed: 01/24/2023]
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48
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Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics. Methods 2015; 85:12-21. [PMID: 26079925 DOI: 10.1016/j.ymeth.2015.06.009] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2015] [Revised: 06/06/2015] [Accepted: 06/08/2015] [Indexed: 11/21/2022] Open
Abstract
The production and degradation of RNA transcripts is inherently subject to biological noise that arises from small gene copy numbers in individual cells. As a result, cellular RNA levels can exhibit large fluctuations over time and from one cell to the next. This article presents a range of precise single-molecule experimental techniques, based upon RNA fluorescence in situ hybridization, which can be used to measure the fluctuations of RNA at the single-cell level. A class of models for gene activation and deactivation is postulated in order to capture complex stochastic effects of chromatin modifications or transcription factor interactions. A computational tool, known as the finite state projection approach, is introduced to accurately and efficiently analyze these models in order to predict how probability distributions of RNA change over time in response to changing environmental conditions. These single-molecule experiments, discrete stochastic models, and computational analyses are systematically integrated to identify models of gene regulation dynamics. To illustrate the power and generality of our integrated experimental and computational approach, we explore cases that include different models for three different RNA types (sRNA, mRNA and nascent RNA), three different experimental techniques and three different biological species (bacteria, yeast and human cells).
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Feig M, Harada R, Mori T, Yu I, Takahashi K, Sugita Y. Complete atomistic model of a bacterial cytoplasm for integrating physics, biochemistry, and systems biology. J Mol Graph Model 2015; 58:1-9. [PMID: 25765281 DOI: 10.1016/j.jmgm.2015.02.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 02/18/2015] [Accepted: 02/22/2015] [Indexed: 01/10/2023]
Abstract
A model for the cytoplasm of Mycoplasma genitalium is presented that integrates data from a variety of sources into a physically and biochemically consistent model. Based on gene annotations, core genes expected to be present in the cytoplasm were determined and a metabolic reaction network was reconstructed. The set of cytoplasmic genes and metabolites from the predicted reactions were assembled into a comprehensive atomistic model consisting of proteins with predicted structures, RNA, protein/RNA complexes, metabolites, ions, and solvent. The resulting model bridges between atomistic and cellular scales, between physical and biochemical aspects, and between structural and systems views of cellular systems and is meant as a starting point for a variety of simulation studies.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI 48824, United States; Department of Chemistry, Michigan State University, East Lansing, MI 48824, United States; Quantitative Biology Center, RIKEN, International Medical Device Alliance (IMDA) 6F, 1-6-5 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
| | - Ryuhei Harada
- Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Quantitative Biology Center, RIKEN, International Medical Device Alliance (IMDA) 6F, 1-6-5 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
| | - Takaharu Mori
- Quantitative Biology Center, RIKEN, International Medical Device Alliance (IMDA) 6F, 1-6-5 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Theoretical Molecular Science Laboratory and iTHES, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Isseki Yu
- Theoretical Molecular Science Laboratory and iTHES, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Koichi Takahashi
- Quantitative Biology Center, RIKEN, Laboratory for Biochemical Simulation, Suita, Osaka 565-0874, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan
| | - Yuji Sugita
- Advanced Institute for Computational Science, RIKEN, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Quantitative Biology Center, RIKEN, International Medical Device Alliance (IMDA) 6F, 1-6-5 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan; Theoretical Molecular Science Laboratory and iTHES, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
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Karr JR, Phillips NC, Covert MW. WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau095. [PMID: 25231498 PMCID: PMC4165886 DOI: 10.1093/database/bau095] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Mechanistic ‘whole-cell’ models are needed to develop a complete understanding of cell physiology. However, extracting biological insights from whole-cell models requires running and analyzing large numbers of simulations. We developed WholeCellSimDB, a database for organizing whole-cell simulations. WholeCellSimDB was designed to enable researchers to search simulation metadata to identify simulations for further analysis, and quickly slice and aggregate simulation results data. In addition, WholeCellSimDB enables users to share simulations with the broader research community. The database uses a hybrid relational/hierarchical data format architecture to efficiently store and retrieve both simulation setup metadata and results data. WholeCellSimDB provides a graphical Web-based interface to search, browse, plot and export simulations; a JavaScript Object Notation (JSON) Web service to retrieve data for Web-based visualizations; a command-line interface to deposit simulations; and a Python API to retrieve data for advanced analysis. Overall, we believe WholeCellSimDB will help researchers use whole-cell models to advance basic biological science and bioengineering. Database URL:http://www.wholecellsimdb.org Source code repository URL:http://github.com/CovertLab/WholeCellSimDB
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Affiliation(s)
- Jonathan R Karr
- Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA, Computer Science and Information Technology, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada and Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Nolan C Phillips
- Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA, Computer Science and Information Technology, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada and Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Markus W Covert
- Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA, Computer Science and Information Technology, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada and Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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