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Xu J, Wiley HS, Sauro HM. Generating synthetic signaling networks for in silico modeling studies. J Theor Biol 2024; 593:111901. [PMID: 39004118 PMCID: PMC11309880 DOI: 10.1016/j.jtbi.2024.111901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 06/07/2024] [Accepted: 07/08/2024] [Indexed: 07/16/2024]
Abstract
Predictive models of signaling pathways have proven to be difficult to develop. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. To meet this need, we developed a method for generating artificial signaling networks that were reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. We defined the reaction degree and reaction distance to measure the topology of reaction networks, especially to consider enzymes. To determine whether our generated signaling networks displayed meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModels signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModels and thus could be useful for building network evaluation tools.
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Affiliation(s)
- Jin Xu
- Department of Bioengineering, University of Washington, Seattle 98195, WA, USA.
| | - H Steven Wiley
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland 99352, WA, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle 98195, WA, USA
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2
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Holtzapple E, Zhou G, Luo H, Tang D, Arazkhani N, Hansen C, Telmer CA, Miskov-Zivanov N. The BioRECIPE Knowledge Representation Format. ACS Synth Biol 2024; 13:2621-2624. [PMID: 39051984 PMCID: PMC11334182 DOI: 10.1021/acssynbio.4c00096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/22/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024]
Abstract
The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to standardize and facilitate human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems and synthetic biology communities.
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Affiliation(s)
- Emilee Holtzapple
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Gaoxiang Zhou
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Haomiao Luo
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Difei Tang
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Niloofar Arazkhani
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Casey Hansen
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Cheryl A. Telmer
- Department
of Biological Sciences, Carnegie Mellon
University, Pittsburgh, Pennsylvania 15213, United States
| | - Natasa Miskov-Zivanov
- Electrical
and Computer Engineering Department, Computational and Systems Biology
Department, Bioengineering Department, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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3
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Agmon E. Prelude to a Compositional Systems Biology. ARXIV 2024:arXiv:2408.00942v1. [PMID: 39130201 PMCID: PMC11312625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Composition is a powerful principle for systems biology, focused on the interfaces, interconnections, and orchestration of distributed processes. Whereas most systems biology models focus on the structure or dynamics of specific subsystems in controlled conditions, compositional systems biology aims to connect such models into integrative multiscale simulations. This emphasizes the space between models-a compositional perspective asks what variables should be exposed through a submodel's interface? How do coupled models connect and translate across scales? How can we connect domain-specific models across biological and physical research areas to drive the synthesis of new knowledge? What is required of software that integrates diverse datasets and submodels into unified multiscale simulations? How can the resulting integrative models be accessed, flexibly recombined into new forms, and iteratively refined by a community of researchers? This essay offers a high-level overview of the key components for compositional systems biology, including: 1) a conceptual framework and corresponding graphical framework to represent interfaces, composition patterns, and orchestration patterns; 2) standardized composition schemas that offer consistent formats for composable data types and models, fostering robust infrastructure for a registry of simulation modules that can be flexibly assembled; 3) a foundational set of biological templates-schemas for cellular and molecular interfaces, which can be filled with detailed submodels and datasets, and are designed to integrate knowledge that sheds light on the molecular emergence of cells; and 4) scientific collaboration facilitated by user-friendly interfaces for connecting researchers with datasets and models, and which allows a community of researchers to effectively build integrative multiscale models of cellular systems.
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Affiliation(s)
- Eran Agmon
- Center for Cell Analysis and Modeling, Department of Molecular Biology and Biophysics, University of Connecticut Health, Farmington, Connecticut, USA
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4
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Priego Espinosa D, Espinal-Enríquez J, Aldana A, Aldana M, Martínez-Mekler G, Carneiro J, Darszon A. Reviewing mathematical models of sperm signaling networks. Mol Reprod Dev 2024; 91:e23766. [PMID: 39175359 DOI: 10.1002/mrd.23766] [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: 05/17/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Dave Garbers' work significantly contributed to our understanding of sperm's regulated motility, capacitation, and the acrosome reaction. These key sperm functions involve complex multistep signaling pathways engaging numerous finely orchestrated elements. Despite significant progress, many parameters and interactions among these elements remain elusive. Mathematical modeling emerges as a potent tool to study sperm physiology, providing a framework to integrate experimental results and capture functional dynamics considering biochemical, biophysical, and cellular elements. Depending on research objectives, different modeling strategies, broadly categorized into continuous and discrete approaches, reveal valuable insights into cell function. These models allow the exploration of hypotheses regarding molecules, conditions, and pathways, whenever they become challenging to evaluate experimentally. This review presents an overview of current theoretical and experimental efforts to understand sperm motility regulation, capacitation, and the acrosome reaction. We discuss the strengths and weaknesses of different modeling strategies and highlight key findings and unresolved questions. Notable discoveries include the importance of specific ion channels, the role of intracellular molecular heterogeneity in capacitation and the acrosome reaction, and the impact of pH changes on acrosomal exocytosis. Ultimately, this review underscores the crucial importance of mathematical frameworks in advancing our understanding of sperm physiology and guiding future experimental investigations.
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Affiliation(s)
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
| | - Andrés Aldana
- Network Science Institute, Northeastern University, Boston, Massachusetts, USA
| | - Maximino Aldana
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, México
- Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Gustavo Martínez-Mekler
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, México
- Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Jorge Carneiro
- Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Alberto Darszon
- Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, México
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5
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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6
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Bleker C, Ramšak Ž, Bittner A, Podpečan V, Zagorščak M, Wurzinger B, Baebler Š, Petek M, Križnik M, van Dieren A, Gruber J, Afjehi-Sadat L, Weckwerth W, Županič A, Teige M, Vothknecht UC, Gruden K. Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses. PLANT COMMUNICATIONS 2024; 5:100920. [PMID: 38616489 PMCID: PMC11211517 DOI: 10.1016/j.xplc.2024.100920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.
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Affiliation(s)
- Carissa Bleker
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.
| | - Živa Ramšak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Andras Bittner
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Vid Podpečan
- Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia
| | - Maja Zagorščak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Bernhard Wurzinger
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Špela Baebler
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Marko Petek
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Maja Križnik
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Annelotte van Dieren
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Juliane Gruber
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Leila Afjehi-Sadat
- Mass Spectrometry Unit, Core Facility Shared Services, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Wolfram Weckwerth
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Anže Županič
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia
| | - Markus Teige
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria
| | - Ute C Vothknecht
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, 53115 Bonn, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.
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7
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Herajy M, Liu F, Heiner M. Design patterns for the construction of computational biological models. Brief Bioinform 2024; 25:bbae318. [PMID: 38961813 PMCID: PMC11222664 DOI: 10.1093/bib/bbae318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/16/2024] [Accepted: 06/17/2024] [Indexed: 07/05/2024] Open
Abstract
Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.
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Affiliation(s)
- Mostafa Herajy
- Department of Mathematics and Computer Science, Faculty of Science, Port Said University, 42521 Port Said, Egypt
| | - Fei Liu
- School of Software Engineering, South China University of Technology, 510006 Guangzhou, China
| | - Monika Heiner
- Computer Science Institute, Brandenburg University of Technology, 03013 Cottbus, Germany
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8
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Fonseca LL, Böttcher L, Mehrad B, Laubenbacher RC. Surrogate modeling and control of medical digital twins. ARXIV 2024:arXiv:2402.05750v2. [PMID: 38827450 PMCID: PMC11142319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.
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Affiliation(s)
- Luis L. Fonseca
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Lucas Böttcher
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
- Department of Computational Science and Philosophy, Frankfurt School of Finance and Management, 60322 Frankfurt am Main, Germany
| | - Borna Mehrad
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Reinhard C. Laubenbacher
- Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, FL, USA
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Höpfl S, Albadry M, Dahmen U, Herrmann KH, Kindler EM, König M, Reichenbach JR, Tautenhahn HM, Wei W, Zhao WT, Radde NE. Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data. Bioinformatics 2024; 40:btae312. [PMID: 38741151 PMCID: PMC11128094 DOI: 10.1093/bioinformatics/btae312] [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: 01/29/2024] [Revised: 04/11/2024] [Accepted: 05/13/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties. RESULTS We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. AVAILABILITY AND IMPLEMENTATION The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.
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Affiliation(s)
- Sebastian Höpfl
- Institute for Stochastics and Applications, University of Stuttgart, 70569 Stuttgart, Germany
| | - Mohamed Albadry
- Experimental Transplantation Surgery, Department of General, Vascular and Visceral Surgery, University Hospital Jena, 07745 Jena, Germany
- Department of Pathology, Faculty of Veterinary Medicine, Menoufia University, Shebin Elkom, Menoufia, Egypt
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Vascular and Visceral Surgery, University Hospital Jena, 07745 Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, 07743 Jena, Germany
| | - Eva Marie Kindler
- Clinic for General, Visceral and Vascular Surgery, Jena University Hospital, 07747 Jena, Germany
| | - Matthias König
- Institute for Biology, Faculty of Life Sciences, Humboldt-University Berlin, 10115 Berlin, Germany
| | - Jürgen Rainer Reichenbach
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, 07743 Jena, Germany
| | - Hans-Michael Tautenhahn
- Clinic for Visceral, Transplantation, Thoracic and Vascular Surgery, Leipzig University Hospital, 04103 Leipzig, Germany
| | - Weiwei Wei
- Experimental Transplantation Surgery, Department of General, Vascular and Visceral Surgery, University Hospital Jena, 07745 Jena, Germany
| | - Wan-Ting Zhao
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, 07743 Jena, Germany
| | - Nicole Erika Radde
- Institute for Stochastics and Applications, University of Stuttgart, 70569 Stuttgart, Germany
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10
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Giannantoni L, Bardini R, Savino A, Di Carlo S. Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems. BMC Bioinformatics 2024; 25:166. [PMID: 38664639 PMCID: PMC11046772 DOI: 10.1186/s12859-024-05782-x] [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: 01/11/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. RESULTS BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. CONCLUSIONS BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.
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Affiliation(s)
- Leonardo Giannantoni
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
| | - Roberta Bardini
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy.
| | - Alessandro Savino
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
| | - Stefano Di Carlo
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
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11
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Predl M, Mießkes M, Rattei T, Zanghellini J. PyCoMo: a python package for community metabolic model creation and analysis. Bioinformatics 2024; 40:btae153. [PMID: 38532295 PMCID: PMC10990682 DOI: 10.1093/bioinformatics/btae153] [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: 10/09/2023] [Revised: 12/29/2023] [Accepted: 03/25/2024] [Indexed: 03/28/2024] Open
Abstract
SUMMARY PyCoMo is a python package for quick and easy generation of genome-scale compartmentalized community metabolic models that are compliant with current openCOBRA file formats. The resulting models can be used to predict (i) the maximum growth rate at a given abundance profile, (ii) the feasible community compositions at a given growth rate, and (iii) all exchange metabolites and cross-feeding interactions in a community metabolic model independent of the abundance profile; we demonstrate PyCoMo's capability by analysing methane production in a previously published simplified biogas community metabolic model. AVAILABILITY AND IMPLEMENTATION PyCoMo is freely available under an MIT licence at http://github.com/univieCUBE/PyCoMo, the Python Package Index, and Zenodo.
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Affiliation(s)
- Michael Predl
- Department of Microbiology and Ecosystem Science, Division of Computational Systems Biology, Centre for Microbiology and Environmental Systems Science, University of Vienna, 1030 Vienna, Austria
- Doctoral School in Microbiology and Environmental Science, University of Vienna, 1030 Vienna, Austria
| | - Marianne Mießkes
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
- Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
| | - Thomas Rattei
- Department of Microbiology and Ecosystem Science, Division of Computational Systems Biology, Centre for Microbiology and Environmental Systems Science, University of Vienna, 1030 Vienna, Austria
- Doctoral School in Microbiology and Environmental Science, University of Vienna, 1030 Vienna, Austria
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
- Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria
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12
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Matzko RO, Konur S. BioNexusSentinel: a visual tool for bioregulatory network and cytohistological RNA-seq genetic expression profiling within the context of multicellular simulation research using ChatGPT-augmented software engineering. BIOINFORMATICS ADVANCES 2024; 4:vbae046. [PMID: 38571784 PMCID: PMC10990683 DOI: 10.1093/bioadv/vbae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/22/2024] [Accepted: 03/18/2024] [Indexed: 04/05/2024]
Abstract
Summary Motivated by the need to parameterize ongoing multicellular simulation research, this paper documents the culmination of a ChatGPT augmented software engineering cycle resulting in an integrated visual platform for efficient cytohistological RNA-seq and bioregulatory network exploration. As contrasted to other systems and synthetic biology tools, BioNexusSentinel was developed de novo to uniquely combine these features. Reactome served as the primary source of remotely accessible biological models, accessible using BioNexusSentinel's novel search engine and REST API requests. The innovative, feature-rich gene expression profiler component was developed to enhance the exploratory experience for the researcher, culminating in the cytohistological RNA-seq explorer based on Human Protein Atlas data. A novel cytohistological classifier would be integrated via pre-processed analysis of the RNA-seq data via R statistical language, providing for useful analytical functionality and good performance for the end-user. Implications of the work span prospects for model orthogonality evaluations, gap identification in network modelling, prototyped automatic kinetics parameterization, and downstream simulation and cellular biological state analysis. This unique computational biology software engineering collaboration with generative natural language processing artificial intelligence was shown to enhance worker productivity, with evident benefits in terms of accelerating coding and machine-human intelligence transfer. Availability and implementation BioNexusSentinel project releases, with corresponding data and installation instructions, are available at https://github.com/RichardMatzko/BioNexusSentinel.
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Affiliation(s)
- Richard Oliver Matzko
- School of Computer Science, AI and Electronics, University of Bradford, Bradford BD7 1HR, United Kingdom
| | - Savas Konur
- School of Computer Science, AI and Electronics, University of Bradford, Bradford BD7 1HR, United Kingdom
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Aghakhani S, Niarakis A, Soliman S. MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps. J Integr Bioinform 2024; 21:jib-2023-0048. [PMID: 38314776 PMCID: PMC11293895 DOI: 10.1515/jib-2023-0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/09/2024] [Indexed: 02/07/2024] Open
Abstract
Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel - European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay, Palaiseau, France
| | - Anna Niarakis
- GenHotel - European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay, Palaiseau, France
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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 PMCID: PMC11293897 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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15
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Hu M, Suthers PF, Maranas CD. KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states. Metab Eng 2024; 82:123-133. [PMID: 38336004 DOI: 10.1016/j.ymben.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/24/2024] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. This versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.
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Affiliation(s)
- Mengqi Hu
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
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Niarakis A, Ostaszewski M, Mazein A, Kuperstein I, Kutmon M, Gillespie ME, Funahashi A, Acencio ML, Hemedan A, Aichem M, Klein K, Czauderna T, Burtscher F, Yamada TG, Hiki Y, Hiroi NF, Hu F, Pham N, Ehrhart F, Willighagen EL, Valdeolivas A, Dugourd A, Messina F, Esteban-Medina M, Peña-Chilet M, Rian K, Soliman S, Aghamiri SS, Puniya BL, Naldi A, Helikar T, Singh V, Fernández MF, Bermudez V, Tsirvouli E, Montagud A, Noël V, Ponce-de-Leon M, Maier D, Bauch A, Gyori BM, Bachman JA, Luna A, Piñero J, Furlong LI, Balaur I, Rougny A, Jarosz Y, Overall RW, Phair R, Perfetto L, Matthews L, Rex DAB, Orlic-Milacic M, Gomez LCM, De Meulder B, Ravel JM, Jassal B, Satagopam V, Wu G, Golebiewski M, Gawron P, Calzone L, Beckmann JS, Evelo CT, D’Eustachio P, Schreiber F, Saez-Rodriguez J, Dopazo J, Kuiper M, Valencia A, Wolkenhauer O, Kitano H, Barillot E, Auffray C, Balling R, Schneider R. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. Front Immunol 2024; 14:1282859. [PMID: 38414974 PMCID: PMC10897000 DOI: 10.3389/fimmu.2023.1282859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/22/2023] [Indexed: 02/29/2024] Open
Abstract
Introduction The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.
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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, Palaiseau, France
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, Netherlands
| | - Marc E. Gillespie
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- St. John’s University, Queens, NY, United States
| | - Akira Funahashi
- Department of Biosciences and Informatics, Keio University, Kanagawa, Japan
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ahmed Hemedan
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Michael Aichem
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Karsten Klein
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Tobias Czauderna
- Faculty of Applied Computer Sciences & Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
| | - Felicia Burtscher
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Takahiro G. Yamada
- Department of Biosciences and Informatics, Keio University, Kanagawa, Japan
| | - Yusuke Hiki
- Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, Kanagawa, Japan
| | - Noriko F. Hiroi
- Faculty of Creative Engineering, Kanagawa Institute of Technology, Kanagawa, Japan
- Keio University School of Medicine, Tokyo, Japan
| | - Finterly Hu
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Nhung Pham
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Egon L. Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Alberto Valdeolivas
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Aurelien Dugourd
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Francesco Messina
- Department of Epidemiology, Preclinical Research and Advanced Diagnostic, National Institute for Infectious Diseases’ Lazzaro Spallanzani’ - IRCCS, Rome, Italy
| | - Marina Esteban-Medina
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Sevilla, Spain
| | - Maria Peña-Chilet
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, Seville, Spain
| | - Kinza Rian
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, Palaiseau, France
| | - Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Aurélien Naldi
- Lifeware Group, Inria, Saclay-île de France, Palaiseau, France
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Vidisha Singh
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France
| | | | - Viviam Bermudez
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Eirini Tsirvouli
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Arnau Montagud
- Barcelona Supercomputing Center (BSC.), Barcelona, Spain
| | - Vincent Noël
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | | | | | | | - Benjamin M. Gyori
- Harvard Medical School, Laboratory of Systems Pharmacology, Boston, MA, United States
| | - John A. Bachman
- Harvard Medical School, Laboratory of Systems Pharmacology, Boston, MA, United States
| | - Augustin Luna
- Computational Biology Branch, National Library of Medicine, Bethesda, MD, United States
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Janet Piñero
- Medbioinformatics Solutions SL, Barcelona, Spain
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dept. of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Laura I. Furlong
- Medbioinformatics Solutions SL, Barcelona, Spain
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dept. of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Adrien Rougny
- Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan
- Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan
| | - Yohan Jarosz
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rupert W. Overall
- Institute for Biology, Humboldt University of Berlin, Berlin, Germany
| | - Robert Phair
- Integrative Bioinformatics, Inc., Mountain View, CA, United States
| | - Livia Perfetto
- Department of Biology and Biotechnology Charles Darwin, Sapienza University of Rome, Rome, Italy
| | - Lisa Matthews
- Department of Biochemistry & Molecular Pharmacology, NYU. Langone Medical Center, New York, NY, United States
| | | | | | - Luis Cristobal Monraz Gomez
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | | | - Jean Marie Ravel
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Bijay Jassal
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt am Main, Germany
| | - Guanming Wu
- Oregon Health Sciences University, Portland, OR, United States
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Laurence Calzone
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | | | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Peter D’Eustachio
- Department of Biochemistry & Molecular Pharmacology, NYU. Langone Medical Center, New York, NY, United States
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Victoria, VIC, Australia
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Joaquin Dopazo
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, Seville, Spain
- FPS/ELIXIR-es, Hospital Virgen del Rocío, Sevilla, Spain
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC.), Barcelona, Spain
- I.C.R.E.A., Pg. Lluís Companys 23, Barcelona, Spain
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
- Leibniz Institute for Food Systems Biology, at the Technical University Munich, Munich, Germany
| | | | - Emmanuel Barillot
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | | | - Rudi Balling
- Institute of Molecular Psychiatry, University of Bonn, Bonn, Germany
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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Jacopin E, Sakamoto Y, Nishida K, Kaizu K, Takahashi K. An architecture for collaboration in systems biology at the age of the Metaverse. NPJ Syst Biol Appl 2024; 10:12. [PMID: 38280851 PMCID: PMC10821884 DOI: 10.1038/s41540-024-00334-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 01/10/2024] [Indexed: 01/29/2024] Open
Abstract
As the current state of the Metaverse is largely driven by corporate interests, which may not align with scientific goals and values, academia should play a more active role in its development. Here, we present the challenges and solutions for building a Metaverse that supports systems biology research and collaboration. Our solution consists of two components: Kosmogora, a server ensuring biological data access, traceability, and integrity in the context of a highly collaborative environment such as a metaverse; and ECellDive, a virtual reality application to explore, interact, and build upon the data managed by Kosmogora. We illustrate the synergy between the two components by visualizing a metabolic network and its flux balance analysis. We also argue that the Metaverse of systems biology will foster closer communication and cooperation between experimentalists and modelers in the field.
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Affiliation(s)
- Eliott Jacopin
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan.
| | - Yuki Sakamoto
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
| | - Kozo Nishida
- RIKEN, Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- Tokyo University of Agriculture and Technology, Department of Biotechnology and Life Science, 2-24-16 Nakamachi, Koganei, Tokyo, 184-8588, Japan
| | - Kazunari Kaizu
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
| | - Koichi Takahashi
- RIKEN, Center for Biosystems Dynamics Research, 6-2-3 Furuedai, Suita, Osaka, 565-0874, Japan
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18
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Tummler K, Klipp E. Data integration strategies for whole-cell modeling. FEMS Yeast Res 2024; 24:foae011. [PMID: 38544322 PMCID: PMC11042497 DOI: 10.1093/femsyr/foae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 03/15/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.
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Affiliation(s)
- Katja Tummler
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
| | - Edda Klipp
- Humboldt-Universität zu Berlin, Faculty of Life Sciences, Institute of Biology, Theoretical Biophysics,, Invalidenstr. 42, 10115 Berlin, Germany
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19
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Sego TJ. SimService: a lightweight library for building simulation services in Python. Bioinformatics 2024; 40:btae009. [PMID: 38237907 PMCID: PMC10809901 DOI: 10.1093/bioinformatics/btae009] [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: 09/19/2023] [Revised: 11/27/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
SUMMARY Integrative biological modeling requires software infrastructure to launch, interconnect, and execute simulation software components without loss of functionality. SimService is a software library that enables deploying simulations in integrated applications as memory-isolated services with interactive proxy objects in the Python programming language. SimService supports customizing the interface of proxies so that simulation developers and users alike can tailor generated simulation instances according to model, method, and integrated application. AVAILABILITY AND IMPLEMENTATION SimService is written in Python, is freely available on GitHub under the MIT license at https://github.com/tjsego/simservice, and is available for download via the Python Package Index (package name "simservice") and conda (package name "simservice" on the conda-forge channel).
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Affiliation(s)
- T J Sego
- Department of Medicine, University of Florida, Gainesville, FL 32610-0225, United States
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20
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Carrasco Muriel J, Long C, Sonnenschein N. Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO. Microbiol Spectr 2023; 11:e0170523. [PMID: 37931133 PMCID: PMC10783817 DOI: 10.1128/spectrum.01705-23] [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: 04/26/2023] [Accepted: 09/11/2023] [Indexed: 11/08/2023] Open
Abstract
IMPORTANCE The metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.
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Affiliation(s)
- Jorge Carrasco Muriel
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Nikolaus Sonnenschein
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark
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21
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Rahul R, Stinchcombe AR, Joseph JW, Ingalls B. Kinetic modelling of β-cell metabolism reveals control points in the insulin-regulating pyruvate cycling pathways. IET Syst Biol 2023; 17:303-315. [PMID: 37938890 PMCID: PMC10725709 DOI: 10.1049/syb2.12077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 11/10/2023] Open
Abstract
Insulin, a key hormone in the regulation of glucose homoeostasis, is secreted by pancreatic β-cells in response to elevated glucose levels. Insulin is released in a biphasic manner in response to glucose metabolism in β-cells. The first phase of insulin secretion is triggered by an increase in the ATP:ADP ratio; the second phase occurs in response to both a rise in ATP:ADP and other key metabolic signals, including a rise in the NADPH:NADP+ ratio. Experimental evidence indicates that pyruvate-cycling pathways play an important role in the elevation of the NADPH:NADP+ ratio in response to glucose. The authors developed a kinetic model for the tricarboxylic acid cycle and pyruvate cycling pathways. The authors successfully validated the model against experimental observations and performed a sensitivity analysis to identify key regulatory interactions in the system. The model predicts that the dicarboxylate carrier and the pyruvate transporter are the most important regulators of pyruvate cycling and NADPH production. In contrast, the analysis showed that variation in the pyruvate carboxylase flux was compensated by a response in the activity of mitochondrial isocitrate dehydrogenase (ICDm ) resulting in minimal effect on overall pyruvate cycling flux. The model predictions suggest starting points for further experimental investigation, as well as potential drug targets for the treatment of type 2 diabetes.
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Affiliation(s)
- Rahul Rahul
- Department of Applied MathematicsUniversity of WaterlooWaterlooOntarioCanada
| | | | - Jamie W. Joseph
- School of PharmacyUniversity of WaterlooWaterlooOntarioCanada
| | - Brian Ingalls
- Department of Applied MathematicsUniversity of WaterlooWaterlooOntarioCanada
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22
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Mehta K, Ljungquist B, Ogden J, Nanda S, Ascoli RG, Ng L, Ascoli GA. Online conversion of reconstructed neural morphologies into standardized SWC format. Nat Commun 2023; 14:7429. [PMID: 37973857 PMCID: PMC10654402 DOI: 10.1038/s41467-023-42931-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Digital reconstructions provide an accurate and reliable way to store, share, model, quantify, and analyze neural morphology. Continuous advances in cellular labeling, tissue processing, microscopic imaging, and automated tracing catalyzed a proliferation of software applications to reconstruct neural morphology. These computer programs typically encode the data in custom file formats. The resulting format heterogeneity severely hampers the interoperability and reusability of these valuable data. Among these many alternatives, the SWC file format has emerged as a popular community choice, coalescing a rich ecosystem of related neuroinformatics resources for tracing, visualization, analysis, and simulation. This report presents a standardized specification of the SWC file format. In addition, we introduce xyz2swc, a free online service that converts all 26 reconstruction formats (and 72 variations) described in the scientific literature into the SWC standard. The xyz2swc service is available open source through a user-friendly browser interface ( https://neuromorpho.org/xyz2swc/ui/ ) and an Application Programming Interface (API).
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Affiliation(s)
- Ketan Mehta
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Bengt Ljungquist
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - James Ogden
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Ruben G Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures & Plasticity, George Mason University, Fairfax, VA, USA.
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23
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Velleuer E, Domínguez-Hüttinger E, Rodríguez A, Harris LA, Carlberg C. Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia. Front Genet 2023; 14:1254966. [PMID: 38028610 PMCID: PMC10652399 DOI: 10.3389/fgene.2023.1254966] [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: 07/07/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.
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Affiliation(s)
- Eunike Velleuer
- Department of Cytopathology, Heinrich Heine University, Düsseldorf, Germany
- Center for Child and Adolescent Health, Helios Klinikum, Krefeld, Germany
| | - Elisa Domínguez-Hüttinger
- Departamento Düsseldorf Biología Molecular y Biotecnología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
| | - Alfredo Rodríguez
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad México, Mexico
- Instituto Nacional de Pediatría, Ciudad México, Mexico
| | - Leonard A. Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, United States
- Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, AR, United States
- Cancer Biology Program, Winthrop P Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Carsten Carlberg
- Institute of Animal Reproduction and Food Research, Polish Academy of Sciences, Olsztyn, Poland
- School of Medicine, Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
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24
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Scheel J, Hoch M, Wolfien M, Gupta S. NaviCenta - The disease map for placental research. Placenta 2023; 143:12-15. [PMID: 37793322 DOI: 10.1016/j.placenta.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023]
Abstract
The placenta remains the key organ to pregnancy complications, such as preeclampsia, contrarily the pathophysiology underlying the placental dysfunctions remains elusive. Here, we present our Disease Map "NaviCenta", which is an online resource based on the interactions between tissues, cellular compartments, and molecules that mediate disease-related processes in the placenta. We built cellular and molecular interaction networks based upon manual curation and annotation of publicly available information in the scientific literature, pathways resources, and Omics data. NaviCenta (Navigate the plaCenta) serves as an open access, spatio-temporal, multi-scale knowledge base, and analytical tool for enhanced interpretation and hypothesis testing on various placental disease phenotypes.
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Affiliation(s)
- Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
| | - Matti Hoch
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
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25
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Bäuerle F, Döbel GO, Camus L, Heilbronner S, Dräger A. Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum. FRONTIERS IN BIOINFORMATICS 2023; 3:1214074. [PMID: 37936955 PMCID: PMC10626998 DOI: 10.3389/fbinf.2023.1214074] [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: 04/28/2023] [Accepted: 10/02/2023] [Indexed: 11/09/2023] Open
Abstract
Introduction: Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in in vitro settings is hardly investigated. Methods: The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Results: We introduce five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and were validated by laboratory experiments. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Discussion: Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.
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Affiliation(s)
- Famke Bäuerle
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Gwendolyn O. Döbel
- 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
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
| | - Laura Camus
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Simon Heilbronner
- Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
- Faculty of Biology, Microbiology, Ludwig Maximilian University of Munich, Munich, Germany
| | - 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
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany
- Cluster of Excellence “Controlling Microbes to Fight Infections (CMFI)”, Eberhard Karl University of Tübingen, Tübingen, Germany
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26
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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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27
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Vezina B, Watts SC, Hawkey J, Cooper HB, Judd LM, Jenney AWJ, Monk JM, Holt KE, Wyres KL. Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models. eLife 2023; 12:RP87406. [PMID: 37815531 PMCID: PMC10564454 DOI: 10.7554/elife.87406] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023] Open
Abstract
Metabolic capacity can vary substantially within a bacterial species, leading to ecological niche separation, as well as differences in virulence and antimicrobial susceptibility. Genome-scale metabolic models are useful tools for studying the metabolic potential of individuals, and with the rapid expansion of genomic sequencing there is a wealth of data that can be leveraged for comparative analysis. However, there exist few tools to construct strain-specific metabolic models at scale. Here, we describe Bactabolize, a reference-based tool which rapidly produces strain-specific metabolic models and growth phenotype predictions. We describe a pan reference model for the priority antimicrobial-resistant pathogen, Klebsiella pneumoniae, and a quality control framework for using draft genome assemblies as input for Bactabolize. The Bactabolize-derived model for K. pneumoniae reference strain KPPR1 performed comparatively or better than currently available automated approaches CarveMe and gapseq across 507 substrate and 2317 knockout mutant growth predictions. Novel draft genomes passing our systematically defined quality control criteria resulted in models with a high degree of completeness (≥99% genes and reactions captured compared to models derived from matched complete genomes) and high accuracy (mean 0.97, n=10). We anticipate the tools and framework described herein will facilitate large-scale metabolic modelling analyses that broaden our understanding of diversity within bacterial species and inform novel control strategies for priority pathogens.
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Affiliation(s)
- Ben Vezina
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Stephen C Watts
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Jane Hawkey
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Helena B Cooper
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | - Louise M Judd
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
| | | | - Jonathan M Monk
- Department of Bioengineering, University of California, San DiegoSan DiegoUnited States
| | - Kathryn E Holt
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
- Department of Infection Biology, London School of Hygiene & Tropical MedicineLondonUnited Kingdom
| | - Kelly L Wyres
- Department of Infectious Diseases, Central Clinical School, Monash UniversityMelbourneAustralia
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28
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Barry CJ, Pillay CS, Rohwer JM. Modelling the Decamerisation Cycle of PRDX1 and the Inhibition-like Effect on Its Peroxidase Activity. Antioxidants (Basel) 2023; 12:1707. [PMID: 37760010 PMCID: PMC10525498 DOI: 10.3390/antiox12091707] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 09/29/2023] Open
Abstract
Peroxiredoxins play central roles in the detoxification of reactive oxygen species and have been modelled across multiple organisms using a variety of kinetic methods. However, the peroxiredoxin dimer-to-decamer transition has been underappreciated in these studies despite the 100-fold difference in activity between these forms. This is due to the lack of available kinetics and a theoretical framework for modelling this process. Using published isothermal titration calorimetry data, we obtained association and dissociation rate constants of 0.050 µM-4·s-1 and 0.055 s-1, respectively, for the dimer-decamer transition of human PRDX1. We developed an approach that greatly reduces the number of reactions and species needed to model the peroxiredoxin decamer oxidation cycle. Using these data, we simulated horse radish peroxidase competition and NADPH-oxidation linked assays and found that the dimer-decamer transition had an inhibition-like effect on peroxidase activity. Further, we incorporated this dimer-decamer topology and kinetics into a published and validated in vivo model of PRDX2 in the erythrocyte and found that it almost perfectly reconciled experimental and simulated responses of PRDX2 oxidation state to hydrogen peroxide insult. By accounting for the dimer-decamer transition of peroxiredoxins, we were able to resolve several discrepancies between experimental data and available kinetic models.
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Affiliation(s)
- Christopher J. Barry
- Laboratory for Molecular Systems Biology, Department of Biochemistry, Stellenbosch University, Stellenbosch 7600, South Africa;
| | - Ché S. Pillay
- School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg 3201, South Africa;
| | - Johann M. Rohwer
- Laboratory for Molecular Systems Biology, Department of Biochemistry, Stellenbosch University, Stellenbosch 7600, South Africa;
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29
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Potter AD, Baiocco CM, Papin JA, Criss AK. Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution in Neisseria gonorrhoeae during neutrophil co-culture. mSystems 2023; 8:e0126522. [PMID: 37387581 PMCID: PMC10470122 DOI: 10.1128/msystems.01265-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/19/2023] [Indexed: 07/01/2023] Open
Abstract
The ability of bacterial pathogens to metabolically adapt to the environmental conditions of their hosts is critical to both colonization and invasive disease. Infection with Neisseria gonorrhoeae (the gonococcus, Gc) is characterized by the influx of neutrophils [polymorphonuclear leukocytes (PMNs)], which fail to clear the bacteria and make antimicrobial products that can exacerbate tissue damage. The inability of the human host to clear Gc infection is particularly concerning in light of the emergence of strains that are resistant to all clinically recommended antibiotics. Bacterial metabolism represents a promising target for the development of new therapeutics against Gc. Here, we generated a curated genome-scale metabolic network reconstruction (GENRE) of Gc strain FA1090. This GENRE links genetic information to metabolic phenotypes and predicts Gc biomass synthesis and energy consumption. We validated this model with published data and in new results reported here. Contextualization of this model using the transcriptional profile of Gc exposed to PMNs revealed substantial rearrangements of Gc central metabolism and induction of Gc nutrient acquisition strategies for alternate carbon source use. These features enhanced the growth of Gc in the presence of neutrophils. From these results, we conclude that the metabolic interplay between Gc and PMNs helps define infection outcomes. The use of transcriptional profiling and metabolic modeling to reveal new mechanisms by which Gc persists in the presence of PMNs uncovers unique aspects of metabolism in this fastidious bacterium, which could be targeted to block infection and thereby reduce the burden of gonorrhea in the human population. IMPORTANCE The World Health Organization designated Gc as a high-priority pathogen for research and development of new antimicrobials. Bacterial metabolism is a promising target for new antimicrobials, as metabolic enzymes are widely conserved among bacterial strains and are critical for nutrient acquisition and survival within the human host. Here we used genome-scale metabolic modeling to characterize the core metabolic pathways of this fastidious bacterium and to uncover the pathways used by Gc during culture with primary human immune cells. These analyses revealed that Gc relies on different metabolic pathways during co-culture with human neutrophils than in rich media. Conditionally essential genes emerging from these analyses were validated experimentally. These results show that metabolic adaptation in the context of innate immunity is important to Gc pathogenesis. Identifying the metabolic pathways used by Gc during infection can highlight new therapeutic targets for drug-resistant gonorrhea.
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Affiliation(s)
- Aimee D. Potter
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Christopher M. Baiocco
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Alison K. Criss
- Department of Microbiology, Immunology, and Cancer Biology, University of Virginia, Charlottesville, Virginia, USA
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30
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Nègre D, Larhlimi A, Bertrand S. Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism? PLoS One 2023; 18:e0289757. [PMID: 37647283 PMCID: PMC10468094 DOI: 10.1371/journal.pone.0289757] [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: 04/07/2023] [Accepted: 07/24/2023] [Indexed: 09/01/2023] Open
Abstract
In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.
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Affiliation(s)
- Delphine Nègre
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
- Nantes Université, École Centrale Nantes, CNRS, Nantes, France
| | | | - Samuel Bertrand
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, Nantes, France
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Davies C, Morgan AE, Mc Auley MT. Computationally Modelling Cholesterol Metabolism and Atherosclerosis. BIOLOGY 2023; 12:1133. [PMID: 37627017 PMCID: PMC10452179 DOI: 10.3390/biology12081133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
Cardiovascular disease (CVD) is the leading cause of death globally. The underlying pathological driver of CVD is atherosclerosis. The primary risk factor for atherosclerosis is elevated low-density lipoprotein cholesterol (LDL-C). Dysregulation of cholesterol metabolism is synonymous with a rise in LDL-C. Due to the complexity of cholesterol metabolism and atherosclerosis mathematical models are routinely used to explore their non-trivial dynamics. Mathematical modelling has generated a wealth of useful biological insights, which have deepened our understanding of these processes. To date however, no model has been developed which fully captures how whole-body cholesterol metabolism intersects with atherosclerosis. The main reason for this is one of scale. Whole body cholesterol metabolism is defined by macroscale physiological processes, while atherosclerosis operates mainly at a microscale. This work describes how a model of cholesterol metabolism was combined with a model of atherosclerotic plaque formation. This new model is capable of reproducing the output from its parent models. Using the new model, we demonstrate how this system can be utilized to identify interventions that lower LDL-C and abrogate plaque formation.
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Affiliation(s)
- Callum Davies
- Department of Physical, Mathematical and Engineering Sciences, University of Chester, Chester CH1 4BJ, UK;
| | - Amy E. Morgan
- School of Health & Sport Sciences, Liverpool Hope University, Liverpool L16 9JD, UK;
| | - Mark T. Mc Auley
- Department of Physical, Mathematical and Engineering Sciences, University of Chester, Chester CH1 4BJ, UK;
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Deepa Maheshvare M, Raha S, König M, Pal D. A pathway model of glucose-stimulated insulin secretion in the pancreatic β-cell. Front Endocrinol (Lausanne) 2023; 14:1185656. [PMID: 37600713 PMCID: PMC10433753 DOI: 10.3389/fendo.2023.1185656] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 06/08/2023] [Indexed: 08/22/2023] Open
Abstract
The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic β-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct a novel data-driven kinetic model of GSIS in the pancreatic β-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and β-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and phenomenological equations for insulin secretion coupled to cellular energy state, ATP dynamics and (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the β-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and biphasic insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic β-cell.
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Affiliation(s)
- M. Deepa Maheshvare
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
| | - Soumyendu Raha
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
| | - Matthias König
- Institute for Biology, Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
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Scott WT, Benito-Vaquerizo S, Zimmermann J, Bajić D, Heinken A, Suarez-Diez M, Schaap PJ. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. PLoS Comput Biol 2023; 19:e1011363. [PMID: 37578975 PMCID: PMC10449394 DOI: 10.1371/journal.pcbi.1011363] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/24/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023] Open
Abstract
Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.
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Affiliation(s)
- William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
| | - Sara Benito-Vaquerizo
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Johannes Zimmermann
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Kiel, Germany
| | - Djordje Bajić
- Department of Biotechnology, Delft University of Technology, Delft, the Netherlands
| | - Almut Heinken
- Inserm U1256 Laboratoire nGERE, Université de Lorraine, Nancy, France
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
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Aldibani HKA, Rajput AJ, Rostami-Hodjegan A. In-depth analysis of patterns in selection of different physiologically-based pharmacokinetic modeling tools: Part II - Assessment of model reusability and comparison between open and non-open source-code software. Biopharm Drug Dispos 2023; 44:292-300. [PMID: 37083940 DOI: 10.1002/bdd.2360] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 11/17/2022] [Accepted: 04/04/2023] [Indexed: 04/22/2023]
Abstract
Whilst the reproducibility of models in the area of systems biology and quantitative systems pharmacology has been the focus of attention lately, the concept of 'reusability' is not addressed. With the advent of the 'Model Master File' dominating some regulatory discussions on pharmaceutical applications of physiologically-based pharmacokinetic (PBPK) models, reusability becomes a vital aspect of confidence in their use. Herein, we define 'reusability' specifically in the context of PBPK models and investigate the influence of open versus non-open source-code (NOSC) nature of the software on the extent of 'reusability'. Original articles (n = 145) that were associated with the development of novel PBPK models were identified as source models and citations to these reports, which involved further PBPK model development, were explored (n > 1800) for reuse cases of the source PBPK model whether in full or partial form. The nature of source-code was a major determinant of external reusability for PBPK models (>50% of the NOSC models as opposed <25% of open source-code [OSC]). Full reusability of the models was not common and mostly involved internal reuse of the OSC model (by the group who had previously developed the original model). The results were stratified by the software utilised (various), organisations involved (academia, industry, regulatory), and type of reusability (full vs. partial). The clear link between external reuse of models and NOSC PBPK software might stem from many elements related to quality and trust that require further investigation, and challenges the unfounded notion that OSC models are associated with higher uptake for reuse.
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Affiliation(s)
| | - Arham Jamaal Rajput
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
- Certara UK Limited, Sheffield, UK
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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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36
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Mirveis Z, Howe O, Cahill P, Patil N, Byrne HJ. Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives. Metabolomics 2023; 19:67. [PMID: 37482587 PMCID: PMC10363518 DOI: 10.1007/s11306-023-02031-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge. AIM This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway. KEY SCIENTIFIC CONCEPTS Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.
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Affiliation(s)
- Zohreh Mirveis
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland.
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland.
| | - Orla Howe
- School of Biological, Health and Sport Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Paul Cahill
- School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Nitin Patil
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
- School of Physics and Optometric & Clinical Sciences, Technological University Dublin, City Campus, Grangegorman, Dublin 7, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, City Campus, Camden Row, Dublin 8, Ireland
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Mazein A, Acencio ML, Balaur I, Rougny A, Welter D, Niarakis A, Ramirez Ardila D, Dogrusoz U, Gawron P, Satagopam V, Gu W, Kremer A, Schneider R, Ostaszewski M. A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance. FRONTIERS IN BIOINFORMATICS 2023; 3:1197310. [PMID: 37426048 PMCID: PMC10325725 DOI: 10.3389/fbinf.2023.1197310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023] Open
Abstract
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.
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Affiliation(s)
- Alexander Mazein
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche Pour la Polyarthrite Rhumatoïde–Genhotel, University Evry, Evry, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Diana Ramirez Ardila
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara, Türkiye
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Andreas Kremer
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
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Xu J. SBMLKinetics: a tool for annotation-independent classification of reaction kinetics for SBML models. BMC Bioinformatics 2023; 24:248. [PMID: 37312031 DOI: 10.1186/s12859-023-05380-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/07/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND Reaction networks are widely used as mechanistic models in systems biology to reveal principles of biological systems. Reactions are governed by kinetic laws that describe reaction rates. Selecting the appropriate kinetic laws is difficult for many modelers. There exist tools that attempt to find the correct kinetic laws based on annotations. Here, I developed annotation-independent technologies that assist modelers by focusing on finding kinetic laws commonly used for similar reactions. RESULTS Recommending kinetic laws and other analyses of reaction networks can be viewed as a classification problem. Existing approaches to determining similar reactions rely heavily on having good annotations, a condition that is often unsatisfied in model repositories such as BioModels. I developed an annotation-independent approach to find similar reactions via reaction classifications. I proposed a two-dimensional kinetics classification scheme (2DK) that analyzed reactions along the dimensions of kinetics type (K type) and reaction type (R type). I identified approximately ten mutually exclusive K types, including zeroth order, mass action, Michaelis-Menten, Hill kinetics, and others. R types were organized by the number of distinct reactants and the number of distinct products in reactions. I constructed a tool, SBMLKinetics, that inputted a collection of SBML models and then calculated reaction classifications as the probability of each 2DK class. The effectiveness of 2DK was evaluated on BioModels, and the scheme classified over 95% of the reactions. CONCLUSIONS 2DK had many applications. It provided a data-driven annotation-independent approach to recommending kinetic laws by using type common for the kind of models in combination with the R type of the reactions. Alternatively, 2DK could also be used to alert users that a kinetic law was unusual for the K type and R type. Last, 2DK provided a way to analyze groups of models to compare their kinetic laws. I applied 2DK to BioModels to compare the kinetics of signaling networks with the kinetics of metabolic networks and found significant differences in K type distributions.
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Affiliation(s)
- Jin Xu
- Department of Bioengineering, University of Washington, Seattle, USA.
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39
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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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40
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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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Maeda K, Kurata H. Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT. Int J Mol Sci 2023; 24:ijms24087296. [PMID: 37108453 PMCID: PMC10138937 DOI: 10.3390/ijms24087296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/05/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.
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Affiliation(s)
- Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan
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42
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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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Deepa Maheshvare M, Raha S, König M, Pal D. A Consensus Model of Glucose-Stimulated Insulin Secretion in the Pancreatic β -Cell. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.532028. [PMID: 36945414 PMCID: PMC10028967 DOI: 10.1101/2023.03.10.532028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic β -cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct the first consensus model of GSIS in the pancreatic β -cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and β -cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and equations for insulin secretion coupled to cellular energy state (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the β -cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic β -cell.
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Aghamiri SS, Puniya BL, Amin R, Helikar T. A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery. Front Immunol 2023; 14:1112985. [PMID: 36993954 PMCID: PMC10040975 DOI: 10.3389/fimmu.2023.1112985] [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: 11/30/2022] [Accepted: 02/22/2023] [Indexed: 03/14/2023] Open
Abstract
Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.
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Affiliation(s)
| | | | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
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Erdem C, Birtwistle MR. MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets. FRONTIERS IN SYSTEMS BIOLOGY 2023; 3:1099413. [PMID: 38269333 PMCID: PMC10807051 DOI: 10.3389/fsysb.2023.1099413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Computational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic with detailed equations describing known (or supposed) physicochemical processes, while some are statistical or machine learning-based approaches, that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore combining machine learned associations with mechanistic models to develop computational models that could more fully represent cellular behavior. In this proposed MEMMAL (MEchanistic Modeling with MAchine Learning) framework, machine learning/statistical models built using omics datasets provide predictions for new interactions between genes and proteins where there is physicochemical uncertainty. These interactions are used as a basis for new reactions in mechanistic models. As a test case, we focused on incorporating novel IFNγ/PD-L1 related associations into a large-scale mechanistic model for cell proliferation and death to better recapitulate the recently released NIH LINCS Consortium MCF10A dataset and enable description of the cellular response to checkpoint inhibitor immunotherapies. This work is a template for combining big-data-inferred interactions with mechanistic models, which could be more broadly applicable for building multi-scale precision medicine and whole cell models.
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Affiliation(s)
- Cemal Erdem
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, United States
| | - Marc R. Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, United States
- Department of Bioengineering, Clemson University, Clemson, SC, United States
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Ildefonso GV, Oliver Metzig M, Hoffmann A, Harris LA, Lopez CF. A biochemical necroptosis model explains cell-type-specific responses to cell death cues. Biophys J 2023; 122:817-834. [PMID: 36710493 PMCID: PMC10027451 DOI: 10.1016/j.bpj.2023.01.035] [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: 05/06/2022] [Revised: 12/31/2022] [Accepted: 01/24/2023] [Indexed: 01/30/2023] Open
Abstract
Necroptosis is a form of regulated cell death associated with degenerative disorders, autoimmune and inflammatory diseases, and cancer. To better understand the biochemical mechanisms regulating necroptosis, we constructed a detailed computational model of tumor necrosis factor-induced necroptosis based on known molecular interactions from the literature. Intracellular protein levels, used as model inputs, were quantified using label-free mass spectrometry, and the model was calibrated using Bayesian parameter inference to experimental protein time course data from a well-established necroptosis-executing cell line. The calibrated model reproduced the dynamics of phosphorylated mixed lineage kinase domain-like protein, an established necroptosis reporter. A subsequent dynamical systems analysis identified four distinct modes of necroptosis signal execution, distinguished by rate constant values and the roles of the RIP1 deubiquitinating enzymes A20 and CYLD. In one case, A20 and CYLD both contribute to RIP1 deubiquitination, in another RIP1 deubiquitination is driven exclusively by CYLD, and in two modes either A20 or CYLD acts as the driver with the other enzyme, counterintuitively, inhibiting necroptosis. We also performed sensitivity analyses of initial protein concentrations and rate constants to identify potential targets for modulating necroptosis sensitivity within each mode. We conclude by associating numerous contrasting and, in some cases, counterintuitive experimental results reported in the literature with one or more of the model-predicted modes of necroptosis execution. In all, we demonstrate that a consensus pathway model of tumor necrosis factor-induced necroptosis can provide insights into unresolved controversies regarding the molecular mechanisms driving necroptosis execution in numerous cell types under different experimental conditions.
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Affiliation(s)
- Geena V Ildefonso
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Marie Oliver Metzig
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, California; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California
| | - Alexander Hoffmann
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, California; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, California
| | - Leonard A Harris
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, Arkansas; Interdisciplinary Graduate Program in Cell and Molecular Biology, University of Arkansas, Fayetteville, Arkansas; Cancer Biology Program, Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
| | - Carlos F Lopez
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.
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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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48
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Höpfl S, Pleiss J, Radde NE. Bayesian estimation reveals that reproducible models in Systems Biology get more citations. Sci Rep 2023; 13:2695. [PMID: 36792648 PMCID: PMC9931699 DOI: 10.1038/s41598-023-29340-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 02/02/2023] [Indexed: 02/17/2023] Open
Abstract
The Systems Biology community has taken numerous actions to develop data and modeling standards towards FAIR data and model handling. Nevertheless, the debate about incentives and rewards for individual researchers to make their results reproducible is ongoing. Here, we pose the specific question of whether reproducible models have a higher impact in terms of citations. Therefore, we statistically analyze 328 published models recently classified by Tiwari et al. based on their reproducibility. For hypothesis testing, we use a flexible Bayesian approach that provides complete distributional information for all quantities of interest and can handle outliers. The results show that in the period from 2013, i.e., 10 years after the introduction of SBML, to 2020, the group of reproducible models is significantly more cited than the non-reproducible group. We show that differences in journal impact factors do not explain this effect and that this effect increases with additional standardization of data and error model integration via PEtab. Overall, our statistical analysis demonstrates the long-term merits of reproducible modeling for the individual researcher in terms of citations. Moreover, it provides evidence for the increased use of reproducible models in the scientific community.
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Affiliation(s)
- Sebastian Höpfl
- Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Nicole E Radde
- Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70569, Stuttgart, Germany.
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Holistic View on the Structure of Immune Response: Petri Net Model. Biomedicines 2023; 11:biomedicines11020452. [PMID: 36830988 PMCID: PMC9953182 DOI: 10.3390/biomedicines11020452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 02/08/2023] Open
Abstract
The simulation of immune response is a challenging task because quantitative data are scarce. Quantitative theoretical models either focus on specific cell-cell interactions or have to make assumptions about parameters. The broad variation of, e.g., the dimensions and abundance between lymph nodes as well as between individual patients hampers conclusive quantitative modeling. No theoretical model has been established representing a consensus on the set of major cellular processes involved in the immune response. In this paper, we apply the Petri net formalism to construct a semi-quantitative mathematical model of the lymph nodes. The model covers the major cellular processes of immune response and fulfills the formal requirements of Petri net models. The intention is to develop a model taking into account the viewpoints of experienced pathologists and computer scientists in the field of systems biology. In order to verify formal requirements, we discuss invariant properties and apply the asynchronous firing rule of a place/transition net. Twenty-five transition invariants cover the model, and each is assigned to a functional mode of the immune response. In simulations, the Petri net model describes the dynamic modes of the immune response, its adaption to antigens, and its loss of memory.
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50
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Cain JY, Yu JS, Bagheri N. The in silico lab: Improving academic code using lessons from biology. Cell Syst 2023; 14:1-6. [PMID: 36657389 DOI: 10.1016/j.cels.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/27/2022] [Accepted: 11/22/2022] [Indexed: 01/19/2023]
Abstract
"Good code" is often regarded as a nebulous, impractical ideal. Common best practices toward improving code quality can be inaccessible to those without a rigorous computer science or software engineering background, contributing to a gap between advancing scientific research and FAIR practices. We seek to equip researchers with the necessary background and context to tackle the challenge of improving code quality in computational biology research using analogies from biology to synthesize why certain best practices are critical for advancing computational research. Improving code quality requires active stewardship; we encourage researchers to deliberately adopt and share practices that ensure reusability, repeatability, and reproducibility.
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Affiliation(s)
- Jason Y Cain
- Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Jessica S Yu
- Department of Biology, University of Washington, Seattle, WA 98195, USA
| | - Neda Bagheri
- Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA; Department of Biology, University of Washington, Seattle, WA 98195, USA.
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