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Wilsdorf P, Reinhardt O, Prike T, Hinsch M, Bijak J, Uhrmacher AM. Simulation studies of social systems: telling the story based on provenance patterns. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240258. [PMID: 39113768 PMCID: PMC11304336 DOI: 10.1098/rsos.240258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 08/10/2024]
Abstract
Social simulation studies are complex. They typically combine various data sources and hypotheses about the system's mechanisms that are integrated by intertwined processes of model building, simulation experiment execution and analysis. Various documentation approaches exist to increase the transparency and traceability of complex social simulation studies. Provenance standards enable the formalization of information on sources and activities, which contribute to the generation of an entity, in a queryable and computationally accessible manner. Provenance patterns can be defined as constraints on the relationships between specific types of activities and entities of a simulation study. In this paper, we refine the provenance pattern-based approach to address specific challenges of social agent-based simulation studies. Specifically, we focus on the activities and entities involved in collecting and analysing primary data about human decisions, and the collection and quality assessment of secondary data. We illustrate the potential of this approach by applying it to central activities and results of an agent-based simulation project and by presenting its implementation in a web-based tool.
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Affiliation(s)
- Pia Wilsdorf
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Oliver Reinhardt
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Toby Prike
- School of Psychological Science, The University of Western Australia, Perth, Australia
| | - Martin Hinsch
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Jakub Bijak
- Department of Social Statistics and Demography, University of Southampton, Southampton, UK
| | - Adelinde M. Uhrmacher
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
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2
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Leonidou N, Ostyn L, Coenye T, Crabbé A, Dräger A. Genome-scale model of Rothia mucilaginosa predicts gene essentialities and reveals metabolic capabilities. Microbiol Spectr 2024; 12:e0400623. [PMID: 38652457 PMCID: PMC11237427 DOI: 10.1128/spectrum.04006-23] [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: 11/28/2023] [Accepted: 03/20/2024] [Indexed: 04/25/2024] Open
Abstract
Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled its anti-inflammatory properties using in vitro three-dimensional lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from this, R. mucilaginosa has been associated with severe infections. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. To gain insights into its cellular metabolism and genetic content, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetics and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model's effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knockouts and mutant transposon libraries. Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa's metabolism for desired performance.IMPORTANCECystic fibrosis (CF) is a genetic disorder characterized by thick mucosal secretions, leading to chronic lung infections. Rothia mucilaginosa is a common bacterium found in various parts of the human body, acting as a normal part of the flora. In people with weakened immune systems, it can become an opportunistic pathogen, while it is prevalent and active in CF airways. Recent studies have highlighted its anti-inflammatory properties in the lower pulmonary system, indicating the intricate relationship between microbes and human health. Herein, we have developed the first manually curated metabolic model of R. mucilaginosa. Our study examined the previously unknown relationships between the bacterium's genotype and phenotype and identified essential genes that impact the metabolism under various conditions. With this, we opt for paving the way for developing new strategies in antimicrobial therapy and metabolic engineering, leading to enhanced therapeutic outcomes in cystic fibrosis and related conditions.
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Affiliation(s)
- Nantia Leonidou
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Lisa Ostyn
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Tom Coenye
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Aurélie Crabbé
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
- Data Analytics and Bioinformatics, Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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3
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Andrews SS, Wiley HS, Sauro HM. Design patterns of biological cells. Bioessays 2024; 46:e2300188. [PMID: 38247191 PMCID: PMC10922931 DOI: 10.1002/bies.202300188] [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: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.
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Affiliation(s)
- Steven S. Andrews
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - H. Steven Wiley
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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4
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Golebiewski M, Bader G, Gleeson P, Gorochowski TE, Keating SM, König M, Myers CJ, Nickerson DP, Sommer B, Waltemath D, Schreiber F. Specifications of standards in systems and synthetic biology: status, developments, and tools in 2024. J Integr Bioinform 2024; 21:jib-2024-0015. [PMID: 39026464 DOI: 10.1515/jib-2024-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Affiliation(s)
- Martin Golebiewski
- 40092 Heidelberg Institute for Theoretical Studies (HITS) , Heidelberg, Germany
| | | | - Padraig Gleeson
- Dept. of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | | | - Sarah M Keating
- Advanced Research Computing Centre, University College London, London, UK
| | - Matthias König
- 9373 Institute for Biology, Institute for Theoretical Biology, Humboldt-University Berlin , Berlin, Germany
| | - Chris J Myers
- Dept. of Electrical, Computer, and Energy Eng., University of Colorado Boulder, Boulder, USA
| | - David P Nickerson
- 428614 Auckland Bioengineering Institute, University of Auckland , Auckland, New Zealand
| | | | - Dagmar Waltemath
- Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany
| | - Falk Schreiber
- Dept. of Computer and Information Science, 26567 University of Konstanz , Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Australia
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5
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Casini I, McCubbin T, Esquivel-Elizondo S, Luque GG, Evseeva D, Fink C, Beblawy S, Youngblut ND, Aristilde L, Huson DH, Dräger A, Ley RE, Marcellin E, Angenent LT, Molitor B. An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter. iScience 2023; 26:108016. [PMID: 37854702 PMCID: PMC10579436 DOI: 10.1016/j.isci.2023.108016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/29/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023] Open
Abstract
Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.
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Affiliation(s)
- Isabella Casini
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Tim McCubbin
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Metabolomics and Proteomics (Q-MAP), The University of Queensland, Brisbane, QLD 4072, Australia
- ARC Centre of Excellence in Synthetic Biology (COESB), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Sofia Esquivel-Elizondo
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Guillermo G. Luque
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Daria Evseeva
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
| | - Christian Fink
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Sebastian Beblawy
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
| | - Nicholas D. Youngblut
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
| | - Ludmilla Aristilde
- Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Daniel H. Huson
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Andreas Dräger
- Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Ruth E. Ley
- Department of Microbiome Science, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - Esteban Marcellin
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Metabolomics and Proteomics (Q-MAP), The University of Queensland, Brisbane, QLD 4072, Australia
- ARC Centre of Excellence in Synthetic Biology (COESB), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Largus T. Angenent
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
- AG Angenent, Max Planck Institute for Biology Tübingen, Max-Planck-Ring 5, 72076 Tübingen, Germany
- Department of Biological and Chemical Engineering, Aarhus University, Gustav Wieds Vej 10D, 8000 Aarhus C, Denmark
- The Novo Nordisk Foundation CO2 Research Center (CORC), Aarhus University, Gustav Wieds Vej 10C, 8000 Aarhus C, Denmark
| | - Bastian Molitor
- Environmental Biotechnology Group, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany
- Cluster of Excellence – Controlling Microbes to Fight Infections, University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
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6
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Tatka LT, Smith LP, Hellerstein JL, Sauro HM. Adapting modeling and simulation credibility standards to computational systems biology. J Transl Med 2023; 21:501. [PMID: 37496031 PMCID: PMC10369698 DOI: 10.1186/s12967-023-04290-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 06/19/2023] [Indexed: 07/28/2023] Open
Abstract
Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as forms of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrower domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models.
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Affiliation(s)
- Lillian T Tatka
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Lucian P Smith
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | | | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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7
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Mendes P. Reproducibility and FAIR principles: the case of a segment polarity network model. Front Cell Dev Biol 2023; 11:1201673. [PMID: 37346177 PMCID: PMC10279958 DOI: 10.3389/fcell.2023.1201673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023] Open
Abstract
The issue of reproducibility of computational models and the related FAIR principles (findable, accessible, interoperable, and reusable) are examined in a specific test case. I analyze a computational model of the segment polarity network in Drosophila embryos published in 2000. Despite the high number of citations to this publication, 23 years later the model is barely accessible, and consequently not interoperable. Following the text of the original publication allowed successfully encoding the model for the open source software COPASI. Subsequently saving the model in the SBML format allowed it to be reused in other open source software packages. Submission of this SBML encoding of the model to the BioModels database enables its findability and accessibility. This demonstrates how the FAIR principles can be successfully enabled by using open source software, widely adopted standards, and public repositories, facilitating reproducibility and reuse of computational cell biology models that will outlive the specific software used.
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Affiliation(s)
- Pedro Mendes
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT, United States
- Department of Cell Biology, University of Connecticut School of Medicine, Farmington, CT, United States
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8
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Shin J, Porubsky V, Carothers J, Sauro HM. Standards, dissemination, and best practices in systems biology. Curr Opin Biotechnol 2023; 81:102922. [PMID: 37004298 PMCID: PMC10435326 DOI: 10.1016/j.copbio.2023.102922] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/14/2023] [Accepted: 02/24/2023] [Indexed: 04/03/2023]
Abstract
The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.
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Affiliation(s)
- Janis Shin
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA
| | - Veronica Porubsky
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - James Carothers
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
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9
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Giess T, Itzigehl S, Range J, Schömig R, Bruckner JR, Pleiss J. FAIR and scalable management of small-angle X-ray scattering data. J Appl Crystallogr 2023; 56:565-575. [PMID: 37032968 PMCID: PMC10077856 DOI: 10.1107/s1600576723001577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/21/2023] [Indexed: 04/07/2023] Open
Abstract
A modular and extensible research data management toolbox based on the programming language Python and the widely used computing platform Jupyter Notebook has been established for the acquisition, visualization, analysis and storage of small-angle X-ray scattering data. A modular research data management toolbox based on the programming language Python, the widely used computing platform Jupyter Notebook, the standardized data exchange format for analytical data (AnIML) and the generic repository Dataverse has been established and applied to analyze small-angle X-ray scattering (SAXS) data according to the FAIR data principles (findable, accessible, interoperable and reusable). The SAS-tools library is a community-driven effort to develop tools for data acquisition, analysis, visualization and publishing of SAXS data. Metadata from the experiment and the results of data analysis are stored as an AnIML document using the novel Python-native pyAnIML API. The AnIML document, measured raw data and plots resulting from the analysis are combined into an archive in OMEX format and uploaded to Dataverse using the novel easyDataverse API, which makes each data set accessible via a unique DOI and searchable via a structured metadata block. SAS-tools is applied to study the effects of alkyl chain length and counterions on the phase diagrams of alkyltrimethylammonium surfactants in order to demonstrate the feasibility and usefulness of a scalable data management workflow for experiments in physical chemistry.
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Affiliation(s)
- Torsten Giess
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Selina Itzigehl
- Institute of Physical Chemistry, University of Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
| | - Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Richard Schömig
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
| | - Johanna R. Bruckner
- Institute of Physical Chemistry, University of Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, Stuttgart 70569, Germany
- Correspondence e-mail:
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10
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Sents Z, Stoughton TE, Buecherl L, Thomas PJ, Fontanarrosa P, Myers CJ. SynBioSuite: A Tool for Improving the Workflow for Genetic Design and Modeling. ACS Synth Biol 2023; 12:892-897. [PMID: 36888740 DOI: 10.1021/acssynbio.2c00597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Synthetic biology research has led to the development of many software tools for designing, constructing, editing, simulating, and sharing genetic parts and circuits. Among these tools are SBOLCanvas, iBioSim, and SynBioHub, which can be used in conjunction to create a genetic circuit design following the design-build-test-learn process. However, although automation works within these tools, most of these software tools are not integrated, and the process of transferring information between them is a very manual, error-prone process. To address this problem, this work automates some of these processes and presents SynBioSuite, a cloud-based tool that eliminates many of the drawbacks of the current approach by automating the setup and reception of results for simulating a designed genetic circuit via an application programming interface.
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Affiliation(s)
- Zachary Sents
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Thomas E Stoughton
- Department of Computer Science, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Lukas Buecherl
- Biomedical Engineering Program, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Payton J Thomas
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Pedro Fontanarrosa
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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11
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Leonidou N, Renz A, Mostolizadeh R, Dräger A. New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells. PLoS Comput Biol 2023; 19:e1010903. [PMID: 36952396 PMCID: PMC10035753 DOI: 10.1371/journal.pcbi.1010903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/30/2023] [Indexed: 03/25/2023] Open
Abstract
COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets' inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.
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Affiliation(s)
- Nantia Leonidou
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Reihaneh Mostolizadeh
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Germany
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12
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Mante J, Abam J, Samineni SP, Pötzsch IM, Beal J, Myers CJ. Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa. ACS Synth Biol 2023; 12:340-346. [PMID: 36595709 DOI: 10.1021/acssynbio.2c00521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel-SBOL Converter was developed to facilitate the use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin.
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Julian Abam
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Sai P Samineni
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Chris J Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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13
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Porubsky VL, Sauro HM. A Practical Guide to Reproducible Modeling for Biochemical Networks. Methods Mol Biol 2023; 2634:107-138. [PMID: 37074576 DOI: 10.1007/978-1-0716-3008-2_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
While scientific disciplines revere reproducibility, many studies - experimental and computational alike - fall short of this ideal and cannot be reproduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to support reproducibility. This chapter points the reader to useful software tools and standardized formats that support reproducible modeling of biochemical networks and provides suggestions on how to implement reproducible methods in practice. Many of the suggestions encourage readers to use best practices from the software development community in order to automate, test, and version control their model components. A Jupyter Notebook demonstrating several of the key steps in building a reproducible biochemical network model is included to supplement the recommendations in the text.
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Affiliation(s)
| | - Herbert M Sauro
- University of Washington, Department of Bioengineering, Seattle, WA, USA
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14
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Mostolizadeh R, Glöckler M, Dräger A. Towards the human nasal microbiome: Simulating D. pigrum and S. aureus. Front Cell Infect Microbiol 2022; 12:925215. [PMID: 36605126 PMCID: PMC9810029 DOI: 10.3389/fcimb.2022.925215] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/15/2022] [Indexed: 01/12/2023] Open
Abstract
The human nose harbors various microbes that decisively influence the wellbeing and health of their host. Among the most threatening pathogens in this habitat is Staphylococcus aureus. Multiple epidemiological studies identify Dolosigranulum pigrum as a likely beneficial bacterium based on its positive association with health, including negative associations with S. aureus. Carefully curated GEMs are available for both bacterial species that reliably simulate their growth behavior in isolation. To unravel the mutual effects among bacteria, building community models for simulating co-culture growth is necessary. However, modeling microbial communities remains challenging. This article illustrates how applying the NCMW fosters our understanding of two microbes' joint growth conditions in the nasal habitat and their intricate interplay from a metabolic modeling perspective. The resulting community model combines the latest available curated GEMs of D. pigrum and S. aureus. This uses case illustrates how to incorporate genuine GEM of participating microorganisms and creates a basic community model mimicking the human nasal environment. Our analysis supports the role of negative microbe-microbe interactions involving D. pigrum examined experimentally in the lab. By this, we identify and characterize metabolic exchange factors involved in a specific interaction between D. pigrum and S. aureus as an in silico candidate factor for a deep insight into the associated species. This method may serve as a blueprint for developing more complex microbial interaction models. Its direct application suggests new ways to prevent disease-causing infections by inhibiting the growth of pathogens such as S. aureus through microbe-microbe interactions.
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Affiliation(s)
- Reihaneh Mostolizadeh
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany,Department of Computer Science, University of Tübingen, Tübingen, Germany,German Center for Infection Research (DZIF), Partner site, Tübingen, Germany,Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany,*Correspondence: Reihaneh Mostolizadeh,
| | - Manuel Glöckler
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany,Department of Computer Science, University of Tübingen, Tübingen, Germany,German Center for Infection Research (DZIF), Partner site, Tübingen, Germany,Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany
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15
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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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16
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Ngo RJK, Yeoh JW, Fan GHW, Loh WKS, Poh CL. BMSS2: A Unified Database-Driven Modeling Tool for Systematic Biomodel Selection. ACS Synth Biol 2022; 11:2901-2906. [PMID: 35866653 DOI: 10.1021/acssynbio.2c00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Modeling in synthetic biology constitutes a powerful means in our continuous search for improved performance with a rational Design-Build-Test-Learn approach. Particularly, kinetic models unravel system dynamics and enable system analysis for guiding experimental design. However, a systematic yet modular pipeline that allows one to identify the appropriate model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we develop BMSS2, a unified tool that streamlines and automates model selection by combining information criterion ranking with upstream and parallel analysis algorithms. These include Bayesian parameter inference, a priori and a posteriori identifiability analysis, and global sensitivity analysis. In addition, the database-driven design supports interactive model storage/retrieval to encourage reusability and facilitate automated model selection. This allows ease of model manipulation and deposition for the selection and analysis, thus enabling better utilization of models in guiding experimental design.
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Affiliation(s)
- Russell Jie Kai Ngo
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore 117583.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456
| | - Jing Wui Yeoh
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore 117583.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456
| | - Gerald Horng Wei Fan
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore 117583
| | - Wilbert Keat Siang Loh
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore 117583
| | - Chueh Loo Poh
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore 117583.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456
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17
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Niarakis A, Waltemath D, Glazier J, Schreiber F, Keating SM, Nickerson D, Chaouiya C, Siegel A, Noël V, Hermjakob H, Helikar T, Soliman S, Calzone L. Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology. Brief Bioinform 2022; 23:bbac212. [PMID: 35671510 PMCID: PMC9294410 DOI: 10.1093/bib/bbac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/20/2022] [Accepted: 05/06/2022] [Indexed: 11/14/2022] Open
Abstract
Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.
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Affiliation(s)
- Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Dagmar Waltemath
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - James Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Australia
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Anne Siegel
- Univ Rennes, CNRS, Inria - IRISA lab. Rennes
| | - Vincent Noël
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Henning Hermjakob
- EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
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18
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Buecherl L, Myers CJ. Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology. Curr Opin Microbiol 2022; 68:102155. [PMID: 35588683 DOI: 10.1016/j.mib.2022.102155] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 01/23/2023]
Abstract
Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.
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Affiliation(s)
- Lukas Buecherl
- Biomedical Engineering Program, University of Colorado Boulder, 1111 Engineering Drive, Boulder, 80309 CO, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, 425 UCB, Boulder, 80309 CO, United States.
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19
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Shaikh B, Smith LP, Vasilescu D, Marupilla G, Wilson M, Agmon E, Agnew H, Andrews SS, Anwar A, Beber ME, Bergmann FT, Brooks D, Brusch L, Calzone L, Choi K, Cooper J, Detloff J, Drawert B, Dumontier M, Ermentrout G, Faeder J, Freiburger A, Fröhlich F, Funahashi A, Garny A, Gennari J, Gleeson P, Goelzer A, Haiman Z, Hasenauer J, Hellerstein J, Hermjakob H, Hoops S, Ison J, Jahn D, Jakubowski H, Jordan R, Kalaš M, König M, Liebermeister W, Sheriff RM, Mandal S, McDougal R, Medley J, Mendes P, Müller R, Myers C, Naldi A, Nguyen TVN, Nickerson D, Olivier B, Patoliya D, Paulevé L, Petzold L, Priya A, Rampadarath A, Rohwer JM, Saglam A, Singh D, Sinha A, Snoep J, Sorby H, Spangler R, Starruß J, Thomas P, van Niekerk D, Weindl D, Zhang F, Zhukova A, Goldberg A, Schaff J, Blinov M, Sauro H, Moraru I, Karr J. BioSimulators: a central registry of simulation engines and services for recommending specific tools. Nucleic Acids Res 2022; 50:W108-W114. [PMID: 35524558 PMCID: PMC9252793 DOI: 10.1093/nar/gkac331] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/07/2022] [Accepted: 04/20/2022] [Indexed: 11/30/2022] Open
Abstract
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.
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Affiliation(s)
- Bilal Shaikh
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Dan Vasilescu
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | | | - Michael Wilson
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Eran Agmon
- Stanford University, Stanford, CA 94305, USA
| | | | | | - Azraf Anwar
- New York University, Brooklyn, NY 11201, USA
| | | | | | - David Brooks
- University of Auckland, 1010 Auckland, New Zealand
| | - Lutz Brusch
- Technical University of Dresden, 01187 Dresden, Germany
| | | | - Kiri Choi
- Korea Institute for Advanced Study, 02455 Seoul, South Korea
| | - Joshua Cooper
- University of North Carolina, Asheville, Ashville, NC 28804, USA
| | | | - Brian Drawert
- University of North Carolina, Asheville, Ashville, NC 28804, USA
| | | | | | | | | | | | | | - Alan Garny
- University of Auckland, 1010 Auckland, New Zealand
| | | | | | - Anne Goelzer
- Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | - Zachary Haiman
- University of California, San Diego, La Jolla, CA 92093, USA
| | | | | | - Henning Hermjakob
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Stefan Hoops
- University of Virginia, Charlottesville, VA 22904, USA
| | - Jon C Ison
- CNRS, UMS 3601, Institut Français de Bioinformatique, IFB-core, 91000 Évry-Courcouronnes, France
| | - Diego Jahn
- Technical University of Dresden, 01187 Dresden, Germany
| | - Henry V Jakubowski
- College of Saint Benedict and Saint John’s University, St. Joseph, MN 56374, USA
| | - Ryann Jordan
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | | | | | - Rahuman S Malik Sheriff
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | | | | | | | - Pedro Mendes
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Robert Müller
- Technical University of Dresden, 01187 Dresden, Germany
| | - Chris J Myers
- University of Colorado at Boulder, Boulder CO, 80309, USA
| | - Aurelien Naldi
- Inria Saclay - Île-de-France Research Centre, 91120 Palaiseau, France
| | - Tung V N Nguyen
- European Molecular Biology Laboratory - European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | | | - Brett G Olivier
- Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, Netherlands
| | - Drashti Patoliya
- Sarvajanik College of Engineering & Technology, Surat, Gujarat 395001, India
| | - Loïc Paulevé
- Centre National de la Recherche Scientifique, 33400 Talence, France
| | - Linda R Petzold
- University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Ankita Priya
- Birla Institute of Technology, Mesra, Jharkhand 835215, India
| | | | | | - Ali S Saglam
- University of Pittsburgh, Pittsburgh, PA 15260, USA
| | | | - Ankur Sinha
- University College London, London, WC1E 6BT, UK
| | - Jacky Snoep
- Stellenbosch University, Stellenbosch, 7600, South Africa
| | - Hugh Sorby
- University of Auckland, 1010 Auckland, New Zealand
| | - Ryan Spangler
- Allen Institute for Cell Science, Seattle, WA 98109, USA
| | - Jörn Starruß
- Technical University of Dresden, 01187 Dresden, Germany
| | | | | | - Daniel Weindl
- Helmholtz Zentrum München GmbH and German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Fengkai Zhang
- National Institutes of Health, Bethesda, MD 20892, USA
| | | | | | - James C Schaff
- University of Connecticut School of Medicine, Farmington, CT 06030, USA,Applied BioMath LLC, Concord, MA 01742, USA
| | - Michael L Blinov
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | | | - Ion I Moraru
- University of Connecticut School of Medicine, Farmington, CT 06030, USA
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20
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Modular Representation of Physiologically Based Pharmacokinetic Models: Nanoparticle Delivery to Solid Tumors in Mice as an Example. MATHEMATICS 2022. [DOI: 10.3390/math10071176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Here we describe a toolkit for presenting physiologically based pharmacokinetic (PBPK) models in a modular graphical view in the BioUML platform. Firstly, we demonstrate the BioUML capabilities for PBPK modeling tested on an existing model of nanoparticles delivery to solid tumors in mice. Secondly, we provide guidance on the conversion of the PBPK model code from a text modeling language like Berkeley Madonna to a visual modular diagram in the BioUML. We give step-by-step explanations of the model transformation and demonstrate that simulation results from the original model are exactly the same as numerical results obtained for the transformed model. The main advantage of the proposed approach is its clarity and ease of perception. Additionally, the modular representation serves as a simplified and convenient base for in silico investigation of the model and reduces the risk of technical errors during its reuse and extension by concomitant biochemical processes. In summary, this article demonstrates that BioUML can be used as an alternative and robust tool for PBPK modeling.
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21
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Fitzpatrick R, Stefan MI. Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways. Neuroinformatics 2022; 20:277-284. [PMID: 35543917 PMCID: PMC9537119 DOI: 10.1007/s12021-022-09584-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/17/2022] [Indexed: 01/09/2023]
Abstract
Computational modelling of biochemical reaction pathways is an increasingly important part of neuroscience research. In order to be useful, computational models need to be valid in two senses: First, they need to be consistent with experimental data and able to make testable predictions (external validity). Second, they need to be internally consistent and independently reproducible (internal validity). Here, we discuss both types of validity and provide a brief overview of tools and technologies used to ensure they are met. We also suggest the introduction of new collaborative technologies to ensure model validity: an incentivised experimental database for external validity and reproducibility audits for internal validity. Both rely on FAIR principles and on collaborative science practices.
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Affiliation(s)
- Richard Fitzpatrick
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK ,School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Melanie I. Stefan
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK ,ZJU-UoE Institute, Zhejiang University, Haining, China
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22
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Range J, Halupczok C, Lohmann J, Swainston N, Kettner C, Bergmann FT, Weidemann A, Wittig U, Schnell S, Pleiss J. EnzymeML-a data exchange format for biocatalysis and enzymology. FEBS J 2021; 289:5864-5874. [PMID: 34890097 DOI: 10.1111/febs.16318] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/15/2021] [Accepted: 12/09/2021] [Indexed: 11/30/2022]
Abstract
EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.
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Affiliation(s)
- Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Colin Halupczok
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Jens Lohmann
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
| | - Neil Swainston
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | | | | | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies, Germany
| | - Santiago Schnell
- Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Germany
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23
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Feierabend M, Renz A, Zelle E, Nöh K, Wiechert W, Dräger A. High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032. Front Microbiol 2021; 12:750206. [PMID: 34867870 PMCID: PMC8634658 DOI: 10.3389/fmicb.2021.750206] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/19/2021] [Indexed: 11/30/2022] Open
Abstract
Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.
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Affiliation(s)
- Martina Feierabend
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Elisabeth Zelle
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
- Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
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24
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Schreiber F, Gleeson P, Golebiewski M, Gorochowski TE, Hucka M, Keating SM, König M, Myers CJ, Nickerson DP, Sommer B, Waltemath D. Specifications of standards in systems and synthetic biology: status and developments in 2021. J Integr Bioinform 2021; 18:jib-2021-0026. [PMID: 34674411 PMCID: PMC8573232 DOI: 10.1515/jib-2021-0026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2021 special issue presents four updates of standards: Synthetic Biology Open Language Visual Version 2.3, Synthetic Biology Open Language Visual Version 3.0, Simulation Experiment Description Markup Language Level 1 Version 4, and OMEX Metadata specification Version 1.2. This document can also be consulted to identify the latest specifications of all COMBINE standards.
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Affiliation(s)
- Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Australia
| | | | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | | | | | | | - Matthias König
- Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Chris J. Myers
- Department of Electrical, Computer, and Energy Eng., University of Colorado Boulder, Boulder, USA
| | - David P. Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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25
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Payne DD, Renz A, Dunphy LJ, Lewis T, Dräger A, Papin JA. An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism. NPJ Syst Biol Appl 2021; 7:37. [PMID: 34625561 PMCID: PMC8501023 DOI: 10.1038/s41540-021-00198-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 09/17/2021] [Indexed: 12/30/2022] Open
Abstract
Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen, Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.
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Affiliation(s)
- Dawson D Payne
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Alina Renz
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Cluster of Excellence 'Controlling Microbes to Fight Infections', University of Tübingen, Tübingen, Germany
| | - Laura J Dunphy
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Taylor Lewis
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, MA, USA
| | - Andreas Dräger
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Cluster of Excellence 'Controlling Microbes to Fight Infections', University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF) partner site, Tübingen, Germany
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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26
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Akberdin IR, Kiselev IN, Pintus SS, Sharipov RN, Vertyshev AY, Vinogradova OL, Popov DV, Kolpakov FA. A Modular Mathematical Model of Exercise-Induced Changes in Metabolism, Signaling, and Gene Expression in Human Skeletal Muscle. Int J Mol Sci 2021; 22:10353. [PMID: 34638694 PMCID: PMC8508736 DOI: 10.3390/ijms221910353] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/04/2021] [Accepted: 09/22/2021] [Indexed: 11/29/2022] Open
Abstract
Skeletal muscle is the principal contributor to exercise-induced changes in human metabolism. Strikingly, although it has been demonstrated that a lot of metabolites accumulating in blood and human skeletal muscle during an exercise activate different signaling pathways and induce the expression of many genes in working muscle fibres, the systematic understanding of signaling-metabolic pathway interrelations with downstream genetic regulation in the skeletal muscle is still elusive. Herein, a physiologically based computational model of skeletal muscle comprising energy metabolism, Ca2+, and AMPK (AMP-dependent protein kinase) signaling pathways and the expression regulation of genes with early and delayed responses was developed based on a modular modeling approach and included 171 differential equations and more than 640 parameters. The integrated modular model validated on diverse including original experimental data and different exercise modes provides a comprehensive in silico platform in order to decipher and track cause-effect relationships between metabolic, signaling, and gene expression levels in skeletal muscle.
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Affiliation(s)
- Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya N. Kiselev
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | - Sergey S. Pintus
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | - Ruslan N. Sharipov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | | | - Olga L. Vinogradova
- Institute of Biomedical Problems of the Russian Academy of Sciences, 123007 Moscow, Russia;
| | - Daniil V. Popov
- Institute of Biomedical Problems of the Russian Academy of Sciences, 123007 Moscow, Russia;
| | - Fedor A. Kolpakov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
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Panchiwala H, Shah S, Planatscher H, Zakharchuk M, König M, Dräger A. The systems biology simulation core library. Bioinformatics 2021; 38:864-865. [PMID: 34554191 PMCID: PMC8756180 DOI: 10.1093/bioinformatics/btab669] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 08/08/2021] [Accepted: 09/20/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY Studying biological systems generally relies on computational modeling and simulation, e.g., model-driven discovery and hypothesis testing. Progress in standardization efforts led to the development of interrelated file formats to exchange and reuse models in systems biology, such as SBML, the Simulation Experiment Description Markup Language (SED-ML) or the Open Modeling EXchange format. Conducting simulation experiments based on these formats requires efficient and reusable implementations to make them accessible to the broader scientific community and to ensure the reproducibility of the results. The Systems Biology Simulation Core Library (SBSCL) provides interpreters and solvers for these standards as a versatile open-source API in JavaTM. The library simulates even complex bio-models and supports deterministic Ordinary Differential Equations; Stochastic Differential Equations; constraint-based analyses; recent SBML and SED-ML versions; exchange of results, and visualization of in silico experiments; open modeling exchange formats (COMBINE archives); hierarchically structured models; and compatibility with standard testing systems, including the Systems Biology Test Suite and published models from the BioModels and BiGG databases. AVAILABILITY AND IMPLEMENTATION SBSCL is freely available at https://draeger-lab.github.io/SBSCL/ and via Maven Central. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Mykola Zakharchuk
- Department of Computer Science, University of Tübingen, Tübingen 72076, Germany
| | - Matthias König
- Institute for Theoretical Biology, Humboldt University of Berlin, Berlin 10115, Germany
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28
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Pleiss J. Standardized Data, Scalable Documentation, Sustainable Storage – EnzymeML As A Basis For FAIR Data Management In Biocatalysis. ChemCatChem 2021. [DOI: 10.1002/cctc.202100822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry University of Stuttgart Allmandring 31 70569 Stuttgart Germany
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29
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Budde K, Smith J, Wilsdorf P, Haack F, Uhrmacher AM. Relating simulation studies by provenance-Developing a family of Wnt signaling models. PLoS Comput Biol 2021; 17:e1009227. [PMID: 34351901 PMCID: PMC8407594 DOI: 10.1371/journal.pcbi.1009227] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/31/2021] [Accepted: 06/29/2021] [Indexed: 12/28/2022] Open
Abstract
For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models. We revise a provenance ontology for simulation studies of cellular biochemical models. Provenance information is useful for understanding the creation of a simulation model because it not only contains information about the entities and activities that have led to a simulation model but also their relations, all of which can be visualized. It provides additional structure by explicitly recording research questions, assumptions, and requirements and relating them along with data, qualitative models, simulation models, and simulation experiments through a small set of predefined but extensible activities. We have applied our concept to a family of 19 Wnt signaling models and implemented a web-based tool (WebProv) to store the provenance information from these studies. The resulting provenance graph visualizes the story line of simulation studies and demonstrates the creation and calibration of simulation models, the successive attempts of validation and extension, and shows, beyond an individual simulation study, how the Wnt models are related. Thereby, the steps and sources that contributed to a simulation model are made explicit. Our approach complements other approaches aimed at facilitating the reuse and assessment of simulation products in systems biology such as model repositories as well as annotation and documentation guidelines.
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Affiliation(s)
- Kai Budde
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
- * E-mail:
| | - Jacob Smith
- Faculty of Computer Science, University of New Brunswick, Fredericton, Canada
| | - Pia Wilsdorf
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Fiete Haack
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Adelinde M. Uhrmacher
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
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30
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Shaikh B, Marupilla G, Wilson M, Blinov ML, Moraru II, Karr JR. RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats. Nucleic Acids Res 2021; 49:W597-W602. [PMID: 34019658 PMCID: PMC8262693 DOI: 10.1093/nar/gkab411] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/18/2021] [Accepted: 04/30/2021] [Indexed: 11/13/2022] Open
Abstract
Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed RunBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. RunBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable RunBioSimulations to execute nine frameworks and 44 algorithms, and they make RunBioSimulations extensible to additional frameworks and algorithms. RunBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that RunBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models.
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Affiliation(s)
- Bilal Shaikh
- Icahn Institute for Data Science & Genomic Technology and Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA
| | - Gnaneswara Marupilla
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Mike Wilson
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Michael L Blinov
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Ion I Moraru
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Jonathan R Karr
- Icahn Institute for Data Science & Genomic Technology and Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA
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31
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Carey MA, Dräger A, Beber ME, Papin JA, Yurkovich JT. Community standards to facilitate development and address challenges in metabolic modeling. Mol Syst Biol 2021; 16:e9235. [PMID: 32845080 PMCID: PMC8411906 DOI: 10.15252/msb.20199235] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remain challenging. As a result, the amount, quality, and format of the information contained within systems biology models are not consistent and therefore present challenges for widespread use and communication. Here, we focused on these standards, resources, and challenges in the field of constraint-based metabolic modeling by conducting a community-wide survey. We used this feedback to (i) outline the major challenges that our field faces and to propose solutions and (ii) identify a set of features that defines what a "gold standard" metabolic network reconstruction looks like concerning content, annotation, and simulation capabilities. We anticipate that this community-driven outline will help the long-term development of community-inspired resources as well as produce high-quality, accessible models within our field. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure the continued development of both practical, usable standards and reproducible, knowledge-rich models.
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Affiliation(s)
- Maureen A Carey
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Institute for Biomedical Informatics (IBMI), University of Tübingen, Tübingen, Germany.,Department of Computer Science, University of Tübingen, Tübingen, Germany.,German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Denmark
| | - Jason A Papin
- Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, VA, USA.,Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
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32
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Renz A, Dräger A. Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus. NPJ Syst Biol Appl 2021; 7:30. [PMID: 34188046 PMCID: PMC8241996 DOI: 10.1038/s41540-021-00188-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 05/25/2021] [Indexed: 12/19/2022] Open
Abstract
Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.
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Affiliation(s)
- Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Cluster of Excellence 'Controlling Microbes to Fight Infections', University of Tübingen, Tübingen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany.
- Department of Computer Science, University of Tübingen, Tübingen, Germany.
- Cluster of Excellence 'Controlling Microbes to Fight Infections', University of Tübingen, Tübingen, Germany.
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany.
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Welsh C, Nickerson DP, Rampadarath A, Neal ML, Sauro HM, Gennari JH. libOmexMeta: Enabling semantic annotation of models to support FAIR principles. Bioinformatics 2021; 37:4898-4900. [PMID: 34132740 PMCID: PMC8665746 DOI: 10.1093/bioinformatics/btab445] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/12/2021] [Accepted: 06/14/2021] [Indexed: 11/14/2022] Open
Abstract
SUMMARY As the number and complexity of biosimulation models grows, so do demands for tools that can help users better understand models and make those models more findable, shareable and reproducible. Consistent model annotation is a step toward these goals. Both models and tools are written in a variety of different languages; thus, the community has recognized the need for standard, language-independent methods for annotation. Based on the Computational Modeling in Biology Network (COMBINE) community consensus, we introduce an open-source, cross-platform software library for semantic annotation of models. AVAILABILITY libOmexMeta is freely available at https://github.com/sys-bio/libOmexMeta under the Apache License 2.0. A live demonstration is at github.com/sys-bio/pyomexmeta-binder-notebook. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ciaran Welsh
- Department of Bioengineering, University of Washington, Seattle, WA
| | | | | | - Maxwell L Neal
- Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA
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34
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Renz A, Widerspick L, Dräger A. Genome-Scale Metabolic Model of Infection with SARS-CoV-2 Mutants Confirms Guanylate Kinase as Robust Potential Antiviral Target. Genes (Basel) 2021; 12:796. [PMID: 34073716 PMCID: PMC8225150 DOI: 10.3390/genes12060796] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 12/17/2022] Open
Abstract
The current SARS-CoV-2 pandemic is still threatening humankind. Despite first successes in vaccine development and approval, no antiviral treatment is available for COVID-19 patients. The success is further tarnished by the emergence and spreading of mutation variants of SARS-CoV-2, for which some vaccines have lower efficacy. This highlights the urgent need for antiviral therapies even more. This article describes how the genome-scale metabolic model (GEM) of the host-virus interaction of human alveolar macrophages and SARS-CoV-2 was refined by incorporating the latest information about the virus's structural proteins and the mutant variants B.1.1.7, B.1.351, B.1.28, B.1.427/B.1.429, and B.1.617. We confirmed the initially identified guanylate kinase as a potential antiviral target with this refined model and identified further potential targets from the purine and pyrimidine metabolism. The model was further extended by incorporating the virus' lipid requirements. This opened new perspectives for potential antiviral targets in the altered lipid metabolism. Especially the phosphatidylcholine biosynthesis seems to play a pivotal role in viral replication. The guanylate kinase is even a robust target in all investigated mutation variants currently spreading worldwide. These new insights can guide laboratory experiments for the validation of identified potential antiviral targets. Only the combination of vaccines and antiviral therapies will effectively defeat this ongoing pandemic.
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Affiliation(s)
- Alina Renz
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany;
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
| | - Lina Widerspick
- Bernhard Nocht Institute for Tropical Medicine, Virus Immunology, 20359 Hamburg, Germany;
| | - Andreas Dräger
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany;
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, 72076 Tübingen, Germany
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35
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Porubsky V, Smith L, Sauro HM. Publishing reproducible dynamic kinetic models. Brief Bioinform 2021; 22:bbaa152. [PMID: 32793969 PMCID: PMC8138891 DOI: 10.1093/bib/bbaa152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 05/19/2020] [Accepted: 06/17/2020] [Indexed: 11/14/2022] Open
Abstract
Publishing repeatable and reproducible computational models is a crucial aspect of the scientific method in computational biology and one that is often forgotten in the rush to publish. The pressures of academic life and the lack of any reward system at institutions, granting agencies and journals means that publishing reproducible science is often either non-existent or, at best, presented in the form of an incomplete description. In the article, we will focus on repeatability and reproducibility in the systems biology field where a great many published models cannot be reproduced and in many cases even repeated. This review describes the current landscape of software tooling, model repositories, model standards and best practices for publishing repeatable and reproducible kinetic models. The review also discusses possible future remedies including working more closely with journals to help reviewers and editors ensure that published kinetic models are at minimum, repeatable. Contact: hsauro@uw.edu.
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Affiliation(s)
- Veronica Porubsky
- Department of Bioengineering, University of Washington, Seattle, 98105,USA
| | - Lucian Smith
- Department of Bioengineering, University of Washington, Seattle, 98105,USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, 98105,USA
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Renz A, Widerspick L, Dräger A. First Genome-Scale Metabolic Model of Dolosigranulum pigrum Confirms Multiple Auxotrophies. Metabolites 2021; 11:232. [PMID: 33918864 PMCID: PMC8069353 DOI: 10.3390/metabo11040232] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/21/2021] [Accepted: 04/06/2021] [Indexed: 12/11/2022] Open
Abstract
Dolosigranulum pigrum is a quite recently discovered Gram-positive coccus. It has gained increasing attention due to its negative correlation with Staphylococcus aureus, which is one of the most successful modern pathogens causing severe infections with tremendous morbidity and mortality due to its multiple resistances. As the possible mechanisms behind its inhibition of S. aureus remain unclear, a genome-scale metabolic model (GEM) is of enormous interest and high importance to better study its role in this fight. This article presents the first GEM of D. pigrum, which was curated using automated reconstruction tools and extensive manual curation steps to yield a high-quality GEM. It was evaluated and validated using all currently available experimental data of D. pigrum. With this model, already predicted auxotrophies and biosynthetic pathways could be verified. The model was used to define a minimal medium for further laboratory experiments and to predict various carbon sources' growth capacities. This model will pave the way to better understand D. pigrum's role in the fight against S. aureus.
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Affiliation(s)
- Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
| | - Lina Widerspick
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany; (A.R.); (L.W.)
- Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
- German Center for Infection Research (DZIF), Partner site Tübingen, 72076 Tübingen, Germany
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Yeoh JW, Swainston N, Vegh P, Zulkower V, Carbonell P, Holowko MB, Peddinti G, Poh CL. SynBiopython: an open-source software library for Synthetic Biology. Synth Biol (Oxf) 2021. [PMCID: PMC8063678 DOI: 10.1093/synbio/ysab001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Advances in hardware automation in synthetic biology laboratories are not yet fully matched by those of their software counterparts. Such automated laboratories, now commonly called biofoundries, require software solutions that would help with many specialized tasks such as batch DNA design, sample and data tracking, and data analysis, among others. Typically, many of the challenges facing biofoundries are shared, yet there is frequent wheel-reinvention where many labs develop similar software solutions in parallel. In this article, we present the first attempt at creating a standardized, open-source Python package. A number of tools will be integrated and developed that we envisage will become the obvious starting point for software development projects within biofoundries globally. Specifically, we describe the current state of available software, present usage scenarios and case studies for common problems, and finally describe plans for future development. SynBiopython is publicly available at the following address: http://synbiopython.org.
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Affiliation(s)
- Jing Wui Yeoh
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
| | - Neil Swainston
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Peter Vegh
- Edinburgh Genome Foundry, University of Edinburgh, Edinburgh, UK
| | | | - Pablo Carbonell
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Maciej B Holowko
- CSIRO Synthetic Biology Future Science Platform, Canberra, ACT, Australia
| | - Gopal Peddinti
- VTT Technical Research Center of Finland, Espoo, Finland
| | - Chueh Loo Poh
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore, Singapore
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38
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Minimum Information Required to Annotate Food Safety Risk Assessment Models (MIRARAM). Food Res Int 2021; 139:109952. [PMID: 33509505 DOI: 10.1016/j.foodres.2020.109952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 11/20/2020] [Accepted: 11/27/2020] [Indexed: 11/21/2022]
Abstract
In the last decades, mathematical models and model-based simulations became important elements not only in the area of risk assessment concerning microbiological and chemical hazards but also in modelling biological phenomena in general. Unfortunately, many of the developed models are published in non-standardized ways, which hinders efficient exchange, re-use and continuous improvement of models within the risk assessment domain. The establishment of guidelines for model annotation is an important pre-condition to overcome these obstacles. Additionally, implementation of annotation guidelines can improve transparency, quality control and even aid the clarification of intellectual property rights. Here, we address the question of "What is the minimum set of metadata that should be provided for a model in the risk assessment domain?". The proposed guideline focuses on food safety risk assessment models and is called "Minimum Information Required to Annotate food safety Risk Assessment Models (MIRARAM)". MIRARAM supports the model creator during the model documentation step and could also be used as a checklist by scientific journal editors or database curators. Software developers could take up MIRARAM and develop easy-to-use software tools or new features in existing programs that can help model creators to provide proposed model annotations in harmonized file formats. Based on experiences from similar guidelines in related scientific disciplines (like systems biology), it is expected that MIRARAM could contribute to the promotion of application and re-use of models as well as to implementing more standardized quality control in the food safety modelling domain.
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Schmiester L, Schälte Y, Bergmann FT, Camba T, Dudkin E, Egert J, Fröhlich F, Fuhrmann L, Hauber AL, Kemmer S, Lakrisenko P, Loos C, Merkt S, Müller W, Pathirana D, Raimúndez E, Refisch L, Rosenblatt M, Stapor PL, Städter P, Wang D, Wieland FG, Banga JR, Timmer J, Villaverde AF, Sahle S, Kreutz C, Hasenauer J, Weindl D. PEtab-Interoperable specification of parameter estimation problems in systems biology. PLoS Comput Biol 2021; 17:e1008646. [PMID: 33497393 PMCID: PMC7864467 DOI: 10.1371/journal.pcbi.1008646] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 02/05/2021] [Accepted: 12/18/2020] [Indexed: 01/24/2023] Open
Abstract
Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been-so far-no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.
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Affiliation(s)
- Leonard Schmiester
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | | | - Tacio Camba
- Department of Applied Mathematics II, University of Vigo, Vigo, Galicia, Spain
- BioProcess Engineering Group, IIM-CSIC, Vigo, Galicia, Spain
| | - Erika Dudkin
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Janine Egert
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
| | - Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lara Fuhrmann
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Adrian L. Hauber
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Svenja Kemmer
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Polina Lakrisenko
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Carolin Loos
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Simon Merkt
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Elba Raimúndez
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Lukas Refisch
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
| | - Marcus Rosenblatt
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Paul L. Stapor
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Philipp Städter
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Dantong Wang
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Franz-Georg Wieland
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Julio R. Banga
- BioProcess Engineering Group, IIM-CSIC, Vigo, Galicia, Spain
| | - Jens Timmer
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | | | - Sven Sahle
- BioQUANT/COS, Heidelberg University, Heidelberg, Germany
| | - Clemens Kreutz
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
- * E-mail:
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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40
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Musilova J, Sedlar K. Tools for time-course simulation in systems biology: a brief overview. Brief Bioinform 2021; 22:6076933. [PMID: 33423059 DOI: 10.1093/bib/bbaa392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 11/13/2022] Open
Abstract
Dynamic modeling of biological systems is essential for understanding all properties of a given organism as it allows us to look not only at the static picture of an organism but also at its behavior under various conditions. With the increasing amount of experimental data, the number of tools that enable dynamic analysis also grows. However, various tools are based on different approaches, use different types of data and offer different functions for analyses; so it can be difficult to choose the most suitable tool for a selected type of model. Here, we bring a brief overview containing descriptions of 50 tools for the reconstruction of biological models, their time-course simulation and dynamic analysis. We examined each tool using test data and divided them based on the qualitative and quantitative nature of the mathematical apparatus they use.
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Affiliation(s)
- Jana Musilova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Karel Sedlar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
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41
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42
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Rampadarath A, Nickerson DP. Automated Execution of Simulation Studies in Systems Medicine Using SED-ML and COMBINE Archive. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11688-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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43
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König M. Executable Simulation Model of the Liver. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11682-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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44
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Choi K, Karr JR, Sauro HM. Status and Challenges of Reproducibility in Computational Systems and Synthetic Biology. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11525-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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45
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Sarwar DM, Nickerson DP. CellML Model Discovery with the Physiome Model Repository. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11681-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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46
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Fontanarrosa P, Doosthosseini H, Borujeni AE, Dorfan Y, Voigt CA, Myers C. Genetic Circuit Dynamics: Hazard and Glitch Analysis. ACS Synth Biol 2020; 9:2324-2338. [PMID: 32786351 DOI: 10.1021/acssynbio.0c00055] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Multiple input changes can cause unwanted switching variations, or glitches, in the output of genetic combinational circuits. These glitches can have drastic effects if the output of the circuit causes irreversible changes within or with other cells such as a cascade of responses, apoptosis, or the release of a pharmaceutical in an off-target tissue. Therefore, avoiding unwanted variation of a circuit's output can be crucial for the safe operation of a genetic circuit. This paper investigates what causes unwanted switching variations in combinational genetic circuits using hazard analysis and a new dynamic model generator. The analysis is done in previously built and modeled genetic circuits with known glitching behavior. The dynamic models generated not only predict the same steady states as previous models but can also predict the unwanted switching variations that have been observed experimentally. Multiple input changes may cause glitches due to propagation delays within the circuit. Modifying the circuit's layout to alter these delays may change the likelihood of certain glitches, but it cannot eliminate the possibility that the glitch may occur. In other words, function hazards cannot be eliminated. Instead, they must be avoided by restricting the allowed input changes to the system. Logic hazards, on the other hand, can be avoided using hazard-free logic synthesis. This paper demonstrates this by showing how a circuit designed using a popular genetic design automation tool can be redesigned to eliminate logic hazards.
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Affiliation(s)
- Pedro Fontanarrosa
- Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Hamid Doosthosseini
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Amin Espah Borujeni
- Synthetic Biology Center and Department of Biological Engineering , Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Yuval Dorfan
- Synthetic Biology Center and Department of Biological Engineering , Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Christopher A. Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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47
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Porubsky VL, Goldberg AP, Rampadarath AK, Nickerson DP, Karr JR, Sauro HM. Best Practices for Making Reproducible Biochemical Models. Cell Syst 2020; 11:109-120. [PMID: 32853539 DOI: 10.1016/j.cels.2020.06.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 05/15/2020] [Accepted: 06/24/2020] [Indexed: 01/03/2023]
Abstract
Like many scientific disciplines, dynamical biochemical modeling is hindered by irreproducible results. This limits the utility of biochemical models by making them difficult to understand, trust, or reuse. We comprehensively list the best practices that biochemical modelers should follow to build reproducible biochemical model artifacts-all data, model descriptions, and custom software used by the model-that can be understood and reused. The best practices provide advice for all steps of a typical biochemical modeling workflow in which a modeler collects data; constructs, trains, simulates, and validates the model; uses the predictions of a model to advance knowledge; and publicly shares the model artifacts. The best practices emphasize the benefits obtained by using standard tools and formats and provides guidance to modelers who do not or cannot use standards in some stages of their modeling workflow. Adoption of these best practices will enhance the ability of researchers to reproduce, understand, and reuse biochemical models.
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Affiliation(s)
- Veronica L Porubsky
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA.
| | - Arthur P Goldberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Anand K Rampadarath
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - David P Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Jonathan R Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
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48
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Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik‐Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier‐Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020; 16:e9110. [PMID: 32845085 PMCID: PMC8411907 DOI: 10.15252/msb.20199110] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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49
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Brunak S, Bjerre Collin C, Eva Ó Cathaoir K, Golebiewski M, Kirschner M, Kockum I, Moser H, Waltemath D. Towards standardization guidelines for in silico approaches in personalized medicine. J Integr Bioinform 2020; 17:jib-2020-0006. [PMID: 32827396 PMCID: PMC7756614 DOI: 10.1515/jib-2020-0006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 04/26/2020] [Indexed: 01/11/2023] Open
Abstract
Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.
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Affiliation(s)
| | | | | | | | - Marc Kirschner
- University of Copenhagen, Copenhagen, Denmark
- Forschungszentrum Jülich GmbH, Project Management Jülich, Jülich, Germany
| | | | - Heike Moser
- Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Dagmar Waltemath
- Medical Informatics Laboratory, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
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50
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Hoksza D, Gawron P, Ostaszewski M, Hasenauer J, Schneider R. Closing the gap between formats for storing layout information in systems biology. Brief Bioinform 2020; 21:1249-1260. [PMID: 31273380 PMCID: PMC7373180 DOI: 10.1093/bib/bbz067] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 04/23/2019] [Accepted: 05/14/2019] [Indexed: 11/13/2022] Open
Abstract
The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.
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Affiliation(s)
- David Hoksza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
- Faculty of Mathematics and Physics, Charles University, Malostranské nám. 25, 118 00 Prague, Czech Republic
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, München, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, avenue du Swing L-4367 Belvaux, Luxembourg
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