1
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Lu H, Xiao L, Liao W, Yan X, Nielsen J. Cell factory design with advanced metabolic modelling empowered by artificial intelligence. Metab Eng 2024; 85:61-72. [PMID: 39038602 DOI: 10.1016/j.ymben.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/06/2024] [Accepted: 07/06/2024] [Indexed: 07/24/2024]
Abstract
Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.
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Affiliation(s)
- Hongzhong Lu
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
| | - Luchi Xiao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Wenbin Liao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Xuefeng Yan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Jens Nielsen
- BioInnovation Institute, Ole Måløes Vej, DK2200, Copenhagen N, Denmark; Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden.
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2
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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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3
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Rafelski SM, Theriot JA. Establishing a conceptual framework for holistic cell states and state transitions. Cell 2024; 187:2633-2651. [PMID: 38788687 DOI: 10.1016/j.cell.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/10/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024]
Abstract
Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.
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Affiliation(s)
- Susanne M Rafelski
- Allen Institute for Cell Science, 615 Westlake Avenue N, Seattle, WA 98125, USA.
| | - Julie A Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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4
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Gilbert BR, Luthey-Schulten Z. Replicating Chromosomes in Whole-Cell Models of Bacteria. Methods Mol Biol 2024; 2819:625-653. [PMID: 39028527 DOI: 10.1007/978-1-0716-3930-6_29] [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] [Indexed: 07/20/2024]
Abstract
Computational models of cells cannot be considered complete unless they include the most fundamental process of life, the replication of genetic material. In a recent study, we presented a computational framework to model systems of replicating bacterial chromosomes as polymers at 10 bp resolution with Brownian dynamics. This approach was used to investigate changes in chromosome organization during replication and extend the applicability of an existing whole-cell model (WCM) for a genetically minimal bacterium, JCVI-syn3A, to the entire cell cycle. To achieve cell-scale chromosome structures that are realistic, we modeled the chromosome as a self-avoiding homopolymer with bending and torsional stiffnesses that capture the essential mechanical properties of dsDNA in Syn3A. Additionally, the polymer interacts with ribosomes distributed according to cryo-electron tomograms of Syn3A. The polymer model was further augmented by computational models of loop extrusion by structural maintenance of chromosomes (SMC) protein complexes and topoisomerase action, and the modeling and analysis of multi-fork replication states.
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Affiliation(s)
- Benjamin R Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- NSF Science and Technology Center for Quantitative Cell Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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5
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Chew YH, Marucci L. Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology. Methods Mol Biol 2024; 2774:71-84. [PMID: 38441759 DOI: 10.1007/978-1-0716-3718-0_6] [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] [Indexed: 03/07/2024]
Abstract
Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.
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Affiliation(s)
- Yin Hoon Chew
- School of Mathematics, University of Birmingham, Birmingham, UK
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, UK.
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK.
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6
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Liu F, Heiner M, Gilbert D. Protocol for biomodel engineering of unilevel to multilevel biological models using colored Petri nets. STAR Protoc 2023; 4:102651. [PMID: 38103198 PMCID: PMC10751555 DOI: 10.1016/j.xpro.2023.102651] [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: 05/03/2023] [Revised: 07/26/2023] [Accepted: 09/27/2023] [Indexed: 12/18/2023] Open
Abstract
Biological systems inherently span multiple levels, which can pose challenges in spatial representation for modelers. We present a protocol that utilizes colored Petri nets to construct and analyze biological models of systems, encompassing both unilevel and multilevel scenarios. We detail a modeling workflow exploiting the PetriNuts platform comprising a set of tools linked together via common file formats. We describe steps for modeling preparation, component-level modeling and analysis, followed by system-level modeling and analysis, and model use.
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Affiliation(s)
- Fei Liu
- School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, P.R. China.
| | - Monika Heiner
- Department of Computing Science, Brandenburg University of Technology Cottbus-Senftenberg, D03013 Cottbus, Germany
| | - David Gilbert
- Department of Computing Science, Brunel University London, UB8 3PH London, UK
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7
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Bernstein DB, Akkas B, Price MN, Arkin AP. Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data. Mol Syst Biol 2023; 19:e11566. [PMID: 37888487 DOI: 10.15252/msb.202311566] [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: 02/01/2023] [Revised: 09/23/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.
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Affiliation(s)
- David B Bernstein
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Batu Akkas
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | - Morgan N Price
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Adam P Arkin
- Department of Bioengineering, University of California, Berkeley, CA, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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8
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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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9
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Gilbert BR, Thornburg ZR, Brier TA, Stevens JA, Grünewald F, Stone JE, Marrink SJ, Luthey-Schulten Z. Dynamics of chromosome organization in a minimal bacterial cell. Front Cell Dev Biol 2023; 11:1214962. [PMID: 37621774 PMCID: PMC10445541 DOI: 10.3389/fcell.2023.1214962] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 07/10/2023] [Indexed: 08/26/2023] Open
Abstract
Computational models of cells cannot be considered complete unless they include the most fundamental process of life, the replication and inheritance of genetic material. By creating a computational framework to model systems of replicating bacterial chromosomes as polymers at 10 bp resolution with Brownian dynamics, we investigate changes in chromosome organization during replication and extend the applicability of an existing whole-cell model (WCM) for a genetically minimal bacterium, JCVI-syn3A, to the entire cell-cycle. To achieve cell-scale chromosome structures that are realistic, we model the chromosome as a self-avoiding homopolymer with bending and torsional stiffnesses that capture the essential mechanical properties of dsDNA in Syn3A. In addition, the conformations of the circular DNA must avoid overlapping with ribosomes identitied in cryo-electron tomograms. While Syn3A lacks the complex regulatory systems known to orchestrate chromosome segregation in other bacteria, its minimized genome retains essential loop-extruding structural maintenance of chromosomes (SMC) protein complexes (SMC-scpAB) and topoisomerases. Through implementing the effects of these proteins in our simulations of replicating chromosomes, we find that they alone are sufficient for simultaneous chromosome segregation across all generations within nested theta structures. This supports previous studies suggesting loop-extrusion serves as a near-universal mechanism for chromosome organization within bacterial and eukaryotic cells. Furthermore, we analyze ribosome diffusion under the influence of the chromosome and calculate in silico chromosome contact maps that capture inter-daughter interactions. Finally, we present a methodology to map the polymer model of the chromosome to a Martini coarse-grained representation to prepare molecular dynamics models of entire Syn3A cells, which serves as an ultimate means of validation for cell states predicted by the WCM.
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Affiliation(s)
- Benjamin R. Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Zane R. Thornburg
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Troy A. Brier
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - 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
| | - John E. Stone
- NVIDIA Corporation, Santa Clara, CA, United States
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Siewert J. Marrink
- Molecular Dynamics Group, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- NSF Center for the Physics of Living Cells, Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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10
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Schäfer M, Pacheco AR, Künzler R, Bortfeld-Miller M, Field CM, Vayena E, Hatzimanikatis V, Vorholt JA. Metabolic interaction models recapitulate leaf microbiota ecology. Science 2023; 381:eadf5121. [PMID: 37410834 DOI: 10.1126/science.adf5121] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 05/18/2023] [Indexed: 07/08/2023]
Abstract
Resource allocation affects the structure of microbiomes, including those associated with living hosts. Understanding the degree to which this dependency determines interspecies interactions may advance efforts to control host-microbiome relationships. We combined synthetic community experiments with computational models to predict interaction outcomes between plant-associated bacteria. We mapped the metabolic capabilities of 224 leaf isolates from Arabidopsis thaliana by assessing the growth of each strain on 45 environmentally relevant carbon sources in vitro. We used these data to build curated genome-scale metabolic models for all strains, which we combined to simulate >17,500 interactions. The models recapitulated outcomes observed in planta with >89% accuracy, highlighting the role of carbon utilization and the contributions of niche partitioning and cross-feeding in the assembly of leaf microbiomes.
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Affiliation(s)
- Martin Schäfer
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Alan R Pacheco
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Rahel Künzler
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | | | | | - Evangelia Vayena
- 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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11
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Shin J, Porubsky V, Carothers J, Sauro HM. Standards, dissemination, and best practices in systems biology. Curr Opin Biotechnol 2023; 81:102922. [PMID: 37004298 PMCID: PMC10435326 DOI: 10.1016/j.copbio.2023.102922] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/14/2023] [Accepted: 02/24/2023] [Indexed: 04/03/2023]
Abstract
The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.
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Affiliation(s)
- Janis Shin
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA
| | - Veronica Porubsky
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - James Carothers
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
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12
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Dong H, Yang X, Shi J, Xiao C, Zhang Y. Exploring the Feasibility of Cell-Free Synthesis as a Platform for Polyhydroxyalkanoate (PHA) Production: Opportunities and Challenges. Polymers (Basel) 2023; 15:polym15102333. [PMID: 37242908 DOI: 10.3390/polym15102333] [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: 04/25/2023] [Revised: 05/12/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023] Open
Abstract
The extensive utilization of traditional petroleum-based plastics has resulted in significant damage to the natural environment and ecological systems, highlighting the urgent need for sustainable alternatives. Polyhydroxyalkanoates (PHAs) have emerged as promising bioplastics that can compete with petroleum-based plastics. However, their production technology currently faces several challenges, primarily focused on high costs. Cell-free biotechnologies have shown significant potential for PHA production; however, despite recent progress, several challenges still need to be overcome. In this review, we focus on the status of cell-free PHA synthesis and compare it with microbial cell-based PHA synthesis in terms of advantages and drawbacks. Finally, we present prospects for the development of cell-free PHA synthesis.
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Affiliation(s)
- Huaming Dong
- School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Xue Yang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Jingjing Shi
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Chunqiao Xiao
- School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China
| | - Yanfei Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
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13
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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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14
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Porubsky VL, Sauro HM. A Practical Guide to Reproducible Modeling for Biochemical Networks. Methods Mol Biol 2023; 2634:107-138. [PMID: 37074576 DOI: 10.1007/978-1-0716-3008-2_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
While scientific disciplines revere reproducibility, many studies - experimental and computational alike - fall short of this ideal and cannot be reproduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to support reproducibility. This chapter points the reader to useful software tools and standardized formats that support reproducible modeling of biochemical networks and provides suggestions on how to implement reproducible methods in practice. Many of the suggestions encourage readers to use best practices from the software development community in order to automate, test, and version control their model components. A Jupyter Notebook demonstrating several of the key steps in building a reproducible biochemical network model is included to supplement the recommendations in the text.
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Affiliation(s)
| | - Herbert M Sauro
- University of Washington, Department of Bioengineering, Seattle, WA, USA
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15
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Erdem C, Mutsuddy A, Bensman EM, Dodd WB, Saint-Antoine MM, Bouhaddou M, Blake RC, Gross SM, Heiser LM, Feltus FA, Birtwistle MR. A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling. Nat Commun 2022; 13:3555. [PMID: 35729113 PMCID: PMC9213456 DOI: 10.1038/s41467-022-31138-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 06/07/2022] [Indexed: 02/01/2023] Open
Abstract
Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.
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Affiliation(s)
- Cemal Erdem
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
| | - Arnab Mutsuddy
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Ethan M Bensman
- Computer Science, School of Computing, Clemson University, Clemson, SC, USA
| | - William B Dodd
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Michael M Saint-Antoine
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Mehdi Bouhaddou
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
| | - Robert C Blake
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Sean M Gross
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - F Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
- Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
- Department of Bioengineering, Clemson University, Clemson, SC, USA.
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16
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Luthey-Schulten Z, Thornburg ZR, Gilbert BR. Integrating cellular and molecular structures and dynamics into whole-cell models. Curr Opin Struct Biol 2022; 75:102392. [PMID: 35623188 DOI: 10.1016/j.sbi.2022.102392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 11/03/2022]
Abstract
A complete description of the state of the cell requires knowledge of its size, shape, components, intracellular reactions, and interactions with its environment-all of these as a function of time and cell growth. Adding to this list is the need for theoretical models and simulations that integrate and help to interpret this daunting amount of experimental data. It seems like an overwhelming list of requirements, but progress is being made on many fronts. In this review, we discuss the current challenges and problems in obtaining sufficient information about each aspect of a dynamical whole-cell model (DWCM) for simple and well-studied bacterial systems.
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Affiliation(s)
- Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA; Center for the Physics of the Living Cell, University of Illinois at Urbana-Champaign, USA.
| | - Zane R Thornburg
- Department of Chemistry, University of Illinois at Urbana-Champaign, USA
| | - Benjamin R Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, USA
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17
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Lv X, Hueso-Gil A, Bi X, Wu Y, Liu Y, Liu L, Ledesma-Amaro R. New synthetic biology tools for metabolic control. Curr Opin Biotechnol 2022; 76:102724. [PMID: 35489308 DOI: 10.1016/j.copbio.2022.102724] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/28/2022] [Accepted: 03/20/2022] [Indexed: 11/29/2022]
Abstract
In industrial bioprocesses, microbial metabolism dictates the product yields, and therefore, our capacity to control it has an enormous potential to help us move towards a bio-based economy. The rapid development of multiomics data has accelerated our systematic understanding of complex metabolic regulatory mechanisms, which allow us to develop tools to manipulate them. In the last few years, machine learning-based metabolic modeling, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) derived synthetic biology tools, and synthetic genetic circuits have been widely used to control the metabolism of microorganisms, manipulate gene expression, and build synthetic pathways for bioproduction. This review describes the latest developments for metabolic control, and focuses on the trends and challenges of metabolic engineering strategies.
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Affiliation(s)
- Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Angeles Hueso-Gil
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW72AZ, UK
| | - Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yaokang Wu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW72AZ, UK.
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18
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Maritan M, Autin L, Karr J, Covert MW, Olson AJ, Goodsell DS. Building Structural Models of a Whole Mycoplasma Cell. J Mol Biol 2022; 434:167351. [PMID: 34774566 PMCID: PMC8752489 DOI: 10.1016/j.jmb.2021.167351] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 02/01/2023]
Abstract
Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.
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Affiliation(s)
- Martina Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/MartinaMaritan
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/grinche
| | - Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA; RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
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19
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Thornburg ZR, Bianchi DM, Brier TA, Gilbert BR, Earnest TM, Melo MC, Safronova N, Sáenz JP, Cook AT, Wise KS, Hutchison CA, Smith HO, Glass JI, Luthey-Schulten Z. Fundamental behaviors emerge from simulations of a living minimal cell. Cell 2022; 185:345-360.e28. [PMID: 35063075 PMCID: PMC9985924 DOI: 10.1016/j.cell.2021.12.025] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/01/2021] [Accepted: 12/17/2021] [Indexed: 01/18/2023]
Abstract
We present a whole-cell fully dynamical kinetic model (WCM) of JCVI-syn3A, a minimal cell with a reduced genome of 493 genes that has retained few regulatory proteins or small RNAs. Cryo-electron tomograms provide the cell geometry and ribosome distributions. Time-dependent behaviors of concentrations and reaction fluxes from stochastic-deterministic simulations over a cell cycle reveal how the cell balances demands of its metabolism, genetic information processes, and growth, and offer insight into the principles of life for this minimal cell. The energy economy of each process including active transport of amino acids, nucleosides, and ions is analyzed. WCM reveals how emergent imbalances lead to slowdowns in the rates of transcription and translation. Integration of experimental data is critical in building a kinetic model from which emerges a genome-wide distribution of mRNA half-lives, multiple DNA replication events that can be compared to qPCR results, and the experimentally observed doubling behavior.
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Affiliation(s)
- Zane R. Thornburg
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - David M. Bianchi
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Troy A. Brier
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Benjamin R. Gilbert
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Tyler M. Earnest
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Marcelo C.R. Melo
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Nataliya Safronova
- Technische Universität Dresden, B CUBE Center for Molecular Bioengineering, 01307 Dresden, Germany
| | - James P. Sáenz
- Technische Universität Dresden, B CUBE Center for Molecular Bioengineering, 01307 Dresden, Germany
| | | | - Kim S. Wise
- J. Craig Venter Institute, La Jolla, CA 92037, USA
| | | | | | | | - Zaida Luthey-Schulten
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; NSF Center for the Physics of Living Cells, Urbana, IL 61801, USA; NIH Center for Macromolecular Modeling and Bioinformatics, Urbana, IL 61801, USA.
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20
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Ankrah NYD, Bernstein DB, Biggs M, Carey M, Engevik M, García-Jiménez B, Lakshmanan M, Pacheco AR, Sulheim S, Medlock GL. Enhancing Microbiome Research through Genome-Scale Metabolic Modeling. mSystems 2021; 6:e0059921. [PMID: 34904863 PMCID: PMC8670372 DOI: 10.1128/msystems.00599-21] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Construction and analysis of genome-scale metabolic models (GEMs) is a well-established systems biology approach that can be used to predict metabolic and growth phenotypes. The ability of GEMs to produce mechanistic insight into microbial ecological processes makes them appealing tools that can open a range of exciting opportunities in microbiome research. Here, we briefly outline these opportunities, present current rate-limiting challenges for the trustworthy application of GEMs to microbiome research, and suggest approaches for moving the field forward.
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Affiliation(s)
- Nana Y. D. Ankrah
- State University of New York at Plattsburgh, Plattsburgh, New York, USA
| | | | | | - Maureen Carey
- University of Virginia, Charlottesville, Virginia, USA
| | - Melinda Engevik
- Medical University of South Carolina, Charleston, South Carolina, USA
| | | | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore
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21
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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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22
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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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23
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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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24
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Schölzel C, Blesius V, Ernst G, Dominik A. Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective. NPJ Syst Biol Appl 2021; 7:27. [PMID: 34083542 PMCID: PMC8175692 DOI: 10.1038/s41540-021-00182-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/19/2021] [Indexed: 02/06/2023] Open
Abstract
Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.
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Affiliation(s)
- Christopher Schölzel
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany.
| | - Valeria Blesius
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| | - Gernot Ernst
- Vestre Viken Hospital Trust, Kongsberg, Norway
- University of Oslo, Oslo, Norway
| | - Andreas Dominik
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
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25
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Song Z, Zhou Y, Feng J, Juusola M. Multiscale 'whole-cell' models to study neural information processing - New insights from fly photoreceptor studies. J Neurosci Methods 2021; 357:109156. [PMID: 33775669 DOI: 10.1016/j.jneumeth.2021.109156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 03/21/2021] [Accepted: 03/23/2021] [Indexed: 11/26/2022]
Abstract
Understanding a neuron's input-output relationship is a longstanding challenge. Arguably, these signalling dynamics can be better understood if studied at three levels of analysis: computational, algorithmic and implementational (Marr, 1982). But it is difficult to integrate such analyses into a single platform that can realistically simulate neural information processing. Multiscale dynamical "whole-cell" modelling, a recent systems biology approach, makes this possible. Dynamical "whole-cell" models are computational models that aim to account for the integrated function of numerous genes or molecules to behave like virtual cells in silico. However, because constructing such models is laborious, only a couple of examples have emerged since the first one, built for Mycoplasma genitalium bacterium, was reported in 2012. Here, we review dynamic "whole-cell" neuron models for fly photoreceptors and how these have been used to study neural information processing. Specifically, we review how the models have helped uncover the mechanisms and evolutionary rules of quantal light information sampling and integration, which underlie light adaptation and further improve our understanding of insect vision.
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Affiliation(s)
- Zhuoyi Song
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Shanghai, China.
| | - Yu Zhou
- School of Computing, Engineering and Physical Sciences, University of Central Lancashire, Preston PR1 2HE, UK
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Mikko Juusola
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN, UK; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
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26
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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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27
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Roth YD, Lian Z, Pochiraju S, Shaikh B, Karr JR. Datanator: an integrated database of molecular data for quantitatively modeling cellular behavior. Nucleic Acids Res 2021; 49:D516-D522. [PMID: 33174603 PMCID: PMC7779073 DOI: 10.1093/nar/gkaa1008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/12/2020] [Accepted: 10/21/2020] [Indexed: 12/23/2022] Open
Abstract
Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering and medicine. However, it is difficult to obtain the diverse data needed for integrative research. To facilitate biochemical research, we developed Datanator (https://datanator.info), an integrated database and set of tools for finding clouds of multiple types of molecular data about specific molecules and reactions in specific organisms and environments, as well as data about chemically-similar molecules and reactions in phylogenetically-similar organisms in similar environments. Currently, Datanator includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction rate constants about a broad range of organisms. Going forward, we aim to launch a community initiative to curate additional data. Datanator also provides tools for filtering, visualizing and exporting these data clouds. We believe that Datanator can facilitate a wide range of research from integrative mechanistic models, such as whole-cell models, to comparative data-driven analyses of multiple organisms.
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Affiliation(s)
- Yosef D Roth
- Icahn Institute for Data Science and Genomic Technology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1255 5th Avenue, Suite C2, New York, NY 10029, USA
| | - Zhouyang Lian
- Icahn Institute for Data Science and Genomic Technology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1255 5th Avenue, Suite C2, New York, NY 10029, USA
| | - Saahith Pochiraju
- Icahn Institute for Data Science and Genomic Technology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1255 5th Avenue, Suite C2, New York, NY 10029, USA
| | - Bilal Shaikh
- Icahn Institute for Data Science and Genomic Technology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1255 5th Avenue, Suite C2, New York, NY 10029, USA
| | - Jonathan R Karr
- Icahn Institute for Data Science and Genomic Technology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1255 5th Avenue, Suite C2, New York, NY 10029, USA
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28
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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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29
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Burke PEP, Campos CBDL, Costa LDF, Quiles MG. A biochemical network modeling of a whole-cell. Sci Rep 2020; 10:13303. [PMID: 32764598 PMCID: PMC7411072 DOI: 10.1038/s41598-020-70145-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 07/23/2020] [Indexed: 01/18/2023] Open
Abstract
All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.
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Affiliation(s)
- Paulo E P Burke
- University of São Paulo, Bioinformatics Graduate Program, São Carlos, SP, Brazil.
| | - Claudia B de L Campos
- Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil
| | - Luciano da F Costa
- São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, Brazil
| | - Marcos G Quiles
- Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil
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30
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Porubsky VL, Goldberg AP, Rampadarath AK, Nickerson DP, Karr JR, Sauro HM. Best Practices for Making Reproducible Biochemical Models. Cell Syst 2020; 11:109-120. [PMID: 32853539 DOI: 10.1016/j.cels.2020.06.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 05/15/2020] [Accepted: 06/24/2020] [Indexed: 01/03/2023]
Abstract
Like many scientific disciplines, dynamical biochemical modeling is hindered by irreproducible results. This limits the utility of biochemical models by making them difficult to understand, trust, or reuse. We comprehensively list the best practices that biochemical modelers should follow to build reproducible biochemical model artifacts-all data, model descriptions, and custom software used by the model-that can be understood and reused. The best practices provide advice for all steps of a typical biochemical modeling workflow in which a modeler collects data; constructs, trains, simulates, and validates the model; uses the predictions of a model to advance knowledge; and publicly shares the model artifacts. The best practices emphasize the benefits obtained by using standard tools and formats and provides guidance to modelers who do not or cannot use standards in some stages of their modeling workflow. Adoption of these best practices will enhance the ability of researchers to reproduce, understand, and reuse biochemical models.
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Affiliation(s)
- Veronica L Porubsky
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA.
| | - Arthur P Goldberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Anand K Rampadarath
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - David P Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Jonathan R Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
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31
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Boada Y, Vignoni A, Picó J, Carbonell P. Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories. iScience 2020; 23:101305. [PMID: 32629420 PMCID: PMC7334618 DOI: 10.1016/j.isci.2020.101305] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/05/2020] [Accepted: 06/18/2020] [Indexed: 12/17/2022] Open
Abstract
Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.
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Affiliation(s)
- Yadira Boada
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain; Centro Universitario EDEM, Escuela de Empresarios, Muelle de la Aduana s/n, La Marina de València, 46024 Valencia, Spain
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
| | - Jesús Picó
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
| | - Pablo Carbonell
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain.
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32
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Richards DM. Receptor Models of Phagocytosis: The Effect of Target Shape. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1246:55-70. [PMID: 32399825 DOI: 10.1007/978-3-030-40406-2_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Phagocytosis is a remarkably complex process, requiring simultaneous organisation of the cell membrane, the cytoskeleton, receptors and various signalling molecules. As can often be the case, mathematical modelling is able to penetrate some of this complexity, identifying the key biophysical components and generating understanding that would take far longer with a purely experimental approach. This chapter will review a particularly important class of phagocytosis model, championed in recent years, that primarily focuses on the role of receptors during the engulfment process. These models are pertinent to a host of unsolved questions in the subject, including the rate of cup growth during uptake, the role of both intra- and extracellular noise, and the precise differences between phagocytosis and other forms of endocytosis. In particular, this chapter will focus on the effect of target shape and orientation, including how these influence the rate and final outcome of phagocytic engulfment.
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33
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Lang PF, Chebaro Y, Zheng X, P Sekar JA, Shaikh B, Natale DA, Karr JR. BpForms and BcForms: a toolkit for concretely describing non-canonical polymers and complexes to facilitate global biochemical networks. Genome Biol 2020; 21:117. [PMID: 32423472 PMCID: PMC7236495 DOI: 10.1186/s13059-020-02025-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 04/16/2020] [Indexed: 12/12/2022] Open
Abstract
Non-canonical residues, caps, crosslinks, and nicks are important to many functions of DNAs, RNAs, proteins, and complexes. However, we do not fully understand how networks of such non-canonical macromolecules generate behavior. One barrier is our limited formats for describing macromolecules. To overcome this barrier, we develop BpForms and BcForms, a toolkit for representing the primary structure of macromolecules as combinations of residues, caps, crosslinks, and nicks. The toolkit can help omics researchers perform quality control and exchange information about macromolecules, help systems biologists assemble global models of cells that encompass processes such as post-translational modification, and help bioengineers design cells.
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Affiliation(s)
- Paul F Lang
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Yassmine Chebaro
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Université de Strasbourg, Illkirch, 67404, France
| | - Xiaoyue Zheng
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
| | - John A P Sekar
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
| | - Bilal Shaikh
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
| | - Darren A Natale
- Protein Information Resource, Georgetown University Medical Center, Washington, DC, 20007, USA
| | - Jonathan R Karr
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.
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34
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Bartley BA, Beal J, Karr JR, Strychalski EA. Organizing genome engineering for the gigabase scale. Nat Commun 2020; 11:689. [PMID: 32019919 PMCID: PMC7000699 DOI: 10.1038/s41467-020-14314-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Accepted: 12/18/2019] [Indexed: 12/11/2022] Open
Abstract
Genome-scale engineering holds great potential to impact science, industry, medicine, and society, and recent improvements in DNA synthesis have enabled the manipulation of megabase genomes. However, coordinating and integrating the workflows and large teams necessary for gigabase genome engineering remains a considerable challenge. We examine this issue and recommend a path forward by: 1) adopting and extending existing representations for designs, assembly plans, samples, data, and workflows; 2) developing new technologies for data curation and quality control; 3) conducting fundamental research on genome-scale modeling and design; and 4) developing new legal and contractual infrastructure to facilitate collaboration.
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Affiliation(s)
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, 02138, USA.
| | - Jonathan R Karr
- Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10128, USA
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35
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Chaudhary T, Shukla P. Bioinoculant capability enhancement through metabolomics and systems biology approaches. Brief Funct Genomics 2019; 18:159-168. [PMID: 31232454 DOI: 10.1093/bfgp/elz011] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 03/30/2019] [Accepted: 04/26/2019] [Indexed: 02/07/2023] Open
Abstract
Bioinoculants are eco-friendly microorganisms, and their products are utilized for improving the potential of soil and fulfill the nutrients requirement for the host plant. The agricultural yield has increased due to the use of bioinoculants over chemical-based fertilizers, and thus it generates interest in understanding the innovation process by various methods. By gene-editing tool, the desired gene product can be changed for engineered microbial inoculants. We have also described various modern biotechnological tools like constraint-based modeling, OptKnock, flux balance analysis and modeling of the biological network for enhancing the bioinoculant capability. These fluxes give the fascinating perception of the metabolic network in the absence of comprehensive kinetic information. These tools also help in the stimulation of the metabolic networks by incorporation of enzyme-encoding genes. The present review explains the use of systems biology and gene-editing tools for improving the capability of bioinoculants. Moreover, this review also emphasizes on the challenges and future perspective of systems biology and its multidisciplinary facets.
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Affiliation(s)
- Twinkle Chaudhary
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
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36
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Landon S, Rees-Garbutt J, Marucci L, Grierson C. Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering. Essays Biochem 2019; 63:267-284. [PMID: 31243142 PMCID: PMC6610458 DOI: 10.1042/ebc20180045] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 05/19/2019] [Accepted: 05/23/2019] [Indexed: 01/04/2023]
Abstract
Producing 'designer cells' with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.
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Affiliation(s)
- Sophie Landon
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, U.K
| | - Joshua Rees-Garbutt
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K
- School of Biological Sciences, University of Bristol, Life Sciences Building, Bristol BS8 1TQ, U.K
| | - Lucia Marucci
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K.
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, U.K
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1UB, U.K
| | - Claire Grierson
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K.
- School of Biological Sciences, University of Bristol, Life Sciences Building, Bristol BS8 1TQ, U.K
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37
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Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BIOMED RESEARCH INTERNATIONAL 2019; 2019:8304260. [PMID: 31281846 PMCID: PMC6590590 DOI: 10.1155/2019/8304260] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/07/2019] [Accepted: 05/20/2019] [Indexed: 01/06/2023]
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.
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38
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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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39
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Hellerstein JL, Gu S, Choi K, Sauro HM. Recent advances in biomedical simulations: a manifesto for model engineering. F1000Res 2019; 8:F1000 Faculty Rev-261. [PMID: 30881691 PMCID: PMC6406177 DOI: 10.12688/f1000research.15997.1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/21/2019] [Indexed: 01/18/2023] Open
Abstract
Biomedical simulations are widely used to understand disease, engineer cells, and model cellular processes. In this article, we explore how to improve the quality of biomedical simulations by developing simulation models using tools and practices employed in software engineering. We refer to this direction as model engineering. Not all techniques used by software engineers are directly applicable to model engineering, and so some adaptations are required. That said, we believe that simulation models can benefit from software engineering practices for requirements, design, and construction as well as from software engineering tools for version control, error checking, and testing. Here we survey current efforts to improve simulation quality and discuss promising research directions for model engineering.
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Affiliation(s)
| | - Stanley Gu
- Department of Bioengineering, William H. Foege Building, University of Washington, Seattle, WA, Box 355061, USA
| | - Kiri Choi
- Department of Bioengineering, William H. Foege Building, University of Washington, Seattle, WA, Box 355061, USA
| | - Herbert M. Sauro
- Department of Bioengineering, William H. Foege Building, University of Washington, Seattle, WA, Box 355061, USA
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40
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Spacial models of malfunctioned protein complexes help to elucidate signal transduction critical for insulin release. Biosystems 2018; 177:48-55. [PMID: 30395892 DOI: 10.1016/j.biosystems.2018.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Revised: 10/30/2018] [Accepted: 11/01/2018] [Indexed: 12/14/2022]
Abstract
Mutations in gene KCNJ11 encoding the Kir6.2 subunit of the ATP-sensitive potassium channel (KATP), a representative of a quite complex biosystem, may affect insulin release from pancreatic beta-cells. Both gain and loss of channel activity are observed, which lead to varied clinical phenotypes ranging from neonatal diabetes to congenital hyperinsulinism. In order to understand the mechanisms of the channel function better we mapped, based on the literature review, known medically relevant Kir6.2/SUR1 mutations into recently (2017) determined CryoEM 3D structures of this complex. We used a clustering algorithm to find hots spots in the 3D structure, thus we may hypothesize about their nano-mechanical role in the channel gating and the insulin level control. We also adapted a simple model of the channel gating to cover all currently known factors that can influence the KATP biosystem functions.
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41
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Blötz C, Lartigue C, Valverde Timana Y, Ruiz E, Paetzold B, Busse J, Stülke J. Development of a replicating plasmid based on the native oriC in Mycoplasma pneumoniae. MICROBIOLOGY-SGM 2018; 164:1372-1382. [PMID: 30252643 DOI: 10.1099/mic.0.000711] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Bacteria of the genus Mycoplasma have recently attracted considerable interest as model organisms in synthetic and systems biology. In particular, Mycoplasma pneumoniae is one of the most intensively studied organisms in the field of systems biology. However, the genetic manipulation of these bacteria is often difficult due to the lack of efficient genetic systems and some intrinsic peculiarities such as an aberrant genetic code. One major disadvantage in working with M. pneumoniae is the lack of replicating plasmids that can be used for the complementation of mutants and the expression of proteins. In this study, we have analysed the genomic region around the gene encoding the replication initiation protein, DnaA, and detected putative binding sites for DnaA (DnaA boxes) that are, however, less conserved than in other bacteria. The construction of several plasmids encompassing this region allowed the selection of plasmid pGP2756 that is stably inherited and that can be used for genetic experiments, as shown by the complementation assays with the glpQ gene encoding the glycerophosphoryl diester phosphodiesterase. Plasmid-borne complementation of the glpQ mutant restored the formation of hydrogen peroxide when bacteria were cultivated in the presence of glycerol phosphocholine. Interestingly, the replicating plasmid can also be used in the close relative, Mycoplasma genitalium but not in more distantly related members of the genus Mycoplasma. Thus, plasmid pGP2756 is a valuable tool for the genetic analysis of M. pneumoniae and M. genitalium.
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Affiliation(s)
- Cedric Blötz
- 1Department of General Microbiology, Institute of Microbiology and Genetics, Georg-August University Göttingen, Göttingen, Germany
| | - Carole Lartigue
- 2INRA, UMR 1332 de Biologie du Fruit et Pathologie, Villenave d'Ornon, France.,3University of Bordeaux, UMR 1332 de Biologie du Fruit et Pathologie, Villenave d'Ornon, France
| | - Yanina Valverde Timana
- 2INRA, UMR 1332 de Biologie du Fruit et Pathologie, Villenave d'Ornon, France.,3University of Bordeaux, UMR 1332 de Biologie du Fruit et Pathologie, Villenave d'Ornon, France
| | - Estelle Ruiz
- 2INRA, UMR 1332 de Biologie du Fruit et Pathologie, Villenave d'Ornon, France.,3University of Bordeaux, UMR 1332 de Biologie du Fruit et Pathologie, Villenave d'Ornon, France
| | - Bernhard Paetzold
- 4Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain.,†Present address: S-Biomedic N.V., Beerse, Belgium
| | - Julia Busse
- 1Department of General Microbiology, Institute of Microbiology and Genetics, Georg-August University Göttingen, Göttingen, Germany
| | - Jörg Stülke
- 1Department of General Microbiology, Institute of Microbiology and Genetics, Georg-August University Göttingen, Göttingen, Germany
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