1
|
Shahidi N, Pan M, Tran K, Crampin EJ, Nickerson DP. SBML to bond graphs: From conversion to composition. Math Biosci 2022; 352:108901. [PMID: 36096376 DOI: 10.1016/j.mbs.2022.108901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 08/15/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
The Systems Biology Markup Language (SBML) is a popular software-independent XML-based format for describing models of biological phenomena. The BioModels Database is the largest online repository of SBML models. Several tools and platforms are available to support the reuse and composition of SBML models. However, these tools do not explicitly assess whether models are physically plausible or thermodynamically consistent. This often leads to ill-posed models that are physically impossible, impeding the development of realistic complex models in biology. Here, we present a framework that can automatically convert SBML models into bond graphs, which imposes energy conservation laws on these models. The new bond graph models are easily mergeable, resulting in physically plausible coupled models. We illustrate this by automatically converting and coupling a model of pyruvate distribution to a model of the pentose phosphate pathway.
Collapse
Affiliation(s)
- Niloofar Shahidi
- Auckland Bioengineering Institute, University of Auckland, Auckland, 1010, New Zealand.
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, 3010, Victoria, Australia; School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, 3010, Victoria, Australia
| | - Kenneth Tran
- Auckland Bioengineering Institute, University of Auckland, Auckland, 1010, New Zealand
| | - Edmund J Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, 3010, Victoria, Australia; School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, 3010, Victoria, Australia; ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3010, Victoria, Australia; School of Medicine, University of Melbourne, Melbourne, 3010, Victoria, Australia
| | - David P Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, 1010, New Zealand
| |
Collapse
|
2
|
Gawthrop PJ, Pan M. Network thermodynamics of biological systems: A bond graph approach. Math Biosci 2022; 352:108899. [PMID: 36057321 DOI: 10.1016/j.mbs.2022.108899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 10/14/2022]
Abstract
Edmund Crampin (1973-2021) was at the forefront of Systems Biology research and his work will influence the field for years to come. This paper brings together and summarises the seminal work of his group in applying energy-based bond graph methods to biological systems. In particular, this paper: (a) motivates the need to consider energy in modelling biology; (b) introduces bond graphs as a methodology for achieving this; (c) describes extensions to modelling electrochemical transduction; (d) outlines how bond graph models can be constructed in a modular manner and (e) describes stoichiometric approaches to deriving fundamental properties of reaction networks. These concepts are illustrated using a new bond graph model of photosynthesis in chloroplasts.
Collapse
Affiliation(s)
- Peter J Gawthrop
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia.
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia; School of Mathematics and Statistics, University of Melbourne, Victoria 3010, Australia
| |
Collapse
|
3
|
Shahidi N, Pan M, Tran K, Crampin EJ, Nickerson DP. A semantics, energy-based approach to automate biomodel composition. PLoS One 2022; 17:e0269497. [PMID: 35657966 PMCID: PMC9165793 DOI: 10.1371/journal.pone.0269497] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/20/2022] [Indexed: 11/19/2022] Open
Abstract
Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each model's components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models.
Collapse
Affiliation(s)
- Niloofar Shahidi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Victoria, Australia
| | - Kenneth Tran
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Victoria, Australia
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - David P. Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| |
Collapse
|
4
|
Cudmore P, Pan M, Gawthrop PJ, Crampin EJ. Analysing and simulating energy-based models in biology using BondGraphTools. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2021; 44:148. [PMID: 34904197 DOI: 10.1140/epje/s10189-021-00152-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 11/18/2021] [Indexed: 06/14/2023]
Abstract
Like all physical systems, biological systems are constrained by the laws of physics. However, mathematical models of biochemistry frequently neglect the conservation of energy, leading to unrealistic behaviour. Energy-based models that are consistent with conservation of mass, charge and energy have the potential to aid the understanding of complex interactions between biological components, and are becoming easier to develop with recent advances in experimental measurements and databases. In this paper, we motivate the use of bond graphs (a modelling tool from engineering) for energy-based modelling and introduce, BondGraphTools, a Python library for constructing and analysing bond graph models. We use examples from biochemistry to illustrate how BondGraphTools can be used to automate model construction in systems biology while maintaining consistency with the laws of physics.
Collapse
Affiliation(s)
- Peter Cudmore
- Systems Biology Laboratory, School of Mathematics and Statistics, Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia.
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC, 3010, Australia.
| | - Peter J Gawthrop
- Systems Biology Laboratory, School of Mathematics and Statistics, Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Edmund J Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC, 3010, Australia
- School of Medicine, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, 3010, Australia
| |
Collapse
|
5
|
Pan M, Gawthrop PJ, Cursons J, Crampin EJ. Modular assembly of dynamic models in systems biology. PLoS Comput Biol 2021; 17:e1009513. [PMID: 34644304 PMCID: PMC8544865 DOI: 10.1371/journal.pcbi.1009513] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/25/2021] [Accepted: 09/30/2021] [Indexed: 11/18/2022] Open
Abstract
It is widely acknowledged that the construction of large-scale dynamic models in systems biology requires complex modelling problems to be broken up into more manageable pieces. To this end, both modelling and software frameworks are required to enable modular modelling. While there has been consistent progress in the development of software tools to enhance model reusability, there has been a relative lack of consideration for how underlying biophysical principles can be applied to this space. Bond graphs combine the aspects of both modularity and physics-based modelling. In this paper, we argue that bond graphs are compatible with recent developments in modularity and abstraction in systems biology, and are thus a desirable framework for constructing large-scale models. We use two examples to illustrate the utility of bond graphs in this context: a model of a mitogen-activated protein kinase (MAPK) cascade to illustrate the reusability of modules and a model of glycolysis to illustrate the ability to modify the model granularity.
Collapse
Affiliation(s)
- Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Parkville, Victoria, Australia
| | - Peter J. Gawthrop
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia
| | - Joseph Cursons
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Parkville, Victoria, Australia
- School of Medicine, University of Melbourne, Parkville, Victoria, Australia
| |
Collapse
|
6
|
Gawthrop PJ, Pan M, Crampin EJ. Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data. J R Soc Interface 2021; 18:20210478. [PMID: 34428949 PMCID: PMC8385351 DOI: 10.1098/rsif.2021.0478] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/02/2021] [Indexed: 12/14/2022] Open
Abstract
Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here, we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermodynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli and published experimental data.
Collapse
Affiliation(s)
- Peter J. Gawthrop
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, School of Chemical and Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, School of Chemical and Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
| |
Collapse
|
7
|
Schölzel C, Blesius V, Ernst G, Dominik A. Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective. NPJ Syst Biol Appl 2021; 7:27. [PMID: 34083542 PMCID: PMC8175692 DOI: 10.1038/s41540-021-00182-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/19/2021] [Indexed: 02/06/2023] Open
Abstract
Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.
Collapse
Affiliation(s)
- Christopher Schölzel
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany.
| | - Valeria Blesius
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| | - Gernot Ernst
- Vestre Viken Hospital Trust, Kongsberg, Norway
- University of Oslo, Oslo, Norway
| | - Andreas Dominik
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| |
Collapse
|
8
|
Shahidi N, Pan M, Safaei S, Tran K, Crampin EJ, Nickerson DP. Hierarchical semantic composition of biosimulation models using bond graphs. PLoS Comput Biol 2021; 17:e1008859. [PMID: 33983945 PMCID: PMC8148364 DOI: 10.1371/journal.pcbi.1008859] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/25/2021] [Accepted: 04/27/2021] [Indexed: 11/19/2022] Open
Abstract
Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.
Collapse
Affiliation(s)
- Niloofar Shahidi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
| | - Soroush Safaei
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Kenneth Tran
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
| | - David P. Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| |
Collapse
|
9
|
Sarwar DM, Nickerson DP. CellML Model Discovery with the Physiome Model Repository. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11681-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
|
10
|
Neal ML, Thompson CT, Kim KG, James RC, Cook DL, Carlson BE, Gennari JH. SemGen: a tool for semantics-based annotation and composition of biosimulation models. Bioinformatics 2019; 35:1600-1602. [PMID: 30256901 PMCID: PMC6499248 DOI: 10.1093/bioinformatics/bty829] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 09/09/2018] [Accepted: 09/24/2018] [Indexed: 01/05/2023] Open
Abstract
SUMMARY As the number and complexity of biosimulation models grows, so do demands for tools that can help users understand models and compose more comprehensive and accurate systems from existing models. SemGen is a tool for semantics-based annotation and composition of biosimulation models designed to address this demand. A key SemGen capability is to decompose and then integrate models across existing model exchange formats including SBML and CellML. To support this capability, we use semantic annotations to explicitly capture the underlying biological and physical meanings of the entities and processes that are modeled. SemGen leverages annotations to expose a model's biological and computational architecture and to help automate model composition. AVAILABILITY AND IMPLEMENTATION SemGen is freely available at https://github.com/SemBioProcess/SemGen. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Maxwell L Neal
- Seattle Children's Research Institute, Center for Global Infectious Disease Research, Seattle, WA, USA
| | - Christopher T Thompson
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Karam G Kim
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Ryan C James
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Daniel L Cook
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Brian E Carlson
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| |
Collapse
|
11
|
Neal ML, König M, Nickerson D, Mısırlı G, Kalbasi R, Dräger A, Atalag K, Chelliah V, Cooling MT, Cook DL, Crook S, de Alba M, Friedman SH, Garny A, Gennari JH, Gleeson P, Golebiewski M, Hucka M, Juty N, Myers C, Olivier BG, Sauro HM, Scharm M, Snoep JL, Touré V, Wipat A, Wolkenhauer O, Waltemath D. Harmonizing semantic annotations for computational models in biology. Brief Bioinform 2019; 20:540-550. [PMID: 30462164 PMCID: PMC6433895 DOI: 10.1093/bib/bby087] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 08/08/2018] [Accepted: 08/17/2018] [Indexed: 02/06/2023] Open
Abstract
Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.
Collapse
Affiliation(s)
- Maxwell Lewis Neal
- Seattle Children’s Research Institute, Center for Global Infectious Disease Research, Seattle, USA
| | - Matthias König
- Department of Biology, Humboldt-University Berlin, Institute for Theoretical Biology, Berlin, Germany
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, UK
| | - Reza Kalbasi
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Koray Atalag
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Vijayalakshmi Chelliah
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael T Cooling
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Daniel L Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA
| | - Miguel de Alba
- German Federal Institute for Risk Assessment, Berlin, Germany
| | | | - Alan Garny
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA
| | - Brett G Olivier
- Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Modelling of Biological Processes, BioQUANT/COS, Heidelberg University, Germany
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Jacky L Snoep
- Department of Biochemistry, Stellenbosch University, Matieland, South Africa
- Department of Molecular Cell Physiology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Manchester Institute for Biotechnology, University of Manchester, Manchester, UK
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa
| | - Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| |
Collapse
|
12
|
Hellerstein JL, Gu S, Choi K, Sauro HM. Recent advances in biomedical simulations: a manifesto for model engineering. F1000Res 2019; 8:F1000 Faculty Rev-261. [PMID: 30881691 PMCID: PMC6406177 DOI: 10.12688/f1000research.15997.1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/21/2019] [Indexed: 01/18/2023] Open
Abstract
Biomedical simulations are widely used to understand disease, engineer cells, and model cellular processes. In this article, we explore how to improve the quality of biomedical simulations by developing simulation models using tools and practices employed in software engineering. We refer to this direction as model engineering. Not all techniques used by software engineers are directly applicable to model engineering, and so some adaptations are required. That said, we believe that simulation models can benefit from software engineering practices for requirements, design, and construction as well as from software engineering tools for version control, error checking, and testing. Here we survey current efforts to improve simulation quality and discuss promising research directions for model engineering.
Collapse
Affiliation(s)
| | - Stanley Gu
- Department of Bioengineering, William H. Foege Building, University of Washington, Seattle, WA, Box 355061, USA
| | - Kiri Choi
- Department of Bioengineering, William H. Foege Building, University of Washington, Seattle, WA, Box 355061, USA
| | - Herbert M. Sauro
- Department of Bioengineering, William H. Foege Building, University of Washington, Seattle, WA, Box 355061, USA
| |
Collapse
|
13
|
Viswan NA, HarshaRani GV, Stefan MI, Bhalla US. FindSim: A Framework for Integrating Neuronal Data and Signaling Models. Front Neuroinform 2018; 12:38. [PMID: 29997492 PMCID: PMC6028806 DOI: 10.3389/fninf.2018.00038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 06/05/2018] [Indexed: 12/30/2022] Open
Abstract
Current experiments touch only small but overlapping parts of very complex subcellular signaling networks in neurons. Even with modern optical reporters and pharmacological manipulations, a given experiment can only monitor and control a very small subset of the diverse, multiscale processes of neuronal signaling. We have developed FindSim (Framework for Integrating Neuronal Data and SIgnaling Models) to anchor models to structured experimental datasets. FindSim is a framework for integrating many individual electrophysiological and biochemical experiments with large, multiscale models so as to systematically refine and validate the model. We use a structured format for encoding the conditions of many standard physiological and pharmacological experiments, specifying which parts of the model are involved, and comparing experiment outcomes with model output. A database of such experiments is run against successive generations of composite cellular models to iteratively improve the model against each experiment, while retaining global model validity. We suggest that this toolchain provides a principled and scalable way to tackle model complexity and diversity of data sources.
Collapse
Affiliation(s)
- Nisha A Viswan
- National Centre for Biological Sciences, Bangalore, India.,Tata Institute of Fundamental Research, The University of Trans-Disciplinary Health Sciences and Technology, Bangalore, India
| | | | - Melanie I Stefan
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,ZJU-UoE Institute, Zhejiang University, Hangzhou, China
| | | |
Collapse
|
14
|
Marshall-Colon A, Long SP, Allen DK, Allen G, Beard DA, Benes B, von Caemmerer S, Christensen AJ, Cox DJ, Hart JC, Hirst PM, Kannan K, Katz DS, Lynch JP, Millar AJ, Panneerselvam B, Price ND, Prusinkiewicz P, Raila D, Shekar RG, Shrivastava S, Shukla D, Srinivasan V, Stitt M, Turk MJ, Voit EO, Wang Y, Yin X, Zhu XG. Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform. FRONTIERS IN PLANT SCIENCE 2017; 8:786. [PMID: 28555150 PMCID: PMC5430029 DOI: 10.3389/fpls.2017.00786] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 04/26/2017] [Indexed: 05/18/2023]
Abstract
Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.
Collapse
Affiliation(s)
- Amy Marshall-Colon
- Department of Plant Biology, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Stephen P. Long
- Department of Plant Biology, University of Illinois at Urbana–Champaign, UrbanaIL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana–Champaign, UrbanaIL, USA
- Department of Crop Sciences, University of Illinois, UrbanaIL, USA
| | - Douglas K. Allen
- United States Department of Agriculture – Agricultural Research Service–Donald Danforth Plant Science Center, St. LouisMO, USA
| | - Gabrielle Allen
- Department of Astronomy–College of Education, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Daniel A. Beard
- Department of Molecular & Integrative Physiology, University of Michigan, Ann ArborMI, USA
| | - Bedrich Benes
- Department of Computer Graphics Technology, Purdue University, West LafayetteIN, USA
| | - Susanne von Caemmerer
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biological Sciences, Australian National University, ActonACT, Australia
| | - A. J. Christensen
- National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Donna J. Cox
- National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - John C. Hart
- Department of Computer Science, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Peter M. Hirst
- Department of Horticulture and Landscape Architecture, Purdue University, West LafayetteIN, USA
| | - Kavya Kannan
- Department of Plant Biology, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Daniel S. Katz
- National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Jonathan P. Lynch
- Department of Plant Science, Pennsylvania State University, University ParkPA, USA
- Centre for Plant Integrative Biology, University of NottinghamNottingham, UK
| | - Andrew J. Millar
- SynthSys and School of Biological Sciences, Edinburgh UniversityEdinburgh, UK
| | - Balaji Panneerselvam
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | | | | | - David Raila
- National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Rachel G. Shekar
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana–Champaign, UrbanaIL, USA
| | - Stuti Shrivastava
- Department of Plant Biology, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Diwakar Shukla
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana–Champaign, UrbanaIL, USA
| | - Venkatraman Srinivasan
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana–Champaign, UrbanaIL, USA
| | - Mark Stitt
- Max Planck Institute of Molecular Plant PhysiologyGolm, Germany
| | - Matthew J. Turk
- School of Information Science, University of Illinois, Urbana–Champaign, UrbanaIL, USA
| | - Eberhard O. Voit
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, AtlantaGA, USA
| | - Yu Wang
- Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana–Champaign, UrbanaIL, USA
| | - Xinyou Yin
- Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & ResearchWageningen, Netherlands
| | - Xin-Guang Zhu
- CAS Key Laboratory for Computational Biology–State Key Laboratory for Hybrid Rice, Partner Institute for Computational Biology, Chinese Academy of SciencesShanghai, China
| |
Collapse
|
15
|
Cooling MT, Nickerson DP, Nielsen PMF, Hunter PJ. Modular modelling with Physiome standards. J Physiol 2016; 594:6817-6831. [PMID: 27353233 PMCID: PMC5134412 DOI: 10.1113/jp272633] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 06/26/2016] [Indexed: 01/27/2023] Open
Abstract
KEY POINTS The complexity of computational models is increasing, supported by research in modelling tools and frameworks. But relatively little thought has gone into design principles for complex models. We propose a set of design principles for complex model construction with the Physiome standard modelling protocol CellML. By following the principles, models are generated that are extensible and are themselves suitable for reuse in larger models of increasing complexity. We illustrate these principles with examples including an architectural prototype linking, for the first time, electrophysiology, thermodynamically compliant metabolism, signal transduction, gene regulation and synthetic biology. The design principles complement other Physiome research projects, facilitating the application of virtual experiment protocols and model analysis techniques to assist the modelling community in creating libraries of composable, characterised and simulatable quantitative descriptions of physiology. ABSTRACT The ability to produce and customise complex computational models has great potential to have a positive impact on human health. As the field develops towards whole-cell models and linking such models in multi-scale frameworks to encompass tissue, organ, or organism levels, reuse of previous modelling efforts will become increasingly necessary. Any modelling group wishing to reuse existing computational models as modules for their own work faces many challenges in the context of construction, storage, retrieval, documentation and analysis of such modules. Physiome standards, frameworks and tools seek to address several of these challenges, especially for models expressed in the modular protocol CellML. Aside from providing a general ability to produce modules, there has been relatively little research work on architectural principles of CellML models that will enable reuse at larger scales. To complement and support the existing tools and frameworks, we develop a set of principles to address this consideration. The principles are illustrated with examples that couple electrophysiology, signalling, metabolism, gene regulation and synthetic biology, together forming an architectural prototype for whole-cell modelling (including human intervention) in CellML. Such models illustrate how testable units of quantitative biophysical simulation can be constructed. Finally, future relationships between modular models so constructed and Physiome frameworks and tools are discussed, with particular reference to how such frameworks and tools can in turn be extended to complement and gain more benefit from the results of applying the principles.
Collapse
Affiliation(s)
| | | | - Poul M. F. Nielsen
- Auckland Bioengineering Institutethe University of AucklandNew Zealand
- Department of Engineering Sciencethe University of AucklandNew Zealand
| | - Peter J. Hunter
- Auckland Bioengineering Institutethe University of AucklandNew Zealand
| |
Collapse
|
16
|
Neal ML, Gennari JH, Cook DL. Qualitative causal analyses of biosimulation models. CEUR WORKSHOP PROCEEDINGS 2016; 1747:http://ceur-ws.org/Vol-1747/IT604_ICBO2016.pdf. [PMID: 28804276 PMCID: PMC5551042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We describe an approach for performing qualitative, systems-level causal analyses on biosimulation models that leverages semantics-based modeling formats, formal ontology, and automated inference. The approach allows users to quickly investigate how a qualitative perturbation to an element within a model's network (an increment or decrement) propagates throughout the modeled system. To support such analyses, we must interpret and annotate the semantics of the models, including both the physical properties modeled and the dependencies that relate them. We build from prior work understanding the semantics of biological properties, but here, we focus on the semantics for dependencies, which provide the critical knowledge necessary for causal analysis of biosimulation models. We describe augmentations to the Ontology of Physics for Biology, via OWL axioms and SWRL rules, and demonstrate that a reasoner can then infer how an annotated model's physical properties influence each other in a qualitative sense. Our goal is to provide researchers with a tool that helps bring the systems-level network dynamics of biosimulation models into perspective, thus facilitating model development, testing, and application.
Collapse
Affiliation(s)
- Maxwell L Neal
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Daniel L Cook
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| |
Collapse
|
17
|
Jardine B, Raymond GM, Bassingthwaighte JB. Semi-automated Modular Program Constructor for physiological modeling: Building cell and organ models. F1000Res 2015; 4:1461. [PMID: 28698795 PMCID: PMC5488124 DOI: 10.12688/f1000research.7476.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/13/2016] [Indexed: 11/20/2022] Open
Abstract
The Modular Program Constructor (MPC) is an open-source Java based modeling
utility, built upon JSim's Mathematical Modeling Language (MML) ( http://www.physiome.org/jsim/) that uses directives embedded in
model code to construct larger, more complicated models quickly and with less
error than manually combining models. A major obstacle in writing complex models
for physiological processes is the large amount of time it takes to model the
myriad processes taking place simultaneously in cells, tissues, and organs. MPC
replaces this task with code-generating algorithms that take model code from
several different existing models and produce model code for a new JSim model.
This is particularly useful during multi-scale model development where many
variants are to be configured and tested against data. MPC encodes and preserves
information about how a model is built from its simpler model modules, allowing
the researcher to quickly substitute or update modules for hypothesis testing.
MPC is implemented in Java and requires JSim to use its output. MPC source code
and documentation are available at http://www.physiome.org/software/MPC/.
Collapse
Affiliation(s)
- Bartholomew Jardine
- Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA
| | - Gary M Raymond
- Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA
| | | |
Collapse
|