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Gast R, Knösche TR, Kennedy A. PyRates-A code-generation tool for modeling dynamical systems in biology and beyond. PLoS Comput Biol 2023; 19:e1011761. [PMID: 38150479 PMCID: PMC10775981 DOI: 10.1371/journal.pcbi.1011761] [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: 09/15/2023] [Revised: 01/09/2024] [Accepted: 12/14/2023] [Indexed: 12/29/2023] Open
Abstract
The mathematical study of real-world dynamical systems relies on models composed of differential equations. Numerical methods for solving and analyzing differential equation systems are essential when complex biological problems have to be studied, such as the spreading of a virus, the evolution of competing species in an ecosystem, or the dynamics of neurons in the brain. Here we present PyRates, a Python-based software for modeling and analyzing differential equation systems via numerical methods. PyRates is specifically designed to account for the inherent complexity of biological systems. It provides a new language for defining models that mirrors the modular organization of real-world dynamical systems and thus simplifies the implementation of complex networks of interacting dynamic entities. Furthermore, PyRates provides extensive support for the various forms of interaction delays that can be observed in biological systems. The core of PyRates is a versatile code-generation system that translates user-defined models into "backend" implementations in various languages, including Python, Fortran, Matlab, and Julia. This allows users to apply a wide range of analysis methods for dynamical systems, eliminating the need for manual translation between code bases. PyRates may also be used as a model definition interface for the creation of custom dynamical systems tools. To demonstrate this, we developed two extensions of PyRates for common analyses of dynamic models of biological systems: PyCoBi for bifurcation analysis and RectiPy for parameter fitting. We demonstrate in a series of example models how PyRates can be used in combination with PyCoBi and RectiPy for model analysis and fitting. Together, these tools offer a versatile framework for applying computational modeling and numerical analysis methods to dynamical systems in biology and beyond.
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Affiliation(s)
- Richard Gast
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland, United States of America
| | - Thomas R. Knösche
- Brain Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ann Kennedy
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, Maryland, United States of America
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2
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May RW, Maso Talou GD, Clark AR, Mynard JP, Smolich JJ, Blanco PJ, Müller LO, Gentles TL, Bloomfield FH, Safaei S. From fetus to neonate: A review of cardiovascular modeling in early life. WIREs Mech Dis 2023:e1608. [DOI: 10.1002/wsbm.1608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 01/31/2023] [Accepted: 03/03/2023] [Indexed: 04/03/2023]
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3
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Kim N, Pronto JD, Nickerson DP, Taberner AJ, Hunter PJ. A novel modular modeling approach for understanding different electromechanics between left and right heart in rat. Front Physiol 2022; 13:965054. [PMID: 36176770 PMCID: PMC9513479 DOI: 10.3389/fphys.2022.965054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 08/22/2022] [Indexed: 12/01/2022] Open
Abstract
While ion channels and transporters involved in excitation-contraction coupling have been linked and constructed as comprehensive computational models, validation of whether each individual component of a model can be reused has not been previously attempted. Here we address this issue while using a novel modular modeling approach to investigate the underlying mechanism for the differences between left ventricle (LV) and right ventricle (RV). Our model was developed from modules constructed using the module assembly principles of the CellML model markup language. The components of three existing separate models of cardiac function were disassembled as to create smaller modules, validated individually, and then the component parts were combined into a new integrative model of a rat ventricular myocyte. The model was implemented in OpenCOR using the CellML standard in order to ensure reproducibility. Simulated action potential (AP), Ca2+ transient, and tension were in close agreement with our experimental measurements: LV AP showed a prolonged duration and a more prominent plateau compared with RV AP; Ca2+ transient showed prolonged duration and slow decay in LV compared to RV; the peak value and relaxation of tension were larger and slower, respectively, in LV compared to RV. Our novel approach of module-based mathematical modeling has established that the ionic mechanisms underlying the APs and Ca2+ handling play a role in the variation in force production between ventricles. This simulation process also provides a useful way to reuse and elaborate upon existing models in order to develop a new model.
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Affiliation(s)
- Nari Kim
- NLRL for Innovative Cardiovascular Engineering, Department of Physiology, College of Medicine, Inje University, Busan, South Korea
- Cardiovascular and Metabolic Disease Center, Inje University, Busan, South Korea
- *Correspondence: Nari Kim,
| | - Julius D. Pronto
- NLRL for Innovative Cardiovascular Engineering, Department of Physiology, College of Medicine, Inje University, Busan, South Korea
- Cardiovascular and Metabolic Disease Center, Inje University, Busan, South Korea
| | - David P. Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Andrew J. Taberner
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Peter J. Hunter
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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4
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Electro-anatomical computational cardiology in humans and experimental animal models. TRANSLATIONAL RESEARCH IN ANATOMY 2022. [DOI: 10.1016/j.tria.2022.100162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Rampadarath A, Nickerson DP. Automated Execution of Simulation Studies in Systems Medicine Using SED-ML and COMBINE Archive. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11688-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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7
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Koutrouli M, Hatzis P, Pavlopoulos GA. Exploring Networks in the STRING and Reactome Database. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11516-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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8
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Guimera AM, Shanley DP, Proctor CJ. Modelling the role of redox-related mechanisms in musculoskeletal ageing. Free Radic Biol Med 2019; 132:11-18. [PMID: 30219703 DOI: 10.1016/j.freeradbiomed.2018.09.013] [Citation(s) in RCA: 5] [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: 04/13/2018] [Revised: 09/07/2018] [Accepted: 09/12/2018] [Indexed: 02/06/2023]
Abstract
The decline in the musculoskeletal system with age is driven at the cellular level by random molecular damage. Cells possess mechanisms to repair or remove damage and many of the pathways involved in this response are regulated by redox signals. However, with ageing there is an increase in oxidative stress which can lead to chronic inflammation and disruption of redox signalling pathways. The complexity of the processes involved has led to the use of computational modelling to help increase our understanding of the system, test hypotheses and make testable predictions. This paper will give a brief background of the biological systems that have been modelled, an introduction to computational modelling, a review of models that involve redox-related mechanisms that are applicable to musculoskeletal ageing, and finally a discussion of the future potential for modelling in this field.
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Affiliation(s)
- Alvaro Martinez Guimera
- Institute for Cell and Molecular Biosciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK
| | - Daryl P Shanley
- Institute for Cell and Molecular Biosciences, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK
| | - Carole J Proctor
- Institute of Cellular Medicine, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK.
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9
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Makowiec D, Wdowczyk J, Struzik ZR. Heart Rhythm Insights Into Structural Remodeling in Atrial Tissue: Timed Automata Approach. Front Physiol 2019; 9:1859. [PMID: 30692928 PMCID: PMC6340163 DOI: 10.3389/fphys.2018.01859] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 12/11/2018] [Indexed: 12/19/2022] Open
Abstract
The heart rhythm of a person following heart transplantation (HTX) is assumed to display an intrinsic cardiac rhythm because it is significantly less influenced by the autonomic nervous system-the main source of heart rate variability in healthy people. Therefore, such a rhythm provides evidence for arrhythmogenic processes developing, usually silently, in the cardiac tissue. A model is proposed to simulate alterations in the cardiac tissue and to observe the effects of these changes on the resulting heart rhythm. The hybrid automata framework used makes it possible to represent reliably and simulate efficiently both the electrophysiology of a cardiac cell and the tissue organization. The curve fitting method used in the design of the hybrid automaton cycle follows the well-recognized physiological phases of the atrial myocyte membrane excitation. Moreover, knowledge of the complex architecture of the right atrium, the ability of the almost free design of intercellular connections makes the automata approach the only one possible. Two particular aspects are investigated: impairment of the impulse transmission between cells and structural changes in intercellular connections. The first aspect models the observed fatigue of cells due to specific cardiac tissue diseases. The second aspect simulates the increase in collagen deposition with aging. Finally, heart rhythms arising from the model are validated with the sinus heart rhythms recorded in HTX patients. The modulation in the impairment of the impulse transmission between cells reveals qualitatively the abnormally high heart rate variability observed in patients living long after HTX.
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Affiliation(s)
- Danuta Makowiec
- Institute of Theoretical Physics and Astrophysics, University of Gdańsk, Gdansk, Poland
| | - Joanna Wdowczyk
- 1st Department of Cardiology, Medical University of Gdańsk, Gdansk, Poland
| | - Zbigniew R Struzik
- RIKEN Advanced Center for Computing and Communication, Wako, Japan.,Graduate School of Education, University of Tokyo, Tokyo, Japan
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MAGPIE: Simplifying access and execution of computational models in the life sciences. PLoS Comput Biol 2017; 13:e1005898. [PMID: 29244826 PMCID: PMC5747461 DOI: 10.1371/journal.pcbi.1005898] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 12/29/2017] [Accepted: 11/27/2017] [Indexed: 11/19/2022] Open
Abstract
Over the past decades, quantitative methods linking theory and observation became increasingly important in many areas of life science. Subsequently, a large number of mathematical and computational models has been developed. The BioModels database alone lists more than 140,000 Systems Biology Markup Language (SBML) models. However, while the exchange within specific model classes has been supported by standardisation and database efforts, the generic application and especially the re-use of models is still limited by practical issues such as easy and straight forward model execution. MAGPIE, a Modeling and Analysis Generic Platform with Integrated Evaluation, closes this gap by providing a software platform for both, publishing and executing computational models without restrictions on the programming language, thereby combining a maximum on flexibility for programmers with easy handling for non-technical users. MAGPIE goes beyond classical SBML platforms by including all models, independent of the underlying programming language, ranging from simple script models to complex data integration and computations. We demonstrate the versatility of MAGPIE using four prototypic example cases. We also outline the potential of MAGPIE to improve transparency and reproducibility of computational models in life sciences. A demo server is available at magpie.imb.medizin.tu-dresden.de.
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Abstract
Modern laboratory techniques allow studying NMDA receptors (NMDAR) either anatomically with specific antibodies coupled to sophisticated confocal microscopy, or physiologically by live imaging or electrophysiological techniques. However, NMDARs are not fixed in time and space and changes in their composition and/or distribution on the post-synaptic membrane may significantly impact the synaptic strength and overall function. The computational modeling approach therefore constitutes a complementary tool for investigating the properties of biological systems based on the knowledge provided by the lab experiments.Here, we describe a general computational method aiming at developing kinetic Markov-chain based models of NMDARs subtypes capable of reproducing various experimental results. These models are then used to make predictions on additional (non-obvious) properties and on their role in synaptic function under various physiological and pharmacological conditions. For the purpose of this book chapter, we will focus on the method used to develop a NMDAR model that includes pharmacological site of action of different compounds. Notably, this elementary model can subsequently be included in a neuron model (not described in detail here) to explore the impact of their differential distribution on synaptic functions.
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13
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McDougal RA, Bulanova AS, Lytton WW. Reproducibility in Computational Neuroscience Models and Simulations. IEEE Trans Biomed Eng 2016; 63:2021-35. [PMID: 27046845 PMCID: PMC5016202 DOI: 10.1109/tbme.2016.2539602] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. METHODS Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. RESULTS Building on these standard practices, model-sharing sites and tools have been developed that fit into several categories: 1) standardized neural simulators; 2) shared computational resources; 3) declarative model descriptors, ontologies, and standardized annotations; and 4) model-sharing repositories and sharing standards. CONCLUSION A number of complementary innovations have been proposed to enhance sharing, transparency, and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation, and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. SIGNIFICANCE Model management will become increasingly important as multiscale models become larger, more detailed, and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.
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Kolczyk K, Conradi C. Challenges in horizontal model integration. BMC SYSTEMS BIOLOGY 2016; 10:28. [PMID: 26968798 PMCID: PMC4788958 DOI: 10.1186/s12918-016-0266-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 02/09/2016] [Indexed: 11/30/2022]
Abstract
Background Systems Biology has motivated dynamic models of important intracellular processes at the pathway level, for example, in signal transduction and cell cycle control. To answer important biomedical questions, however, one has to go beyond the study of isolated pathways towards the joint study of interacting signaling pathways or the joint study of signal transduction and cell cycle control. Thereby the reuse of established models is preferable, as it will generally reduce the modeling effort and increase the acceptance of the combined model in the field. Results Obtaining a combined model can be challenging, especially if the submodels are large and/or come from different working groups (as is generally the case, when models stored in established repositories are used). To support this task, we describe a semi-automatic workflow based on established software tools. In particular, two frequent challenges are described: identification of the overlap and subsequent (re)parameterization of the integrated model. Conclusions The reparameterization step is crucial, if the goal is to obtain a model that can reproduce the data explained by the individual models. For demonstration purposes we apply our workflow to integrate two signaling pathways (EGF and NGF) from the BioModels Database. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0266-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Katrin Kolczyk
- Max-Planck-Institute Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106, Magdeburg, Germany
| | - Carsten Conradi
- Max-Planck-Institute Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106, Magdeburg, Germany.
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15
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Walker MA, Madduri R, Rodriguez A, Greenstein JL, Winslow RL. Models and Simulations as a Service: Exploring the Use of Galaxy for Delivering Computational Models. Biophys J 2016; 110:1038-43. [PMID: 26958881 PMCID: PMC4788737 DOI: 10.1016/j.bpj.2015.12.041] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 12/02/2015] [Accepted: 12/21/2015] [Indexed: 11/29/2022] Open
Abstract
We describe the ways in which Galaxy, a web-based reproducible research platform, can be used for web-based sharing of complex computational models. Galaxy allows users to seamlessly customize and run simulations on cloud computing resources, a concept we refer to as Models and Simulations as a Service (MaSS). To illustrate this application of Galaxy, we have developed a tool suite for simulating a high spatial-resolution model of the cardiac Ca(2+) spark that requires supercomputing resources for execution. We also present tools for simulating models encoded in the SBML and CellML model description languages, thus demonstrating how Galaxy's reproducible research features can be leveraged by existing technologies. Finally, we demonstrate how the Galaxy workflow editor can be used to compose integrative models from constituent submodules. This work represents an important novel approach, to our knowledge, to making computational simulations more accessible to the broader scientific community.
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Affiliation(s)
- Mark A Walker
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Ravi Madduri
- Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, Illinois
| | - Alex Rodriguez
- Computation Institute, University of Chicago, Argonne National Laboratory, Chicago, Illinois
| | - Joseph L Greenstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Raimond L Winslow
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
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16
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Taking Aim at Moving Targets in Computational Cell Migration. Trends Cell Biol 2016; 26:88-110. [DOI: 10.1016/j.tcb.2015.09.003] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 08/31/2015] [Accepted: 09/03/2015] [Indexed: 01/07/2023]
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17
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Olivier BG, Swat MJ, Moné MJ. Modeling and Simulation Tools: From Systems Biology to Systems Medicine. Methods Mol Biol 2016; 1386:441-63. [PMID: 26677194 DOI: 10.1007/978-1-4939-3283-2_19] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Modeling is an integral component of modern biology. In this chapter we look into the role of the model, as it pertains to Systems Medicine, and the software that is required to instantiate and run it. We do this by comparing the development, implementation, and characteristics of tools that have been developed to work with two divergent methodologies: Systems Biology and Pharmacometrics. From the Systems Biology perspective we consider the concept of "Software as a Medical Device" and what this may imply for the migration of research-oriented, simulation software into the domain of human health.In our second perspective, we see how in practice hundreds of computational tools already accompany drug discovery and development at every stage of the process. Standardized exchange formats are required to streamline the model exchange between tools, which would minimize translation errors and reduce the required time. With the emergence, almost 15 years ago, of the SBML standard, a large part of the domain of interest is already covered and models can be shared and passed from software to software without recoding them. Until recently the last stage of the process, the pharmacometric analysis used in clinical studies carried out on subject populations, lacked such an exchange medium. We describe a new emerging exchange format in Pharmacometrics which covers the non-linear mixed effects models, the standard statistical model type used in this area. By interfacing these two formats the entire domain can be covered by complementary standards and subsequently the according tools.
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Affiliation(s)
- Brett G Olivier
- Systems Bioinformatics, VU University Amsterdam, Amsterdam, The Netherlands.
| | - Maciej J Swat
- EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Martijn J Moné
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.,Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands
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Myokit: A simple interface to cardiac cellular electrophysiology. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2015; 120:100-14. [PMID: 26721671 DOI: 10.1016/j.pbiomolbio.2015.12.008] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Revised: 11/07/2015] [Accepted: 12/16/2015] [Indexed: 11/24/2022]
Abstract
Myokit is a new powerful and versatile software tool for modeling and simulation of cardiac cellular electrophysiology. Myokit consists of an easy-to-read modeling language, a graphical user interface, single and multi-cell simulation engines and a library of advanced analysis tools accessible through a Python interface. Models can be loaded from Myokit's native file format or imported from CellML. Model export is provided to C, MATLAB, CellML, CUDA and OpenCL. Patch-clamp data can be imported and used to estimate model parameters. In this paper, we review existing tools to simulate the cardiac cellular action potential to find that current tools do not cater specifically to model development and that there is a gap between easy-to-use but limited software and powerful tools that require strong programming skills from their users. We then describe Myokit's capabilities, focusing on its model description language, simulation engines and import/export facilities in detail. Using three examples, we show how Myokit can be used for clinically relevant investigations, multi-model testing and parameter estimation in Markov models, all with minimal programming effort from the user. This way, Myokit bridges a gap between performance, versatility and user-friendliness.
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Sadowski MI, Grant C, Fell TS. Harnessing QbD, Programming Languages, and Automation for Reproducible Biology. Trends Biotechnol 2015; 34:214-227. [PMID: 26708960 DOI: 10.1016/j.tibtech.2015.11.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 11/16/2015] [Accepted: 11/19/2015] [Indexed: 12/18/2022]
Abstract
Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology.
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Affiliation(s)
- Michael I Sadowski
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Chris Grant
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Tim S Fell
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK.
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20
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Rodriguez N, Thomas A, Watanabe L, Vazirabad IY, Kofia V, Gómez HF, Mittag F, Matthes J, Rudolph J, Wrzodek F, Netz E, Diamantikos A, Eichner J, Keller R, Wrzodek C, Fröhlich S, Lewis NE, Myers CJ, Le Novère N, Palsson BØ, Hucka M, Dräger A. JSBML 1.0: providing a smorgasbord of options to encode systems biology models. Bioinformatics 2015; 31:3383-6. [PMID: 26079347 PMCID: PMC4595895 DOI: 10.1093/bioinformatics/btv341] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 05/29/2015] [Indexed: 11/16/2022] Open
Abstract
Summary: JSBML, the official pure Java programming library for the Systems Biology Markup Language (SBML) format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active open-source project with contributions from a growing, international team of developers who not only maintain compatibility with SBML, but also drive steady improvements to the Java interface and promote ease-of-use with end users. Availability and implementation: Source code, binaries and documentation for JSBML can be freely obtained under the terms of the LGPL 2.1 from the website http://sbml.org/Software/JSBML. More information about JSBML can be found in the user guide at http://sbml.org/Software/JSBML/docs/. Contact:jsbml-development@googlegroups.com or andraeger@eng.ucsd.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nicolas Rodriguez
- European Bioinformatics Institute (EBI), Hinxton, UK, Babraham Institute, Babraham Research Campus, Cambridge, UK
| | - Alex Thomas
- University of California, San Diego, La Jolla, CA, USA, Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA
| | | | | | | | | | - Florian Mittag
- European Bioinformatics Institute (EBI), Hinxton, UK, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Jakob Matthes
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Jan Rudolph
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany, Tel Aviv University, Tel Aviv, Israel
| | - Finja Wrzodek
- European Bioinformatics Institute (EBI), Hinxton, UK, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Eugen Netz
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Alexander Diamantikos
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Johannes Eichner
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Roland Keller
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Clemens Wrzodek
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany, Roche Pharmaceutical Research and Early Development, pRED Informatics, Roche Innovation Center, Penzberg, Germany
| | - Sebastian Fröhlich
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany, Martin Luther University of Halle-Wittenberg, Halle (Saale), Germany and
| | - Nathan E Lewis
- University of California, San Diego, La Jolla, CA, USA, Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA
| | | | - Nicolas Le Novère
- European Bioinformatics Institute (EBI), Hinxton, UK, Babraham Institute, Babraham Research Campus, Cambridge, UK
| | - Bernhard Ø Palsson
- University of California, San Diego, La Jolla, CA, USA, Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA
| | - Michael Hucka
- The California Institute of Technology, Pasadena, CA, USA
| | - Andreas Dräger
- University of California, San Diego, La Jolla, CA, USA, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
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Abstract
BACKGROUND Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. MOTIVATION This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. RESULTS For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline.
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Affiliation(s)
- Giulio Caravagna
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, I-20126 Milan, Italy
| | - Luca De Sano
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, I-20126 Milan, Italy
| | - Marco Antoniotti
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, I-20126 Milan, Italy
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Garny A, Hunter PJ. OpenCOR: a modular and interoperable approach to computational biology. Front Physiol 2015; 6:26. [PMID: 25705192 PMCID: PMC4319394 DOI: 10.3389/fphys.2015.00026] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Accepted: 01/16/2015] [Indexed: 11/26/2022] Open
Abstract
Computational biologists have been developing standards and formats for nearly two decades, with the aim of easing the description and exchange of experimental data, mathematical models, simulation experiments, etc. One of those efforts is CellML (cellml.org), an XML-based markup language for the encoding of mathematical models. Early CellML-based environments include COR and OpenCell. However, both of those tools have limitations and were eventually replaced with OpenCOR (opencor.ws). OpenCOR is an open source modeling environment that is supported on Windows, Linux and OS X. It relies on a modular approach, which means that all of its features come in the form of plugins. Those plugins can be used to organize, edit, simulate and analyze models encoded in the CellML format. We start with an introduction to CellML and two of its early adopters, which limitations eventually led to the development of OpenCOR. We then go onto describing the general philosophy behind OpenCOR, as well as describing its openness and its development process. Next, we illustrate various aspects of OpenCOR, such as its user interface and some of the plugins that come bundled with it (e.g., its editing and simulation plugins). Finally, we discuss some of the advantages and limitations of OpenCOR before drawing some concluding remarks.
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Affiliation(s)
- Alan Garny
- Auckland Bioengineering Institute, The University of AucklandAuckland, New Zealand
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Nickerson DP, Ladd D, Hussan JR, Safaei S, Suresh V, Hunter PJ, Bradley CP. Using CellML with OpenCMISS to Simulate Multi-Scale Physiology. Front Bioeng Biotechnol 2015; 2:79. [PMID: 25601911 PMCID: PMC4283644 DOI: 10.3389/fbioe.2014.00079] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 12/11/2014] [Indexed: 11/13/2022] Open
Abstract
OpenCMISS is an open-source modeling environment aimed, in particular, at the solution of bioengineering problems. OpenCMISS consists of two main parts: a computational library (OpenCMISS-Iron) and a field manipulation and visualization library (OpenCMISS-Zinc). OpenCMISS is designed for the solution of coupled multi-scale, multi-physics problems in a general-purpose parallel environment. CellML is an XML format designed to encode biophysically based systems of ordinary differential equations and both linear and non-linear algebraic equations. A primary design goal of CellML is to allow mathematical models to be encoded in a modular and reusable format to aid reproducibility and interoperability of modeling studies. In OpenCMISS, we make use of CellML models to enable users to configure various aspects of their multi-scale physiological models. This avoids the need for users to be familiar with the OpenCMISS internal code in order to perform customized computational experiments. Examples of this are: cellular electrophysiology models embedded in tissue electrical propagation models; material constitutive relationships for mechanical growth and deformation simulations; time-varying boundary conditions for various problem domains; and fluid constitutive relationships and lumped-parameter models. In this paper, we provide implementation details describing how CellML models are integrated into multi-scale physiological models in OpenCMISS. The external interface OpenCMISS presents to users is also described, including specific examples exemplifying the extensibility and usability these tools provide the physiological modeling and simulation community. We conclude with some thoughts on future extension of OpenCMISS to make use of other community developed information standards, such as FieldML, SED-ML, and BioSignalML. Plans for the integration of accelerator code (graphical processing unit and field programmable gate array) generated from CellML models is also discussed.
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Affiliation(s)
- David P. Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - David Ladd
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Jagir R. Hussan
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Vinod Suresh
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Peter J. Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Dräger A, Palsson BØ. Improving collaboration by standardization efforts in systems biology. Front Bioeng Biotechnol 2014; 2:61. [PMID: 25538939 PMCID: PMC4259112 DOI: 10.3389/fbioe.2014.00061] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 11/14/2014] [Indexed: 11/17/2022] Open
Abstract
Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments. The spectrum now even covers the interaction of entire neurons in the brain, three-dimensional motions, and the description of pharmacometric studies. Thereby, the mathematical description of systems and approaches for their (repeated) simulation are clearly separated from each other and also from their graphical representation. Minimum information definitions constitute guidelines and common operation protocols in order to ensure reproducibility of findings and a unified knowledge representation. Central database infrastructures have been established that provide the scientific community with persistent links from model annotations to online resources. A rich variety of open-source software tools thrives for all data formats, often supporting a multitude of programing languages. Regular meetings and workshops of developers and users lead to continuous improvement and ongoing development of these standardization efforts. This article gives a brief overview about the current state of the growing number of operation protocols, mark-up languages, graphical descriptions, and fundamental software support with relevance to systems biology.
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Affiliation(s)
- Andreas Dräger
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Cognitive Systems, Center for Bioinformatics Tübingen (ZBIT), Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Bernhard Ø. Palsson
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
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Pathmanathan P, Gray RA. Verification of computational models of cardiac electro-physiology. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2014; 30:525-544. [PMID: 24259465 DOI: 10.1002/cnm.2615] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2013] [Revised: 07/23/2013] [Accepted: 10/20/2013] [Indexed: 06/02/2023]
Abstract
For computational models of cardiac activity to be used in safety-critical clinical decision-making, thorough and rigorous testing of the accuracy of predictions is required. The field of 'verification, validation and uncertainty quantification' has been developed to evaluate the credibility of computational predictions. The first stage, verification, is the evaluation of how well computational software correctly solves the underlying mathematical equations. The aim of this paper is to introduce novel methods for verifying multi-cellular electro-physiological solvers, a crucial first stage for solvers to be used with confidence in clinical applications. We define 1D-3D model problems with exact solutions for each of the monodomain, bidomain, and bidomain-with-perfusing-bath formulations of cardiac electro-physiology, which allow for the first time the testing of cardiac solvers against exact errors on fully coupled problems in all dimensions. These problems are carefully constructed so that they can be easily run using a general solver and can be used to greatly increase confidence that an implementation is correct, which we illustrate by testing one major solver, 'Chaste', on the problems. We then perform case studies on calculation verification (also known as solution verification) for two specific applications. We conclude by making several recommendations regarding verification in cardiac modelling.
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Affiliation(s)
- Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA; Computational Biology Group, Oxford University, UK
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Abstract
In this chapter, we discuss in detail two essential methods used to evaluate the interaction of Myc with another protein of interest: co-immunoprecipitation (Co-IP) and in vitro pull-down assays. Co-IP is a method that, by immunoaffinity, allows the identification of protein-protein interactions within cells. We provide methods to conduct Co-IPs from whole-cell extracts as well as cytoplasmic and nuclear-enriched fractions. By contrast, the pull-down assay evaluates whether a bait protein that is bound to a solid support can specifically interact with a prey protein that is in solution. We provide methods to conduct in vitro pull-downs and further detail how to use this assay to distinguish whether a protein-protein interaction is direct or indirect. We also discuss methods used to screen for Myc interactors and provide an in silico strategy to help prioritize hits for further validation using the described Co-IP and in vitro pull-down assays.
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Otasek D, Pastrello C, Holzinger A, Jurisica I. Visual Data Mining: Effective Exploration of the Biological Universe. INTERACTIVE KNOWLEDGE DISCOVERY AND DATA MINING IN BIOMEDICAL INFORMATICS 2014. [DOI: 10.1007/978-3-662-43968-5_2] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Smith LP, Butterworth E, Bassingthwaighte JB, Sauro HM. SBML and CellML translation in antimony and JSim. Bioinformatics 2013; 30:903-7. [PMID: 24215024 DOI: 10.1093/bioinformatics/btt641] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The creation and exchange of biologically relevant models is of great interest to many researchers. When multiple standards are in use, models are more readily used and re-used if there exist robust translators between the various accepted formats. SUMMARY Antimony 2.4 and JSim 2.10 provide translation capabilities from their own formats to SBML and CellML. All provided unique challenges, stemming from differences in each format's inherent design, in addition to differences in functionality. AVAILABILITY AND IMPLEMENTATION Both programs are available under BSD licenses; Antimony from http://antimony.sourceforge.net/and JSim from http://physiome.org/jsim/. CONTACT lpsmith@u.washington.edu.
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Affiliation(s)
- Lucian P Smith
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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Kretschmer J, Haunsberger T, Drost E, Koch E, Möller K. Simulating physiological interactions in a hybrid system of mathematical models. J Clin Monit Comput 2013; 28:513-23. [PMID: 23990270 DOI: 10.1007/s10877-013-9502-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Accepted: 08/13/2013] [Indexed: 11/29/2022]
Abstract
Mathematical models can be deployed to simulate physiological processes of the human organism. Exploiting these simulations, reactions of a patient to changes in the therapy regime can be predicted. Based on these predictions, medical decision support systems (MDSS) can help in optimizing medical therapy. An MDSS designed to support mechanical ventilation in critically ill patients should not only consider respiratory mechanics but should also consider other systems of the human organism such as gas exchange or blood circulation. A specially designed framework allows combining three model families (respiratory mechanics, cardiovascular dynamics and gas exchange) to predict the outcome of a therapy setting. Elements of the three model families are dynamically combined to form a complex model system with interacting submodels. Tests revealed that complex model combinations are not computationally feasible. In most patients, cardiovascular physiology could be simulated by simplified models decreasing computational costs. Thus, a simplified cardiovascular model that is able to reproduce basic physiological behavior is introduced. This model purely consists of difference equations and does not require special algorithms to be solved numerically. The model is based on a beat-to-beat model which has been extended to react to intrathoracic pressure levels that are present during mechanical ventilation. The introduced reaction to intrathoracic pressure levels as found during mechanical ventilation has been tuned to mimic the behavior of a complex 19-compartment model. Tests revealed that the model is able to represent general system behavior comparable to the 19-compartment model closely. Blood pressures were calculated with a maximum deviation of 1.8 % in systolic pressure and 3.5 % in diastolic pressure, leading to a simulation error of 0.3 % in cardiac output. The gas exchange submodel being reactive to changes in cardiac output showed a resulting deviation of less than 0.1 %. Therefore, the proposed model is usable in combinations where cardiovascular simulation does not have to be detailed. Computing costs have been decreased dramatically by a factor 186 compared to a model combination employing the 19-compartment model.
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Affiliation(s)
- Jörn Kretschmer
- Institute of Technical Medicine, Furtwangen University, Jakob-Kienzle-Str. 17, 78054, Villingen-Schwenningen, Germany,
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Britten RD, Christie GR, Little C, Miller AK, Bradley C, Wu A, Yu T, Hunter P, Nielsen P. FieldML, a proposed open standard for the Physiome project for mathematical model representation. Med Biol Eng Comput 2013; 51:1191-207. [PMID: 23900627 PMCID: PMC3825639 DOI: 10.1007/s11517-013-1097-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2012] [Accepted: 07/02/2013] [Indexed: 11/28/2022]
Abstract
The FieldML project has made significant progress towards the goal of addressing the need to have open standards and open source software for representing finite element method (FEM) models and, more generally, multivariate field models, such as many of the models that are core to the euHeart project and the Physiome project. FieldML version 0.5 is the most recently released format from the FieldML project. It is an XML format that already has sufficient capability to represent the majority of euHeart’s explicit models such as the anatomical FEM models and simulation solution fields. The details of FieldML version 0.5 are presented, as well as its limitations and some discussion of the progress being made to address these limitations.
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31
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Keller R, Dörr A, Tabira A, Funahashi A, Ziller MJ, Adams R, Rodriguez N, Novère NL, Hiroi N, Planatscher H, Zell A, Dräger A. The systems biology simulation core algorithm. BMC SYSTEMS BIOLOGY 2013; 7:55. [PMID: 23826941 PMCID: PMC3707837 DOI: 10.1186/1752-0509-7-55] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2012] [Accepted: 06/18/2013] [Indexed: 11/30/2022]
Abstract
BACKGROUND With the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases. RESULTS This article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database. CONCLUSIONS The formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list simulation-core-development@lists.sourceforge.net.
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Affiliation(s)
- Roland Keller
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Alexander Dörr
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Akito Tabira
- Graduate School of Science and Technology, Keio University, Yokohama, Japan
| | - Akira Funahashi
- Graduate School of Science and Technology, Keio University, Yokohama, Japan
| | - Michael J Ziller
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Richard Adams
- SynthSys Edinburgh, CH Waddington Building, University of Edinburgh, Edinburgh EH9 3JD, UK
| | - Nicolas Rodriguez
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Noriko Hiroi
- Graduate School of Science and Technology, Keio University, Yokohama, Japan
| | - Hannes Planatscher
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
- Present address: Natural and Medical Sciences Institute at the University of Tuebingen Reutlingen, Germany
| | - Andreas Zell
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
| | - Andreas Dräger
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
- Present address: University of California, San Diego, 417 Powell-Focht Bioengineering Hall 9500, Gilman Drive, La Jolla, CA 92093-0412, USA
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Francis F, García MR, Middleton RH. A single compartment model of pacemaking in dissasociated substantia nigra neurons: stability and energy analysis. J Comput Neurosci 2013; 35:295-316. [PMID: 23686304 DOI: 10.1007/s10827-013-0453-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Revised: 04/01/2013] [Accepted: 04/03/2013] [Indexed: 01/08/2023]
Abstract
Spontaneous oscillations in the mid-brain dopaminergic neurons are an important feature of motor control. The degeneration of these neurons is involved in movement disorders, particularly Parkinson's Disease. Modelling of this activity is an important part of developing an understanding of the pathogenic process. We develop a mathematical paradigm to describe this activity with a single compartment approach and a CellML version is made publicly available. The model explicitly describes the dynamics of the transmembrane potential with changes in the levels of important cations and is consistent with two major observations in the literature regarding its behaviour in the presence of channel blockers. Stability of the model behaviour is determined from the properties of its Monodromy matrix. We also discuss from the perspective of energy, a pharmacological intervention suggested in the treatment of Parkinson's Disease.
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Affiliation(s)
- Febe Francis
- Hamilton Institute, NUI Maynooth, Co. Kildare, Ireland
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De Filippo C, Ramazzotti M, Fontana P, Cavalieri D. Bioinformatic approaches for functional annotation and pathway inference in metagenomics data. Brief Bioinform 2013; 13:696-710. [PMID: 23175748 PMCID: PMC3505041 DOI: 10.1093/bib/bbs070] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Metagenomic approaches are increasingly recognized as a baseline for understanding the
ecology and evolution of microbial ecosystems. The development of methods for pathway
inference from metagenomics data is of paramount importance to link a phenotype to a
cascade of events stemming from a series of connected sets of genes or proteins.
Biochemical and regulatory pathways have until recently been thought and modelled within
one cell type, one organism, one species. This vision is being dramatically changed by the
advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial
populations in fundamental biochemical functions. The new landscape we face requires a
clear picture of the potentialities of existing tools and development of new tools to
characterize, reconstruct and model biochemical and regulatory pathways as the result of
integration of function in complex symbiotic interactions of ontologically and
evolutionary distinct cell types.
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Kretschmer J, Schranz C, Knöbel C, Wingender J, Koch E, Möller K. Efficient computation of interacting model systems. J Biomed Inform 2013; 46:401-9. [PMID: 23395682 DOI: 10.1016/j.jbi.2013.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2012] [Revised: 01/11/2013] [Accepted: 01/17/2013] [Indexed: 11/15/2022]
Abstract
Physiological processes in the human body can be predicted by mathematical models. Medical Decision Support Systems (MDSS) might exploit these predictions when optimizing therapy settings. In critically ill patients depending on mechanical ventilation, these predictions should also consider other organ systems of the human body. In a previously presented framework we combine elements of three model families: respiratory mechanics, cardiovascular dynamics and gas exchange. Computing combinations of moderately complex submodels showed to be computationally costly thus limiting the applicability of those model combinations in an MDSS. A decoupled computing approach was therefore developed, which enables individual evaluation of every submodel. Direct model interaction is not possible in separate calculations. Therefore, interface signals need to be substituted by estimates. These estimates are iteratively improved by increasing model detail in every iteration exploiting the hierarchical structure of the implemented model families. Simulation error converged to a minimum after three iterations. Maximum simulation error showed to be 1.44% compared to the original common coupled computing approach. Simulation error was found to be below measurement noise generally found in clinical data. Simulation time was reduced by factor 34 using one iteration and factor 13 using three iterations. Following the proposed calculation scheme moderately complex model combinations seem to be applicable for model based decision support.
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Affiliation(s)
- J Kretschmer
- Furtwangen University, Institute of Technical Medicine, Jakob-Kienzle-Straße 17, 78054 Villingen-Schwenningen, Germany.
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Waltemath D, Henkel R, Hälke R, Scharm M, Wolkenhauer O. Improving the reuse of computational models through version control. Bioinformatics 2013; 29:742-8. [PMID: 23335018 DOI: 10.1093/bioinformatics/btt018] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Only models that are accessible to researchers can be reused. As computational models evolve over time, a number of different but related versions of a model exist. Consequently, tools are required to manage not only well-curated models but also their associated versions. RESULTS In this work, we discuss conceptual requirements for model version control. Focusing on XML formats such as Systems Biology Markup Language and CellML, we present methods for the identification and explanation of differences and for the justification of changes between model versions. In consequence, researchers can reflect on these changes, which in turn have considerable value for the development of new models. The implementation of model version control will therefore foster the exploration of published models and increase their reusability.
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Affiliation(s)
- Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.
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Pastrello C, Otasek D, Fortney K, Agapito G, Cannataro M, Shirdel E, Jurisica I. Visual data mining of biological networks: one size does not fit all. PLoS Comput Biol 2013; 9:e1002833. [PMID: 23341759 PMCID: PMC3547662 DOI: 10.1371/journal.pcbi.1002833] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.
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Affiliation(s)
- Chiara Pastrello
- CRO Aviano, National Cancer Institute, Aviano, Italy
- Ontario Cancer Institute, the Campbell Family Institute for Cancer Research, and Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - David Otasek
- Ontario Cancer Institute, the Campbell Family Institute for Cancer Research, and Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Kristen Fortney
- Ontario Cancer Institute, the Campbell Family Institute for Cancer Research, and Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Giuseppe Agapito
- Ontario Cancer Institute, the Campbell Family Institute for Cancer Research, and Techna Institute, University Health Network, Toronto, Ontario, Canada
- Department of Medical and Surgical Sciences, University Magna Græcia, Catanzaro, Italy
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, University Magna Græcia, Catanzaro, Italy
| | - Elize Shirdel
- Ontario Cancer Institute, the Campbell Family Institute for Cancer Research, and Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Igor Jurisica
- Ontario Cancer Institute, the Campbell Family Institute for Cancer Research, and Techna Institute, University Health Network, Toronto, Ontario, Canada
- Departments of Computer Science and Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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Nickerson DP, Garny A, Nielsen PMF, Hunter PJ. Standards and tools supporting collaborative development of the virtual physiological human. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:5541-5544. [PMID: 24110992 DOI: 10.1109/embc.2013.6610805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The development of a virtual physiological human has an ambitious goal that requires the participation of a large and diverse community of scientists. To be successful in achieving this goal, members of this community must be able to share their work and easily collaborate on new developments and novel applications of existing work. To aid in this, various standardization projects have evolved as part of the Physiome community, as well as supporting computational tools and infrastructure. We present here an overview of the current state of these standardization efforts and key tools that support the collaborative development, integration, and exchange of computational physiology models under the Physiome umbrella.
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Blätke MA, Dittrich A, Rohr C, Heiner M, Schaper F, Marwan W. JAK/STAT signalling – an executable model assembled from molecule-centred modules demonstrating a module-oriented database concept for systems and synthetic biology. MOLECULAR BIOSYSTEMS 2013; 9:1290-307. [DOI: 10.1039/c3mb25593j] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Abstract
Spurred by recent innovations in genome sequencing, the reconstruction of genome-scale models has increased in recent years. Genome-scale models are now available for a wide range of organisms, and models have been successfully applied to a number of research topics including metabolic engineering, genome annotation, biofuel production, and interpretation of omics data sets. The challenge is how to manage the large amount of data in genome-scale models and perform comparative analysis to gain new biological insights. In this chapter, important standards for genome-scale modeling are outlined. Furthermore, management strategies as well as existing repository and construction tools are discussed. As the comparison of models is an important aspect during the development and analysis stages, available methods are presented and existing software solutions are reviewed.
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Affiliation(s)
- Stephan Pabinger
- Division for Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria.
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Abstract
Modern graphics cards contain hundreds of cores that can be programmed for intensive calculations. They are beginning to be used for spiking neural network simulations. The goal is to make parallel simulation of spiking neural networks available to a large audience, without the requirements of a cluster. We review the ongoing efforts towards this goal, and we outline the main difficulties.
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Affiliation(s)
- Romain Brette
- Laboratoire Psychologie de la Perception, CNRS and Université Paris Descartes, Paris, France.
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du Preez FB, van Niekerk DD, Kooi B, Rohwer JM, Snoep JL. From steady-state to synchronized yeast glycolytic oscillations I: model construction. FEBS J 2012; 279:2810-22. [PMID: 22712534 DOI: 10.1111/j.1742-4658.2012.08665.x] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
UNLABELLED An existing detailed kinetic model for the steady-state behavior of yeast glycolysis was tested for its ability to simulate dynamic behavior. Using a small subset of experimental data, the original model was adapted by adjusting its parameter values in three optimization steps. Only small adaptations to the original model were required for realistic simulation of experimental data for limit-cycle oscillations. The greatest changes were required for parameter values for the phosphofructokinase reaction. The importance of ATP for the oscillatory mechanism and NAD(H) for inter-and intra-cellular communications and synchronization was evident in the optimization steps and simulation experiments. In an accompanying paper [du Preez F et al. (2012) FEBS J279, 2823-2836], we validate the model for a wide variety of experiments on oscillatory yeast cells. The results are important for re-use of detailed kinetic models in modular modeling approaches and for approaches such as that used in the Silicon Cell initiative. DATABASE The mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/dupreez/index.html.
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Affiliation(s)
- Franco B du Preez
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
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Declarative representation of uncertainty in mathematical models. PLoS One 2012; 7:e39721. [PMID: 22802941 PMCID: PMC3389025 DOI: 10.1371/journal.pone.0039721] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2012] [Accepted: 05/25/2012] [Indexed: 11/19/2022] Open
Abstract
An important aspect of multi-scale modelling is the ability to represent mathematical models in forms that can be exchanged between modellers and tools. While the development of languages like CellML and SBML have provided standardised declarative exchange formats for mathematical models, independent of the algorithm to be applied to the model, to date these standards have not provided a clear mechanism for describing parameter uncertainty. Parameter uncertainty is an inherent feature of many real systems. This uncertainty can result from a number of situations, such as: when measurements include inherent error; when parameters have unknown values and so are replaced by a probability distribution by the modeller; when a model is of an individual from a population, and parameters have unknown values for the individual, but the distribution for the population is known. We present and demonstrate an approach by which uncertainty can be described declaratively in CellML models, by utilising the extension mechanisms provided in CellML. Parameter uncertainty can be described declaratively in terms of either a univariate continuous probability density function or multiple realisations of one variable or several (typically non-independent) variables. We additionally present an extension to SED-ML (the Simulation Experiment Description Markup Language) to describe sampling sensitivity analysis simulation experiments. We demonstrate the usability of the approach by encoding a sample model in the uncertainty markup language, and by developing a software implementation of the uncertainty specification (including the SED-ML extension for sampling sensitivty analyses) in an existing CellML software library, the CellML API implementation. We used the software implementation to run sampling sensitivity analyses over the model to demonstrate that it is possible to run useful simulations on models with uncertainty encoded in this form.
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Waltemath D, Adams R, Bergmann FT, Hucka M, Kolpakov F, Miller AK, Moraru II, Nickerson D, Sahle S, Snoep JL, Le Novère N. Reproducible computational biology experiments with SED-ML--the Simulation Experiment Description Markup Language. BMC SYSTEMS BIOLOGY 2011; 5:198. [PMID: 22172142 PMCID: PMC3292844 DOI: 10.1186/1752-0509-5-198] [Citation(s) in RCA: 139] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2011] [Accepted: 12/15/2011] [Indexed: 11/17/2022]
Abstract
BACKGROUND The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. RESULTS In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. CONCLUSIONS With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined.
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Affiliation(s)
- Dagmar Waltemath
- Department of Systems Biology & Bioinformatics, Institute of Computer Science, University of Rostock, D-18051 Rostock, Germany
| | - Richard Adams
- Centre for Systems Biology Edinburgh, CHWaddington Building, University of Edinburgh, Edinburgh EH9 3JD, UK
| | - Frank T Bergmann
- California Institute of Technology, 1200 East California Blvd., Pasadena, CA 91125, USA
| | - Michael Hucka
- California Institute of Technology, 1200 East California Blvd., Pasadena, CA 91125, USA
| | - Fedor Kolpakov
- Institute of Systems Biology Ltd., Detskiy proezd 15, Novosibirsk, 630090, Russia
| | - Andrew K Miller
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Ion I Moraru
- Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - David Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Sven Sahle
- BIOQUANT, University of Heidelberg, Im Neuenheimer Feld 267, Heidelberg, Germany
| | - Jacky L Snoep
- Department of Biochemistry, Stellenbosch University, Privatebag X1, Matieland 7602, South Africa
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, the University of Manchester, 131 Princess Street Manchester, M1 7DN, UK
- Molecular Cell Physiology, VU University, Amsterdam, The Netherlands
| | - Nicolas Le Novère
- EBI, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
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Subramaniam S, Fahy E, Gupta S, Sud M, Byrnes RW, Cotter D, Dinasarapu AR, Maurya MR. Bioinformatics and systems biology of the lipidome. Chem Rev 2011; 111:6452-90. [PMID: 21939287 PMCID: PMC3383319 DOI: 10.1021/cr200295k] [Citation(s) in RCA: 123] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Shankar Subramaniam
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
- San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, California, 92093, USA
- Departments of Chemistry and Biochemistry, and Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, California 92093, USA
| | - Eoin Fahy
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Shakti Gupta
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Manish Sud
- San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, California, 92093, USA
| | - Robert W. Byrnes
- San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, California, 92093, USA
| | - Dawn Cotter
- San Diego Supercomputer Center, 9500 Gilman Drive, La Jolla, California, 92093, USA
| | - Ashok Reddy Dinasarapu
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Mano Ram Maurya
- Department of Bioengineering, University of California at San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
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OpenCMISS: a multi-physics & multi-scale computational infrastructure for the VPH/Physiome project. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2011; 107:32-47. [PMID: 21762717 DOI: 10.1016/j.pbiomolbio.2011.06.015] [Citation(s) in RCA: 111] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2011] [Accepted: 06/30/2011] [Indexed: 11/22/2022]
Abstract
The VPH/Physiome Project is developing the model encoding standards CellML (cellml.org) and FieldML (fieldml.org) as well as web-accessible model repositories based on these standards (models.physiome.org). Freely available open source computational modelling software is also being developed to solve the partial differential equations described by the models and to visualise results. The OpenCMISS code (opencmiss.org), described here, has been developed by the authors over the last six years to replace the CMISS code that has supported a number of organ system Physiome projects. OpenCMISS is designed to encompass multiple sets of physical equations and to link subcellular and tissue-level biophysical processes into organ-level processes. In the Heart Physiome project, for example, the large deformation mechanics of the myocardial wall need to be coupled to both ventricular flow and embedded coronary flow, and the reaction-diffusion equations that govern the propagation of electrical waves through myocardial tissue need to be coupled with equations that describe the ion channel currents that flow through the cardiac cell membranes. In this paper we discuss the design principles and distributed memory architecture behind the OpenCMISS code. We also discuss the design of the interfaces that link the sets of physical equations across common boundaries (such as fluid-structure coupling), or between spatial fields over the same domain (such as coupled electromechanics), and the concepts behind CellML and FieldML that are embodied in the OpenCMISS data structures. We show how all of these provide a flexible infrastructure for combining models developed across the VPH/Physiome community.
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Luna A, Karac EI, Sunshine M, Chang L, Nussinov R, Aladjem MI, Kohn KW. A formal MIM specification and tools for the common exchange of MIM diagrams: an XML-Based format, an API, and a validation method. BMC Bioinformatics 2011; 12:167. [PMID: 21586134 PMCID: PMC3118169 DOI: 10.1186/1471-2105-12-167] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2011] [Accepted: 05/17/2011] [Indexed: 01/15/2023] Open
Abstract
Background The Molecular Interaction Map (MIM) notation offers a standard set of symbols and rules on their usage for the depiction of cellular signaling network diagrams. Such diagrams are essential for disseminating biological information in a concise manner. A lack of software tools for the notation restricts wider usage of the notation. Development of software is facilitated by a more detailed specification regarding software requirements than has previously existed for the MIM notation. Results A formal implementation of the MIM notation was developed based on a core set of previously defined glyphs. This implementation provides a detailed specification of the properties of the elements of the MIM notation. Building upon this specification, a machine-readable format is provided as a standardized mechanism for the storage and exchange of MIM diagrams. This new format is accompanied by a Java-based application programming interface to help software developers to integrate MIM support into software projects. A validation mechanism is also provided to determine whether MIM datasets are in accordance with syntax rules provided by the new specification. Conclusions The work presented here provides key foundational components to promote software development for the MIM notation. These components will speed up the development of interoperable tools supporting the MIM notation and will aid in the translation of data stored in MIM diagrams to other standardized formats. Several projects utilizing this implementation of the notation are outlined herein. The MIM specification is available as an additional file to this publication. Source code, libraries, documentation, and examples are available at http://discover.nci.nih.gov/mim.
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Affiliation(s)
- Augustin Luna
- Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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Miller AK, Yu T, Britten R, Cooling MT, Lawson J, Cowan D, Garny A, Halstead MDB, Hunter PJ, Nickerson DP, Nunns G, Wimalaratne SM, Nielsen PMF. Revision history aware repositories of computational models of biological systems. BMC Bioinformatics 2011; 12:22. [PMID: 21235804 PMCID: PMC3033326 DOI: 10.1186/1471-2105-12-22] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2010] [Accepted: 01/14/2011] [Indexed: 11/10/2022] Open
Abstract
Background Building repositories of computational models of biological systems ensures that published models are available for both education and further research, and can provide a source of smaller, previously verified models to integrate into a larger model. One problem with earlier repositories has been the limitations in facilities to record the revision history of models. Often, these facilities are limited to a linear series of versions which were deposited in the repository. This is problematic for several reasons. Firstly, there are many instances in the history of biological systems modelling where an 'ancestral' model is modified by different groups to create many different models. With a linear series of versions, if the changes made to one model are merged into another model, the merge appears as a single item in the history. This hides useful revision history information, and also makes further merges much more difficult, as there is no record of which changes have or have not already been merged. In addition, a long series of individual changes made outside of the repository are also all merged into a single revision when they are put back into the repository, making it difficult to separate out individual changes. Furthermore, many earlier repositories only retain the revision history of individual files, rather than of a group of files. This is an important limitation to overcome, because some types of models, such as CellML 1.1 models, can be developed as a collection of modules, each in a separate file. The need for revision history is widely recognised for computer software, and a lot of work has gone into developing version control systems and distributed version control systems (DVCSs) for tracking the revision history. However, to date, there has been no published research on how DVCSs can be applied to repositories of computational models of biological systems. Results We have extended the Physiome Model Repository software to be fully revision history aware, by building it on top of Mercurial, an existing DVCS. We have demonstrated the utility of this approach, when used in conjunction with the model composition facilities in CellML, to build and understand more complex models. We have also demonstrated the ability of the repository software to present version history to casual users over the web, and to highlight specific versions which are likely to be useful to users. Conclusions Providing facilities for maintaining and using revision history information is an important part of building a useful repository of computational models, as this information is useful both for understanding the source of and justification for parts of a model, and to facilitate automated processes such as merges. The availability of fully revision history aware repositories, and associated tools, will therefore be of significant benefit to the community.
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Affiliation(s)
- Andrew K Miller
- Auckland Bioengineering Institute, The University of Auckland, Private Bag 92019, Auckland, NZ.
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Yu T, Lloyd CM, Nickerson DP, Cooling MT, Miller AK, Garny A, Terkildsen JR, Lawson J, Britten RD, Hunter PJ, Nielsen PMF. The Physiome Model Repository 2. Bioinformatics 2011; 27:743-4. [DOI: 10.1093/bioinformatics/btq723] [Citation(s) in RCA: 129] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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