1
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Zitnik M, Li MM, Wells A, Glass K, Morselli Gysi D, Krishnan A, Murali TM, Radivojac P, Roy S, Baudot A, Bozdag S, Chen DZ, Cowen L, Devkota K, Gitter A, Gosline SJC, Gu P, Guzzi PH, Huang H, Jiang M, Kesimoglu ZN, Koyuturk M, Ma J, Pico AR, Pržulj N, Przytycka TM, Raphael BJ, Ritz A, Sharan R, Shen Y, Singh M, Slonim DK, Tong H, Yang XH, Yoon BJ, Yu H, Milenković T. Current and future directions in network biology. BIOINFORMATICS ADVANCES 2024; 4:vbae099. [PMID: 39143982 PMCID: PMC11321866 DOI: 10.1093/bioadv/vbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
Summary Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation Not applicable.
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Affiliation(s)
- Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Michelle M Li
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Aydin Wells
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Deisy Morselli Gysi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Statistics, Federal University of Paraná, Curitiba, Paraná 81530-015, Brazil
- Department of Physics, Northeastern University, Boston, MA 02115, United States
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
| | - Predrag Radivojac
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, United States
| | - Sushmita Roy
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Wisconsin Institute for Discovery, Madison, WI 53715, United States
| | - Anaïs Baudot
- Aix Marseille Université, INSERM, MMG, Marseille, France
| | - Serdar Bozdag
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- Department of Mathematics, University of North Texas, Denton, TX 76203, United States
| | - Danny Z Chen
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, United States
- Morgridge Institute for Research, Madison, WI 53715, United States
| | - Sara J C Gosline
- Biological Sciences Division, Pacific Northwest National Laboratory, Seattle, WA 98109, United States
| | - Pengfei Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Pietro H Guzzi
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, 88100, Italy
| | - Heng Huang
- Department of Computer Science, University of Maryland College Park, College Park, MD 20742, United States
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Ziynet Nesibe Kesimoglu
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, United States
| | - Nataša Pržulj
- Department of Computer Science, University College London, London, WC1E 6BT, England
- ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, 08010, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20814, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
| | - Anna Ritz
- Department of Biology, Reed College, Portland, OR 97202, United States
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, NJ 08544, United States
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, United States
| | - Donna K Slonim
- Department of Computer Science, Tufts University, Medford, MA 02155, United States
| | - Hanghang Tong
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Xinan Holly Yang
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, United States
| | - Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, United States
| | - Haiyuan Yu
- Department of Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, United States
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
- Lucy Family Institute for Data and Society, University of Notre Dame, Notre Dame, IN 46556, United States
- Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States
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2
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Hao T, Song Z, Zhang M, Zhang L, Yang J, Li J, Sun J. Reconstruction of Metabolic-Protein Interaction Integrated Network of Eriocheir sinensis and Analysis of Ecdysone Synthesis. Genes (Basel) 2024; 15:410. [PMID: 38674345 PMCID: PMC11049885 DOI: 10.3390/genes15040410] [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: 02/28/2024] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Integrated networks have become a new interest in genome-scale network research due to their ability to comprehensively reflect and analyze the molecular processes in cells. Currently, none of the integrated networks have been reported for higher organisms. Eriocheir sinensis is a typical aquatic animal that grows through ecdysis. Ecdysone has been identified to be a crucial regulator of ecdysis, but the influence factors and regulatory mechanisms of ecdysone synthesis in E. sinensis are still unclear. In this work, the genome-scale metabolic network and protein-protein interaction network of E. sinensis were integrated to reconstruct a metabolic-protein interaction integrated network (MPIN). The MPIN was used to analyze the influence factors of ecdysone synthesis through flux variation analysis. In total, 236 integrated reactions (IRs) were found to influence the ecdysone synthesis of which 16 IRs had a significant impact. These IRs constitute three ecdysone synthesis routes. It is found that there might be alternative pathways to obtain cholesterol for ecdysone synthesis in E. sinensis instead of absorbing it directly from the feeds. The MPIN reconstructed in this work is the first integrated network for higher organisms. The analysis based on the MPIN supplies important information for the mechanism analysis of ecdysone synthesis in E. sinensis.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Zhentao Song
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Mingzhi Zhang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Lingrui Zhang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Jiarui Yang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
| | - Jingjing Li
- Tianjin Fisheries Research Institute, Tianjin 300211, China;
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China; (T.H.); (Z.S.); (M.Z.); (L.Z.); (J.Y.)
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Sun G, DeFelice MM, Gillies TE, Ahn-Horst TA, Andrews CJ, Krummenacker M, Karp PD, Morrison JH, Covert MW. Cross-evaluation of E. coli's operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons. Cell Syst 2024; 15:227-245.e7. [PMID: 38417437 PMCID: PMC10957310 DOI: 10.1016/j.cels.2024.02.002] [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: 08/30/2023] [Revised: 09/12/2023] [Accepted: 02/08/2024] [Indexed: 03/01/2024]
Abstract
Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Mialy M DeFelice
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Taryn E Gillies
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Travis A Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Cecelia J Andrews
- Department of Developmental Biology, Stanford University, Stanford, CA 94305, USA
| | | | | | - Jerry H Morrison
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
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4
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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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5
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Georgouli K, Yeom JS, Blake RC, Navid A. Multi-scale models of whole cells: progress and challenges. Front Cell Dev Biol 2023; 11:1260507. [PMID: 38020904 PMCID: PMC10661945 DOI: 10.3389/fcell.2023.1260507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Whole-cell modeling is "the ultimate goal" of computational systems biology and "a grand challenge for 21st century" (Tomita, Trends in Biotechnology, 2001, 19(6), 205-10). These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of "system" models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.
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Affiliation(s)
- Konstantia Georgouli
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Jae-Seung Yeom
- Center for Applied Scientific Computing, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Robert C. Blake
- Center for Applied Scientific Computing, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Ali Navid
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
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6
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Integrative modeling of the cell. Acta Biochim Biophys Sin (Shanghai) 2022; 54:1213-1221. [PMID: 36017893 PMCID: PMC9909318 DOI: 10.3724/abbs.2022115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
A whole-cell model represents certain aspects of the cell structure and/or function. Due to the high complexity of the cell, an integrative modeling approach is often taken to utilize all available information including experimental data, prior knowledge and prior models. In this review, we summarize an emerging workflow of whole-cell modeling into five steps: (i) gather information; (ii) represent the modeled system into modules; (iii) translate input information into scoring function; (iv) sample the whole-cell model; (v) validate and interpret the model. In particular, we propose the integrative modeling of the cell by combining available (whole-cell) models to maximize the accuracy, precision, and completeness. In addition, we list quantitative predictions of various aspects of cell biology from existing whole-cell models. Moreover, we discuss the remaining challenges and future directions, and highlight the opportunity to establish an integrative spatiotemporal multi-scale whole-cell model based on a community approach.
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7
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Ahn-Horst TA, Mille LS, Sun G, Morrison JH, Covert MW. An expanded whole-cell model of E. coli links cellular physiology with mechanisms of growth rate control. NPJ Syst Biol Appl 2022; 8:30. [PMID: 35986058 PMCID: PMC9391491 DOI: 10.1038/s41540-022-00242-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/28/2022] [Indexed: 11/09/2022] Open
Abstract
Growth and environmental responses are essential for living organisms to survive and adapt to constantly changing environments. In order to simulate new conditions and capture dynamic responses to environmental shifts in a developing whole-cell model of E. coli, we incorporated additional regulation, including dynamics of the global regulator guanosine tetraphosphate (ppGpp), along with dynamics of amino acid biosynthesis and translation. With the model, we show that under perturbed ppGpp conditions, small molecule feedback inhibition pathways, in addition to regulation of expression, play a role in ppGpp regulation of growth. We also found that simulations with dysregulated amino acid synthesis pathways provide average amino acid concentration predictions that are comparable to experimental results but on the single-cell level, concentrations unexpectedly show regular fluctuations. Additionally, during both an upshift and downshift in nutrient availability, the simulated cell responds similarly with a transient increase in the mRNA:rRNA ratio. This additional simulation functionality should support a variety of new applications and expansions of the E. coli Whole-Cell Modeling Project.
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Affiliation(s)
- Travis A Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | - Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Jerry H Morrison
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
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8
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Duan D, Hanson M, Holland DO, Johnson ME. Integrating protein copy numbers with interaction networks to quantify stoichiometry in clathrin-mediated endocytosis. Sci Rep 2022; 12:5413. [PMID: 35354856 PMCID: PMC8967901 DOI: 10.1038/s41598-022-09259-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 03/21/2022] [Indexed: 11/25/2022] Open
Abstract
Proteins that drive processes like clathrin-mediated endocytosis (CME) are expressed at copy numbers within a cell and across cell types varying from hundreds (e.g. auxilin) to millions (e.g. clathrin). These variations contain important information about function, but without integration with the interaction network, they cannot capture how supply and demand for each protein depends on binding to shared and distinct partners. Here we construct the interface-resolved network of 82 proteins involved in CME and establish a metric, a stoichiometric balance ratio (SBR), that quantifies whether each protein in the network has an abundance that is sub- or super-stoichiometric dependent on the global competition for binding. We find that highly abundant proteins (like clathrin) are super-stoichiometric, but that not all super-stoichiometric proteins are highly abundant, across three cell populations (HeLa, fibroblast, and neuronal synaptosomes). Most strikingly, within all cells there is significant competition to bind shared sites on clathrin and the central AP-2 adaptor by other adaptor proteins, resulting in most being in excess supply. Our network and systematic analysis, including response to perturbations of network components, show how competition for shared binding sites results in functionally similar proteins having widely varying stoichiometries, due to variations in both abundance and their unique network of binding partners.
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Affiliation(s)
- Daisy Duan
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Meretta Hanson
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | | | - Margaret E Johnson
- TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
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9
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Sun G, Ahn-Horst TA, Covert MW. The E. coli Whole-Cell Modeling Project. EcoSal Plus 2021; 9:eESP00012020. [PMID: 34242084 PMCID: PMC11163835 DOI: 10.1128/ecosalplus.esp-0001-2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 05/26/2021] [Indexed: 12/22/2022]
Abstract
The Escherichia coli whole-cell modeling project seeks to create the most detailed computational model of an E. coli cell in order to better understand and predict the behavior of this model organism. Details about the approach, framework, and current version of the model are discussed. Currently, the model includes the functions of 43% of characterized genes, with ongoing efforts to include additional data and mechanisms. As additional information is incorporated in the model, its utility and predictive power will continue to increase, which means that discovery efforts can be accelerated by community involvement in the generation and inclusion of data. This project will be an invaluable resource to the E. coli community that could be used to verify expected physiological behavior, to predict new outcomes and testable hypotheses for more efficient experimental design iterations, and to evaluate heterogeneous data sets in the context of each other through deep curation.
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Affiliation(s)
- Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Travis A. Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Markus W. Covert
- Department of Bioengineering, Stanford University, Stanford, California, USA
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10
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Gallup O, Ming H, Ellis T. Ten future challenges for synthetic biology. ENGINEERING BIOLOGY 2021; 5:51-59. [PMID: 36968258 PMCID: PMC9996719 DOI: 10.1049/enb2.12011] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022] Open
Abstract
After 2 decades of growth and success, synthetic biology has now become a mature field that is driving significant innovation in the bioeconomy and pushing the boundaries of the biomedical sciences and biotechnology. So what comes next? In this article, 10 technological advances are discussed that are expected and hoped to come from the next generation of research and investment in synthetic biology; from ambitious projects to make synthetic life, cell simulators and custom genomes, through to new methods of engineering biology that use automation, deep learning and control of evolution. The non-exhaustive list is meant to inspire those joining the field and looks forward to how synthetic biology may evolve over the coming decades.
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Affiliation(s)
- Olivia Gallup
- Department of BioengineeringImperial College LondonLondonUK
| | - Hia Ming
- Department of BioengineeringImperial College LondonLondonUK
| | - Tom Ellis
- Department of BioengineeringImperial College LondonLondonUK
- Imperial College Centre for Synthetic BiologyImperial College LondonLondonUK
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11
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Rees-Garbutt J, Rightmyer J, Chalkley O, Marucci L, Grierson C. Testing Theoretical Minimal Genomes Using Whole-Cell Models. ACS Synth Biol 2021; 10:1598-1604. [PMID: 34111356 DOI: 10.1021/acssynbio.0c00515] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The minimal gene set for life has often been theorized, with at least ten produced for Mycoplasma genitalium (M. genitalium). Due to the difficulty of using M. genitalium in the lab, combined with its long replication time of 12-15 h, none of these theoretical minimal genomes have been tested, even with modern techniques. The publication of the M. genitalium whole-cell model provided the first opportunity to test them, simulating the genome edits in silico. We simulated minimal gene sets from the literature, finding that they produced in silico cells that did not divide. Using knowledge from previous research, we reintroduced specific essential and low essential genes in silico; enabling cellular division. This reinforces the need to identify species-specific low essential genes and their interactions. Any genome designs created using the currently incomplete and fragmented gene essentiality information will very likely require in vivo reintroductions to correct issues and produce dividing cells.
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Affiliation(s)
- Joshua Rees-Garbutt
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K
- School of Biological Sciences, University of Bristol, Bristol Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, U.K
| | - Jake Rightmyer
- School of Biological Sciences, University of Bristol, Bristol Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, U.K
| | - Oliver Chalkley
- BrisSynBio, University of Bristol, Bristol BS8 1TQ, U.K
- Bristol Centre for Complexity Sciences, Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, U.K
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, 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, Bristol Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, U.K
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12
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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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13
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Santibáñez R, Garrido D, Martin AJM. Atlas: automatic modeling of regulation of bacterial gene expression and metabolism using rule-based languages. Bioinformatics 2021; 36:5473-5480. [PMID: 33367504 PMCID: PMC8016457 DOI: 10.1093/bioinformatics/btaa1040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 11/19/2020] [Accepted: 12/12/2020] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Cells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes. RESULTS We developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria. AVAILABILITY AND IMPLEMENTATION Code, models and tutorials are available online (https://github.com/networkbiolab/atlas). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rodrigo Santibáñez
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Universidad Mayor, Santiago 8580745, Chile
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
| | - Alberto J M Martin
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Universidad Mayor, Santiago 8580745, Chile
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Johnson ME, Chen A, Faeder JR, Henning P, Moraru II, Meier-Schellersheim M, Murphy RF, Prüstel T, Theriot JA, Uhrmacher AM. Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry. Mol Biol Cell 2021; 32:186-210. [PMID: 33237849 PMCID: PMC8120688 DOI: 10.1091/mbc.e20-08-0530] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/13/2020] [Accepted: 11/17/2020] [Indexed: 12/29/2022] Open
Abstract
Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.
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Affiliation(s)
- M. E. Johnson
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218
| | - A. Chen
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218
| | - J. R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260
| | - P. Henning
- Institute for Visual and Analytic Computing, University of Rostock, 18055 Rostock, Germany
| | - I. I. Moraru
- Department of Cell Biology, Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030
| | - M. Meier-Schellersheim
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - R. F. Murphy
- Computational Biology Department, Department of Biological Sciences, Department of Biomedical Engineering, Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15289
| | - T. Prüstel
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - J. A. Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195
| | - A. M. Uhrmacher
- Institute for Visual and Analytic Computing, University of Rostock, 18055 Rostock, Germany
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Kurasawa H, Ohno T, Arai R, Aizawa Y. A guideline and challenges toward the minimization of bacterial and eukaryotic genomes. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.coisb.2020.10.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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16
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Clarke R, Kraikivski P, Jones BC, Sevigny CM, Sengupta S, Wang Y. A systems biology approach to discovering pathway signaling dysregulation in metastasis. Cancer Metastasis Rev 2020; 39:903-918. [PMID: 32776157 PMCID: PMC7487029 DOI: 10.1007/s10555-020-09921-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 07/13/2020] [Indexed: 02/07/2023]
Abstract
Total metastatic burden is the primary cause of death for many cancer patients. While the process of metastasis has been studied widely, much remains to be understood. Moreover, few agents have been developed that specifically target the major steps of the metastatic cascade. Many individual genes and pathways have been implicated in metastasis but a holistic view of how these interact and cooperate to regulate and execute the process remains somewhat rudimentary. It is unclear whether all of the signaling features that regulate and execute metastasis are yet fully understood. Novel features of a complex system such as metastasis can often be discovered by taking a systems-based approach. We introduce the concepts of systems modeling and define some of the central challenges facing the application of a multidisciplinary systems-based approach to understanding metastasis and finding actionable targets therein. These challenges include appreciating the unique properties of the high-dimensional omics data often used for modeling, limitations in knowledge of the system (metastasis), tumor heterogeneity and sampling bias, and some of the issues key to understanding critical features of molecular signaling in the context of metastasis. We also provide a brief introduction to integrative modeling that focuses on both the nodes and edges of molecular signaling networks. Finally, we offer some observations on future directions as they relate to developing a systems-based model of the metastatic cascade.
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Affiliation(s)
- Robert Clarke
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA.
- Hormel Institute and Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Austin, MN, 55912, USA.
| | - Pavel Kraikivski
- Academy of Integrated Science, Division of Systems Biology, Virginia Polytechnic and State University, Blacksburg, VA, 24061, USA
| | - Brandon C Jones
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Catherine M Sevigny
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Surojeet Sengupta
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA
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17
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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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18
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Macklin DN, Ahn-Horst TA, Choi H, Ruggero NA, Carrera J, Mason JC, Sun G, Agmon E, DeFelice MM, Maayan I, Lane K, Spangler RK, Gillies TE, Paull ML, Akhter S, Bray SR, Weaver DS, Keseler IM, Karp PD, Morrison JH, Covert MW. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science 2020; 369:eaav3751. [PMID: 32703847 PMCID: PMC7990026 DOI: 10.1126/science.aav3751] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 10/28/2019] [Accepted: 05/26/2020] [Indexed: 12/24/2022]
Abstract
The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.
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Affiliation(s)
- Derek N Macklin
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Travis A Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Heejo Choi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Nicholas A Ruggero
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Javier Carrera
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - John C Mason
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Mialy M DeFelice
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Inbal Maayan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Keara Lane
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Ryan K Spangler
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Taryn E Gillies
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Morgan L Paull
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Sajia Akhter
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Samuel R Bray
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | | | | | | | - Jerry H Morrison
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
- Allen Discovery Center at Stanford University, Stanford University, Stanford, CA 94305, USA
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19
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Jose AM. The analysis of living systems can generate both knowledge and illusions. eLife 2020; 9:56354. [PMID: 32553111 PMCID: PMC7302876 DOI: 10.7554/elife.56354] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 06/12/2020] [Indexed: 12/20/2022] Open
Abstract
Life relies on phenomena that range from changes in molecules that occur within nanoseconds to changes in populations that occur over millions of years. Researchers have developed a vast range of experimental techniques to analyze living systems, but a given technique usually only works over a limited range of length or time scales. Therefore, gaining a full understanding of a living system usually requires the integration of information obtained at multiple different scales by two or more techniques. This approach has undoubtedly led to a much better understanding of living systems but, equally, the staggering complexity of these systems, the sophistication and limitations of the techniques available in modern biology, and the need to use two or more techniques, can lead to persistent illusions of knowledge. Here, in an effort to make better use of the experimental techniques we have at our disposal, I propose a broad classification of techniques into six complementary approaches: perturbation, visualization, substitution, characterization, reconstitution, and simulation. Such a taxonomy might also help increase the reproducibility of inferences and improve peer review.
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Affiliation(s)
- Antony M Jose
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, United States
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20
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Yeo HC, Hong J, Lakshmanan M, Lee DY. Enzyme capacity-based genome scale modelling of CHO cells. Metab Eng 2020; 60:138-147. [PMID: 32330653 DOI: 10.1016/j.ymben.2020.04.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/21/2020] [Accepted: 04/14/2020] [Indexed: 10/24/2022]
Abstract
Chinese hamster ovary (CHO) cells are most prevalently used for producing recombinant therapeutics in biomanufacturing. Recently, more rational and systems approaches have been increasingly exploited to identify key metabolic bottlenecks and engineering targets for cell line engineering and process development based on the CHO genome-scale metabolic model which mechanistically characterizes cell culture behaviours. However, it is still challenging to quantify plausible intracellular fluxes and discern metabolic pathway usages considering various clonal traits and bioprocessing conditions. Thus, we newly incorporated enzyme kinetic information into the updated CHO genome-scale model (iCHO2291) and added enzyme capacity constraints within the flux balance analysis framework (ecFBA) to significantly reduce the flux variability in biologically meaningful manner, as such improving the accuracy of intracellular flux prediction. Interestingly, ecFBA could capture the overflow metabolism under the glucose excess condition where the usage of oxidative phosphorylation is limited by the enzyme capacity. In addition, its applicability was successfully demonstrated via a case study where the clone- and media-specific lactate metabolism was deciphered, suggesting that the lactate-pyruvate cycling could be beneficial for CHO cells to efficiently utilize the mitochondrial redox capacity. In summary, iCHO2296 with ecFBA can be used to confidently elucidate cell cultures and effectively identify key engineering targets, thus guiding bioprocess optimization and cell engineering efforts as a part of digital twin model for advanced biomanufacturing in future.
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Affiliation(s)
- Hock Chuan Yeo
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, 138668, Singapore; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore
| | - Jongkwang Hong
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, 138668, Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, 138668, Singapore.
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, 138668, Singapore; School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.
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21
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Rees-Garbutt J, Chalkley O, Landon S, Purcell O, Marucci L, Grierson C. Designing minimal genomes using whole-cell models. Nat Commun 2020; 11:836. [PMID: 32047145 PMCID: PMC7012841 DOI: 10.1038/s41467-020-14545-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 12/17/2019] [Indexed: 11/29/2022] Open
Abstract
In the future, entire genomes tailored to specific functions and environments could be designed using computational tools. However, computational tools for genome design are currently scarce. Here we present algorithms that enable the use of design-simulate-test cycles for genome design, using genome minimisation as a proof-of-concept. Minimal genomes are ideal for this purpose as they have a simple functional assay whether the cell replicates or not. We used the first (and currently only published) whole-cell model for the bacterium Mycoplasma genitalium. Our computational design-simulate-test cycles discovered novel in silico minimal genomes which, if biologically correct, predict in vivo genomes smaller than JCVI-Syn3.0; a bacterium with, currently, the smallest genome that can be grown in pure culture. In the process, we identified 10 low essential genes and produced evidence for at least two Mycoplasma genitalium in silico minimal genomes. This work brings combined computational and laboratory genome engineering a step closer. Genome engineering will one day benefit from computational tools that can design genomes with desired functions. Here the authors develop computational design-simulate-test algorithms to design minimal genomes based on the whole-cell model of Mycoplasma genitalium.
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Affiliation(s)
- Joshua Rees-Garbutt
- BrisSynBio, University of Bristol, Bristol, BS8 1TQ, UK.,School of Biological Sciences, University of Bristol, Bristol Life Sciences Building, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK
| | - Oliver Chalkley
- BrisSynBio, University of Bristol, Bristol, BS8 1TQ, UK.,Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB, UK.,Bristol Centre for Complexity Science, Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB, UK
| | - Sophie Landon
- BrisSynBio, University of Bristol, Bristol, BS8 1TQ, UK.,Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB, UK
| | - Oliver Purcell
- Engine Biosciences, MBC Biolabs, 733 Industrial Road, San Carlos, CA, 94070, USA
| | - Lucia Marucci
- BrisSynBio, University of Bristol, Bristol, BS8 1TQ, UK. .,Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB, UK. .,School of Cellular and Molecular Medicine, University of Bristol, Bristol, BS8 1UB, UK.
| | - Claire Grierson
- BrisSynBio, University of Bristol, Bristol, BS8 1TQ, UK. .,School of Biological Sciences, University of Bristol, Bristol Life Sciences Building, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK.
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23
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Nilsson A, Nielsen J, Palsson BO. Metabolic Models of Protein Allocation Call for the Kinetome. Cell Syst 2019; 5:538-541. [PMID: 29284126 DOI: 10.1016/j.cels.2017.11.013] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 10/09/2017] [Accepted: 11/20/2017] [Indexed: 11/26/2022]
Abstract
The flux of metabolites in the living cell depend on enzyme activities. Recently, many metabolic phenotypes have been explained by computer models that incorporate enzyme activity data. To move further, the scientific community needs to measure the kinetics of all enzymes in a systematic way.
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Affiliation(s)
- Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm DK2970, Denmark.
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm DK2970, Denmark; Departments of Bioengineering and Pediatrics, Mail Code 412, University of California, San Diego, La Jolla, CA 92093, USA
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24
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An energetic reformulation of kinetic rate laws enables scalable parameter estimation for biochemical networks. J Theor Biol 2019; 461:145-156. [DOI: 10.1016/j.jtbi.2018.10.041] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 09/20/2018] [Accepted: 10/19/2018] [Indexed: 11/18/2022]
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25
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Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model. Cell Syst 2018; 7:567-579.e6. [DOI: 10.1016/j.cels.2018.10.013] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 09/07/2018] [Accepted: 10/29/2018] [Indexed: 12/25/2022]
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26
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Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 2018; 173:11-19. [PMID: 29570991 PMCID: PMC6014618 DOI: 10.1016/j.cell.2018.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 11/25/2017] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.
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Affiliation(s)
- Jitin Singla
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kyle M McClary
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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28
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Babtie AC, Stumpf MPH. How to deal with parameters for whole-cell modelling. J R Soc Interface 2017; 14:20170237. [PMID: 28768879 PMCID: PMC5582120 DOI: 10.1098/rsif.2017.0237] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 06/22/2017] [Indexed: 11/12/2022] Open
Abstract
Dynamical systems describing whole cells are on the verge of becoming a reality. But as models of reality, they are only useful if we have realistic parameters for the molecular reaction rates and cell physiological processes. There is currently no suitable framework to reliably estimate hundreds, let alone thousands, of reaction rate parameters. Here, we map out the relative weaknesses and promises of different approaches aimed at redressing this issue. While suitable procedures for estimation or inference of the whole (vast) set of parameters will, in all likelihood, remain elusive, some hope can be drawn from the fact that much of the cellular behaviour may be explained in terms of smaller sets of parameters. Identifying such parameter sets and assessing their behaviour is now becoming possible even for very large systems of equations, and we expect such methods to become central tools in the development and analysis of whole-cell models.
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Affiliation(s)
- Ann C Babtie
- Department of Life Sciences, Imperial College London, London, UK
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29
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Abstract
Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally.
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Affiliation(s)
- Marcus Thomas
- Computational Biology Department, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, United States of America. Joint Carnegie Mellon University/University of Pittsburgh Ph.D. Program in Computational Biology, 4400 Fifth Avenue, Pittsburgh, PA 15213, United States of America
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30
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Menon G, Krishnan J. Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules. J Chem Phys 2017; 145:035103. [PMID: 27448907 DOI: 10.1063/1.4953914] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.
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Affiliation(s)
- Govind Menon
- Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - J Krishnan
- Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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31
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Loewe L, Scheuer KS, Keel SA, Vyas V, Liblit B, Hanlon B, Ferris MC, Yin J, Dutra I, Pietsch A, Javid CG, Moog CL, Meyer J, Dresel J, McLoone B, Loberger S, Movaghar A, Gilchrist-Scott M, Sabri Y, Sescleifer D, Pereda-Zorrilla I, Zietlow A, Smith R, Pietenpol S, Goldfinger J, Atzen SL, Freiberg E, Waters NP, Nusbaum C, Nolan E, Hotz A, Kliman RM, Mentewab A, Fregien N, Loewe M. Evolvix BEST Names for semantic reproducibility across code2brain interfaces. Ann N Y Acad Sci 2016; 1387:124-144. [PMID: 27918836 PMCID: PMC5299481 DOI: 10.1111/nyas.13192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 07/12/2016] [Indexed: 11/30/2022]
Abstract
Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general‐purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long‐term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder‐brains to reader‐brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core.
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Affiliation(s)
- Laurence Loewe
- Wisconsin Institute for Discovery.,Laboratory of Genetics
| | | | - Seth A Keel
- Wisconsin Institute for Discovery.,Laboratory of Genetics
| | | | | | | | - Michael C Ferris
- Wisconsin Institute for Discovery.,Departments of Computer Sciences
| | - John Yin
- Wisconsin Institute for Discovery.,Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Inês Dutra
- Department of Computer Science, University of Porto, Porto, Portugal
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Richard M Kliman
- Department of Biology, Cedar Crest College, Allentown, Pennsylvania
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32
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Waltemath D, Karr JR, Bergmann FT, Chelliah V, Hucka M, Krantz M, Liebermeister W, Mendes P, Myers CJ, Pir P, Alaybeyoglu B, Aranganathan NK, Baghalian K, Bittig AT, Burke PEP, Cantarelli M, Chew YH, Costa RS, Cursons J, Czauderna T, Goldberg AP, Gomez HF, Hahn J, Hameri T, Gardiol DFH, Kazakiewicz D, Kiselev I, Knight-Schrijver V, Knupfer C, Konig M, Lee D, Lloret-Villas A, Mandrik N, Medley JK, Moreau B, Naderi-Meshkin H, Palaniappan SK, Priego-Espinosa D, Scharm M, Sharma M, Smallbone K, Stanford NJ, Song JH, Theile T, Tokic M, Tomar N, Toure V, Uhlendorf J, Varusai TM, Watanabe LH, Wendland F, Wolfien M, Yurkovich JT, Zhu Y, Zardilis A, Zhukova A, Schreiber F. Toward Community Standards and Software for Whole-Cell Modeling. IEEE Trans Biomed Eng 2016; 63:2007-14. [PMID: 27305665 PMCID: PMC5451320 DOI: 10.1109/tbme.2016.2560762] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. METHODS We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. RESULTS Our analysis revealed several challenges to representing WC models using the current standards. CONCLUSION We, therefore, propose several new WC modeling standards, software, and databases. SIGNIFICANCE We anticipate that these new standards and software will enable more comprehensive models.
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33
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Manzoni R, Urrios A, Velazquez-Garcia S, de Nadal E, Posas F. Synthetic biology: insights into biological computation. Integr Biol (Camb) 2016; 8:518-32. [DOI: 10.1039/c5ib00274e] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Synthetic biology attempts to rationally engineer biological systems in order to perform desired functions. Our increasing understanding of biological systems guides this rational design, while the huge background in electronics for building circuits defines the methodology.
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Affiliation(s)
- Romilde Manzoni
- Cell Signaling Research Group
- Departament de Ciències Experimentals i de la Salut
- Universitat Pompeu Fabra (UPF)
- E-08003 Barcelona
- Spain
| | - Arturo Urrios
- Cell Signaling Research Group
- Departament de Ciències Experimentals i de la Salut
- Universitat Pompeu Fabra (UPF)
- E-08003 Barcelona
- Spain
| | - Silvia Velazquez-Garcia
- Cell Signaling Research Group
- Departament de Ciències Experimentals i de la Salut
- Universitat Pompeu Fabra (UPF)
- E-08003 Barcelona
- Spain
| | - Eulàlia de Nadal
- Cell Signaling Research Group
- Departament de Ciències Experimentals i de la Salut
- Universitat Pompeu Fabra (UPF)
- E-08003 Barcelona
- Spain
| | - Francesc Posas
- Cell Signaling Research Group
- Departament de Ciències Experimentals i de la Salut
- Universitat Pompeu Fabra (UPF)
- E-08003 Barcelona
- Spain
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34
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Carrera J, Covert MW. Why Build Whole-Cell Models? Trends Cell Biol 2015; 25:719-722. [PMID: 26471224 DOI: 10.1016/j.tcb.2015.09.004] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 09/11/2015] [Accepted: 09/14/2015] [Indexed: 10/22/2022]
Abstract
Our ability to build computational models that account for all known gene functions in a cell has increased dramatically. But why build whole-cell models, and how can they best be used? In this forum, we enumerate several areas in which whole-cell modeling can significantly impact research and technology.
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Affiliation(s)
- Javier Carrera
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, CA 94305-4125, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, CA 94305-4125, USA.
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35
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Creixell P, Reimand J, Haider S, Wu G, Shibata T, Vazquez M, Mustonen V, Gonzalez-Perez A, Pearson J, Sander C, Raphael BJ, Marks DS, Ouellette BFF, Valencia A, Bader GD, Boutros PC, Stuart JM, Linding R, Lopez-Bigas N, Stein LD. Pathway and network analysis of cancer genomes. Nat Methods 2015; 12:615-621. [PMID: 26125594 DOI: 10.1038/nmeth.3440] [Citation(s) in RCA: 218] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 04/27/2015] [Indexed: 12/26/2022]
Abstract
Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.
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Affiliation(s)
- Pau Creixell
- Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark
| | - Jüri Reimand
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Syed Haider
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Guanming Wu
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Tatsuhiro Shibata
- Division of Cancer Genomics, National Cancer Center, Chuo-ku, Tokyo, Japan
| | - Miguel Vazquez
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Ville Mustonen
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Abel Gonzalez-Perez
- Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain
| | - John Pearson
- Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Chris Sander
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Benjamin J Raphael
- Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA USA
| | - B F Francis Ouellette
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Alfonso Valencia
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Paul C Boutros
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Joshua M Stuart
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.,Center for Biomolecular Science and Engineering, University of California, Santa Cruz, California, USA
| | - Rune Linding
- Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark.,Biotech Research & Innovation Centre (BRIC), University of Copenhagen (UCPH), DK-2200 Copenhagen, Denmark
| | - Nuria Lopez-Bigas
- Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
| | - Lincoln D Stein
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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36
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Abstract
Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units.
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37
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Karr JR, Takahashi K, Funahashi A. The principles of whole-cell modeling. Curr Opin Microbiol 2015; 27:18-24. [PMID: 26115539 DOI: 10.1016/j.mib.2015.06.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 05/25/2015] [Accepted: 06/05/2015] [Indexed: 11/17/2022]
Abstract
Whole-cell models which comprehensively predict how phenotypes emerge from genotype promise to enable rational bioengineering and precision medicine. Here, we outline the key principles of whole-cell modeling which have emerged from our work developing bacterial whole-cell models: single-cellularity; functional, genetic, molecular, and temporal completeness; biophysical realism including temporal dynamics and stochastic variation; species-specificity; and model integration and reproducibility. We also outline the whole-cell model construction process, highlighting existing resources. Numerous challenges remain to achieving fully complete models including developing new experimental tools to more completely characterize cells and developing a strong theoretical understanding of hybrid mathematics. Solving these challenges requires collaboration among computational and experimental biologists, biophysicists, biochemists, applied mathematicians, computer scientists, and software engineers.
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Affiliation(s)
- Jonathan R Karr
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Koichi Takahashi
- RIKEN Quantitative Biology Center, RIKEN, Osaka 565-0874, Japan; Institute for Advanced Biosciences, Keio University, Fujisawa 252-8520, Japan
| | - Akira Funahashi
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan
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38
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Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models. PLoS Comput Biol 2015; 11:e1004096. [PMID: 26020786 PMCID: PMC4447414 DOI: 10.1371/journal.pcbi.1004096] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation. Whole-cell models promise to enable rational bioengineering by predicting how cells behave. Even for simple bacteria, whole-cell models require thousands of parameters, many of which are poorly characterized or unknown. New approaches are needed to estimate these parameters. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new approaches for whole-cell model parameter identification. Here we describe the challenge, the best performing methods, new insights into the identifiability of whole-cell models, and several lessons we learned for improving future challenges. Going forward, we believe that collaborative efforts have the potential to produce powerful tools for identifying whole-cell models.
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39
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Torres-Puig S, Broto A, Querol E, Piñol J, Pich OQ. A novel sigma factor reveals a unique regulon controlling cell-specific recombination in Mycoplasma genitalium. Nucleic Acids Res 2015; 43:4923-36. [PMID: 25925568 PMCID: PMC4446450 DOI: 10.1093/nar/gkv422] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 04/19/2015] [Indexed: 11/22/2022] Open
Abstract
The Mycoplasma genitalium MG428 protein shows homology to members of the sigma-70 family of sigma factors. Herein, we found that MG428 activates transcription of recA, ruvA and ruvB as well as several genes with unknown function. Deletion of MG_428 or some of the up-regulated unknown genes led to severe recombination defects. Single cell analyses revealed that activation of the MG428-regulon is a rare event under laboratory growth conditions. A conserved sequence with sigma-70 promoter architecture (TTGTCA-N18/19-ATTWAT) was identified in the upstream region of all of the MG428-regulated genes or operons. Primer extension analyses demonstrated that transcription initiates immediately downstream of this sigma70-type promoter in a MG428-dependent manner. Furthermore, mutagenesis of the conserved −10 and −35 elements corroborated the requirement of these regions for promoter function. Therefore, a new mycoplasma promoter directs transcription of a unique recombination regulon. Additionally, MG428 was found to interact with the RNAP core enzyme, reinforcing the predicted role of this protein as an alternative sigma factor. Finally, our results indicate that MG428 contributes to the generation of genetic diversity in this model organism. Since recombination is an important mechanism to generate antigenic variation, MG428 emerges as a novel factor contributing to M. genitalium virulence.
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Affiliation(s)
- Sergi Torres-Puig
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular. Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
| | - Alicia Broto
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular. Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
| | - Enrique Querol
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular. Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
| | - Jaume Piñol
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular. Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
| | - Oscar Q Pich
- Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular. Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
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40
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Christley S, Cockrell C, An G. Computational Studies of the Intestinal Host-Microbiota Interactome. COMPUTATION (BASEL, SWITZERLAND) 2015; 3:2-28. [PMID: 34765258 PMCID: PMC8580329 DOI: 10.3390/computation3010002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.
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Affiliation(s)
- Scott Christley
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
| | - Chase Cockrell
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
| | - Gary An
- Department of Surgery, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
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41
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O'Brien EJ, Palsson BO. Computing the functional proteome: recent progress and future prospects for genome-scale models. Curr Opin Biotechnol 2015; 34:125-34. [PMID: 25576845 DOI: 10.1016/j.copbio.2014.12.017] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Revised: 12/16/2014] [Accepted: 12/17/2014] [Indexed: 11/18/2022]
Abstract
Constraint-based models enable the computation of feasible, optimal, and realized biological phenotypes from reaction network reconstructions and constraints on their operation. To date, stoichiometric reconstructions have largely focused on metabolism, resulting in genome-scale metabolic models (M-Models). Recent expansions in network content to encompass proteome synthesis have resulted in models of metabolism and protein expression (ME-Models). ME-Models advance the predictions possible with constraint-based models from network flux states to the spatially resolved molecular composition of a cell. Specifically, ME-Models enable the prediction of transcriptome and proteome allocation and limitations, and basal expression states and regulatory needs. Continued expansion in reconstruction content and constraints will result in an increasingly refined representation of cellular composition and behavior.
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Affiliation(s)
- Edward J O'Brien
- Bioinformatics and Systems Biology Program, University of California, San Diego, United States; Department of Bioengineering, University of California, San Diego, United States
| | - Bernhard O Palsson
- Bioinformatics and Systems Biology Program, University of California, San Diego, United States; Department of Bioengineering, University of California, San Diego, United States; Department of Pediatrics, University of California, San Diego, United States; Novo Nordisk Center for Biosustainability, The Danish Technical University, Denmark.
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42
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Payne SH. The utility of protein and mRNA correlation. Trends Biochem Sci 2014; 40:1-3. [PMID: 25467744 DOI: 10.1016/j.tibs.2014.10.010] [Citation(s) in RCA: 132] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2014] [Revised: 10/27/2014] [Accepted: 10/30/2014] [Indexed: 01/08/2023]
Abstract
Transcriptomic, proteomic, and metabolomic measurements are revolutionizing the way we model and predict cellular behavior, and multi-omic comparisons are being published with increased regularity. Some have expected a trivial and predictable correlation between mRNA and protein; however, the manifest complexity of biological regulation suggests a more nuanced relationship. Indeed, observing this lack of strict correlation provides clues for new research topics, and has the potential for transformative biological insight.
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Affiliation(s)
- Samuel H Payne
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99354, USA.
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43
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Guo H, Chen C, Lee DJ, Wang A, Gao D, Ren N. Coupled carbon, sulfur and nitrogen cycles of mixotrophic growth of Pseudomonas sp. C27 under denitrifying sulfide removal conditions. BIORESOURCE TECHNOLOGY 2014; 171:120-6. [PMID: 25189517 DOI: 10.1016/j.biortech.2014.08.035] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2014] [Revised: 08/03/2014] [Accepted: 08/05/2014] [Indexed: 05/08/2023]
Abstract
Pseudomonas sp. C27 is a facultative autotrophic bacterium (FAB) that can effectively conduct mixotrophic denitrifying sulfide removal (DSR) reactions using organic matters and sulfide as electron donors. Quantitative proteomics analysis of C27 using isobaric tag for relative and absolute quantitation (iTRAQ) and bioinformatics techniques identified 1916 unique proteins, based on which a novel pathway utilizing couple carbon, sulfide and nitrogen cycles for mixotrophic growth of C27. DSR experiments at different C/N ratios confirmed the presence of the new pathway. This novel pathway may be of great significance for C27-alike strains to conduct sulfide and nitrate removals in biological treatments.
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Affiliation(s)
- Hongliang Guo
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Chuan Chen
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Duu-Jong Lee
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan; Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan.
| | - Aijie Wang
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
| | - Dawen Gao
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; School of Forestry, Northeast Forestry University, Harbin 150040, China
| | - Nanqi Ren
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
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Karr JR, Phillips NC, Covert MW. WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau095. [PMID: 25231498 PMCID: PMC4165886 DOI: 10.1093/database/bau095] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Mechanistic ‘whole-cell’ models are needed to develop a complete understanding of cell physiology. However, extracting biological insights from whole-cell models requires running and analyzing large numbers of simulations. We developed WholeCellSimDB, a database for organizing whole-cell simulations. WholeCellSimDB was designed to enable researchers to search simulation metadata to identify simulations for further analysis, and quickly slice and aggregate simulation results data. In addition, WholeCellSimDB enables users to share simulations with the broader research community. The database uses a hybrid relational/hierarchical data format architecture to efficiently store and retrieve both simulation setup metadata and results data. WholeCellSimDB provides a graphical Web-based interface to search, browse, plot and export simulations; a JavaScript Object Notation (JSON) Web service to retrieve data for Web-based visualizations; a command-line interface to deposit simulations; and a Python API to retrieve data for advanced analysis. Overall, we believe WholeCellSimDB will help researchers use whole-cell models to advance basic biological science and bioengineering. Database URL:http://www.wholecellsimdb.org Source code repository URL:http://github.com/CovertLab/WholeCellSimDB
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Affiliation(s)
- Jonathan R Karr
- Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA, Computer Science and Information Technology, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada and Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Nolan C Phillips
- Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA, Computer Science and Information Technology, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada and Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Markus W Covert
- Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA, Computer Science and Information Technology, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada and Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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Macklin DN, Ruggero NA, Covert MW. The future of whole-cell modeling. Curr Opin Biotechnol 2014; 28:111-5. [PMID: 24556244 DOI: 10.1016/j.copbio.2014.01.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Revised: 01/19/2014] [Accepted: 01/20/2014] [Indexed: 12/21/2022]
Abstract
Integrated whole-cell modeling is poised to make a dramatic impact on molecular and systems biology, bioengineering, and medicine--once certain obstacles are overcome. From our group's experience building a whole-cell model of Mycoplasma genitalium, we identified several significant challenges to building models of more complex cells. Here we review and discuss these challenges in seven areas: first, experimental interrogation; second, data curation; third, model building and integration; fourth, accelerated computation; fifth, analysis and visualization; sixth, model validation; and seventh, collaboration and community development. Surmounting these challenges will require the cooperation of an interdisciplinary group of researchers to create increasingly sophisticated whole-cell models and make data, models, and simulations more accessible to the wider community.
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Affiliation(s)
- Derek N Macklin
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Nicholas A Ruggero
- Department of Chemical Engineering, Stanford University, Stanford, CA, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
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Link H, Christodoulou D, Sauer U. Advancing metabolic models with kinetic information. Curr Opin Biotechnol 2014; 29:8-14. [PMID: 24534671 DOI: 10.1016/j.copbio.2014.01.015] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Revised: 01/18/2014] [Accepted: 01/23/2014] [Indexed: 12/21/2022]
Abstract
Kinetic models are crucial to quantitatively understand and predict how functional behavior emerges from dynamic concentration changes of cellular components. The current challenge is on resolving uncertainties about parameter values of reaction kinetics. Additionally, there are also major structural uncertainties due to unknown molecular interactions and only putatively assigned regulatory functions. What if one or few key regulators of biochemical reactions are missing in a metabolic model? By reviewing current advances in building kinetic models of metabolism, we found that such models experience a paradigm shift away from fitting parameters towards identifying key regulatory interactions.
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Affiliation(s)
- Hannes Link
- Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland
| | - Dimitris Christodoulou
- Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland; Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland.
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