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Guest JD, Kelly-Hedrick M, Williamson T, Park C, Ali DM, Sivaganesan A, Neal CJ, Tator CH, Fehlings MG. Development of a Systems Medicine Approach to Spinal Cord Injury. J Neurotrauma 2023; 40:1849-1877. [PMID: 37335060 PMCID: PMC10460697 DOI: 10.1089/neu.2023.0024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2023] Open
Abstract
Traumatic spinal cord injury (SCI) causes a sudden onset multi-system disease, permanently altering homeostasis with multiple complications. Consequences include aberrant neuronal circuits, multiple organ system dysfunctions, and chronic phenotypes such as neuropathic pain and metabolic syndrome. Reductionist approaches are used to classify SCI patients based on residual neurological function. Still, recovery varies due to interacting variables, including individual biology, comorbidities, complications, therapeutic side effects, and socioeconomic influences for which data integration methods are lacking. Infections, pressure sores, and heterotopic ossification are known recovery modifiers. However, the molecular pathobiology of the disease-modifying factors altering the neurological recovery-chronic syndrome trajectory is mainly unknown, with significant data gaps between intensive early treatment and chronic phases. Changes in organ function such as gut dysbiosis, adrenal dysregulation, fatty liver, muscle loss, and autonomic dysregulation disrupt homeostasis, generating progression-driving allostatic load. Interactions between interdependent systems produce emergent effects, such as resilience, that preclude single mechanism interpretations. Due to many interacting variables in individuals, substantiating the effects of treatments to improve neurological outcomes is difficult. Acute injury outcome predictors, including blood and cerebrospinal fluid biomarkers, neuroimaging signal changes, and autonomic system abnormalities, often do not predict chronic SCI syndrome phenotypes. In systems medicine, network analysis of bioinformatics data is used to derive molecular control modules. To better understand the evolution from acute SCI to chronic SCI multi-system states, we propose a topological phenotype framework integrating bioinformatics, physiological data, and allostatic load tested against accepted established recovery metrics. This form of correlational phenotyping may reveal critical nodal points for intervention to improve recovery trajectories. This study examines the limitations of current classifications of SCI and how these can evolve through systems medicine.
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Affiliation(s)
- James D. Guest
- Neurological Surgery and the Miami Project to Cure Paralysis, University of Miami, Miami, Florida, USA
| | | | - Theresa Williamson
- Massachusetts General Neurosurgery, Harvard University, Boston, Massachusetts, USA
| | - Christine Park
- Department of Neurosurgery, Duke University, Durham, North Carolina, USA
| | - Daniyal Mansoor Ali
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ahilan Sivaganesan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Chris J. Neal
- Division of Neurosurgery, Walter Reed National Military Medical Center, Bethesda, Maryland, USA
| | - Charles H. Tator
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Michael G. Fehlings
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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Babarenda Gamage TP, Elsayed A, Lin C, Wu A, Feng Y, Yu J, Gao L, Wijenayaka S, Nash MP, Doyle AJ, Nickerson DP. Vision for the 12 LABOURS Digital Twin Platform . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083471 DOI: 10.1109/embc40787.2023.10341138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Clinical translation of personalised computational physiology workflows and digital twins can revolutionise healthcare by providing a better understanding of an individual's physiological processes and any changes that could lead to serious health consequences. However, the lack of common infrastructure for developing these workflows and digital twins has hampered the realisation of this vision. The Auckland Bioengineering Institute's 12 LABOURS project aims to address these challenges by developing a Digital Twin Platform to enable researchers to develop and personalise computational physiology models to an individual's health data in clinical workflows. This will allow clinical trials to be more efficiently conducted to demonstrate the efficacy of these personalised clinical workflows. We present a demonstration of the platform's capabilities using publicly available data and an existing automated computational physiology workflow developed to assist clinicians with diagnosing and treating breast cancer. We also demonstrate how the platform facilitates the discovery and exploration of data and the presentation of workflow results as part of clinical reports through a web portal. Future developments will involve integrating the platform with health systems and remote-monitoring devices such as wearables and implantables to support home-based healthcare. Integrating outputs from multiple workflows that are applied to the same individual's health data will also enable the generation of their personalised digital twin.Clinical Relevance- The proposed 12 LABOURS Digital Twin Platform will enable researchers to 1) more efficiently conduct clinical trials to assess the efficacy of their computational physiology workflows and support the clinical translation of their research; 2) reuse primary and derived data from these workflows to generate novel workflows; and 3) generate personalised digital twins by integrating the outputs of different computational physiology workflows.
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Kumar H, Green R, Cornfeld DM, Condron P, Emsden T, Elsayed A, Zhao D, Gilbert K, Nash MP, Clark AR, Tawhai MH, Burrowes K, Murphy R, Tayebi M, McGeown J, Kwon E, Shim V, Wang A, Choisne J, Carman L, Besier T, Handsfield G, Babarenda Gamage TP, Shen J, Maso Talou G, Safaei S, Maller JJ, Taylor D, Potter L, Holdsworth SJ, Wilson GA. Roadmap for an imaging and modelling paediatric study in rural NZ. Front Physiol 2023; 14:1104838. [PMID: 36969588 PMCID: PMC10036853 DOI: 10.3389/fphys.2023.1104838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/30/2023] [Indexed: 03/12/2023] Open
Abstract
Our study methodology is motivated from three disparate needs: one, imaging studies have existed in silo and study organs but not across organ systems; two, there are gaps in our understanding of paediatric structure and function; three, lack of representative data in New Zealand. Our research aims to address these issues in part, through the combination of magnetic resonance imaging, advanced image processing algorithms and computational modelling. Our study demonstrated the need to take an organ-system approach and scan multiple organs on the same child. We have pilot tested an imaging protocol to be minimally disruptive to the children and demonstrated state-of-the-art image processing and personalized computational models using the imaging data. Our imaging protocol spans brain, lungs, heart, muscle, bones, abdominal and vascular systems. Our initial set of results demonstrated child-specific measurements on one dataset. This work is novel and interesting as we have run multiple computational physiology workflows to generate personalized computational models. Our proposed work is the first step towards achieving the integration of imaging and modelling improving our understanding of the human body in paediatric health and disease.
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Affiliation(s)
- Haribalan Kumar
- Mātai Medical Research Institute, Gisborne, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- GE Healthcare (Australia & New Zealand), Auckland, New Zealand
| | - Robby Green
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Daniel M. Cornfeld
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Paul Condron
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Taylor Emsden
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Ayah Elsayed
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Auckland University of Technology, Auckland, New Zealand
| | - Debbie Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Kat Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Martyn P. Nash
- Mātai Medical Research Institute, Gisborne, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Alys R. Clark
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn H. Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Kelly Burrowes
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Maryam Tayebi
- Mātai Medical Research Institute, Gisborne, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Josh McGeown
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Eryn Kwon
- Mātai Medical Research Institute, Gisborne, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Mātai Medical Research Institute, Gisborne, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Julie Choisne
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Laura Carman
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thor Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Geoffrey Handsfield
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Jiantao Shen
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Gonzalo Maso Talou
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Jerome J. Maller
- GE Healthcare (Australia & New Zealand), Auckland, New Zealand
- Monash Alfred Psychiatry Research Centre, Melbourne, VIC, Australia
| | | | - Leigh Potter
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Samantha J. Holdsworth
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, Centre for Brain Research, University of Auckland, Auckland, New Zealand
- *Correspondence: Samantha J. Holdsworth,
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Gawthrop PJ, Pan M. Energy-based advection modelling using bond graphs. J R Soc Interface 2022. [PMCID: PMC9554522 DOI: 10.1098/rsif.2022.0492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Advection, the transport of a substance by the flow of a fluid, is a key process in biological systems. The energy-based bond graph approach to modelling chemical transformation within reaction networks is extended to include transport and thus advection. The approach is illustrated using a simple model of advection via circulating flow and by a simple pharmacokinetic model of anaesthetic gas uptake. This extension provides a physically consistent framework for linking advective flows with the fluxes associated with chemical reactions within the context of physiological systems in general and the human physiome in particular.
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Affiliation(s)
- Peter J. Gawthrop
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria 3010, Australia,School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, Victoria 3010, Australia
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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.
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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
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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.
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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
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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.
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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
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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.
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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
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Sermesant M, Delingette H, Cochet H, Jaïs P, Ayache N. Applications of artificial intelligence in cardiovascular imaging. Nat Rev Cardiol 2021; 18:600-609. [PMID: 33712806 DOI: 10.1038/s41569-021-00527-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome.
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Affiliation(s)
| | | | - Hubert Cochet
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
| | - Pierre Jaïs
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
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Gladding PA, Loader S, Smith K, Zarate E, Green S, Villas-Boas S, Shepherd P, Kakadiya P, Hewitt W, Thorstensen E, Keven C, Coe M, Nakisa B, Vuong T, Rastgoo MN, Jüllig M, Starc V, Schlegel TT. Multiomics, virtual reality and artificial intelligence in heart failure. Future Cardiol 2021; 17:1335-1347. [PMID: 34008412 DOI: 10.2217/fca-2020-0225] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.
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Affiliation(s)
- Patrick A Gladding
- Department of Cardiology, Waitemata District Health Board, Auckland 0620, New Zealand
| | - Suzanne Loader
- Department of Cardiology, Waitemata District Health Board, Auckland 0620, New Zealand
| | - Kevin Smith
- Clinical Laboratory, Waitemata District Health Board, Auckland 0620, New Zealand
| | - Erica Zarate
- School of Biological Science, University of Auckland, Auckland 1010, New Zealand
| | - Saras Green
- School of Biological Science, University of Auckland, Auckland 1010, New Zealand
| | - Silas Villas-Boas
- School of Biological Science, University of Auckland, Auckland 1010, New Zealand
| | - Phillip Shepherd
- Grafton Genomics Ltd, Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Purvi Kakadiya
- Grafton Genomics Ltd, Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Will Hewitt
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
| | - Eric Thorstensen
- Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Christine Keven
- Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Margaret Coe
- Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Bahareh Nakisa
- School of Information Technology, Deakin University, Victoria 3125, Australia
| | - Tan Vuong
- School of Information Technology, Deakin University, Victoria 3125, Australia
| | - Mohammad Naim Rastgoo
- School of Electrical Engineering & Computer Science, Queensland University of Technology, Brisbane, QLD 4072, Australia
| | - Mia Jüllig
- Paper Dog Limited, Waiheke Island, Auckland 1081, New Zealand
| | - Vito Starc
- Faculty of Medicine, University of Ljubljana, Ljubljana 1000, Slovenia
| | - Todd T Schlegel
- Karolinska Institutet, Stockholm, Sweden 171 77, Switzerland.,Nicollier-Schlegel Sàrl, Trélex, Karolinaka 1270, Switzerland
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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.
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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
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12
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Gawthrop PJ, Pan M. Network Thermodynamical Modeling of Bioelectrical Systems: A Bond Graph Approach. Bioelectricity 2021; 3:3-13. [PMID: 34476374 DOI: 10.1089/bioe.2020.0042] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Interactions among biomolecules, electrons, and protons are essential to many fundamental processes sustaining life. It is therefore of interest to build mathematical models of these bioelectrical processes not only to enhance understanding but also to enable computer models to complement in vitro and in vivo experiments. Such models can never be entirely accurate; it is nevertheless important that the models are compatible with physical principles. Network Thermodynamics, as implemented with bond graphs, provide one approach to creating physically compatible mathematical models of bioelectrical systems. This is illustrated using simple models of ion channels, redox reactions, proton pumps, and electrogenic membrane transporters thus demonstrating that the approach can be used to build mathematical and computer models of a wide range of bioelectrical systems.
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Affiliation(s)
- Peter J Gawthrop
- Systems Biology Laboratory, Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia.,Systems Biology Laboratory, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Michael Pan
- Systems Biology Laboratory, Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia.,Systems Biology Laboratory, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
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13
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Machine learning methods to support personalized neuromusculoskeletal modelling. Biomech Model Mechanobiol 2020; 19:1169-1185. [DOI: 10.1007/s10237-020-01367-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 07/08/2020] [Indexed: 12/19/2022]
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14
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Physically-plausible modelling of biomolecular systems: A simplified, energy-based model of the mitochondrial electron transport chain. J Theor Biol 2020; 493:110223. [PMID: 32119969 DOI: 10.1016/j.jtbi.2020.110223] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 01/21/2020] [Accepted: 02/27/2020] [Indexed: 11/20/2022]
Abstract
Advances in systems biology and whole-cell modelling demand increasingly comprehensive mathematical models of cellular biochemistry. Such models require the development of simplified representations of specific processes which capture essential biophysical features but without unnecessarily complexity. Recently there has been renewed interest in thermodynamically-based modelling of cellular processes. Here we present an approach to developing of simplified yet thermodynamically consistent (hence physically plausible) models which can readily be incorporated into large scale biochemical descriptions but which do not require full mechanistic detail of the underlying processes. We illustrate the approach through development of a simplified, physically plausible model of the mitochondrial electron transport chain and show that the simplified model behaves like the full system.
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15
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Burrowes KS, Iravani A, Kang W. Integrated lung tissue mechanics one piece at a time: Computational modeling across the scales of biology. Clin Biomech (Bristol, Avon) 2019; 66:20-31. [PMID: 29352607 DOI: 10.1016/j.clinbiomech.2018.01.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 12/05/2017] [Accepted: 01/09/2018] [Indexed: 02/07/2023]
Abstract
The lung is a delicately balanced and highly integrated mechanical system. Lung tissue is continuously exposed to the environment via the air we breathe, making it susceptible to damage. As a consequence, respiratory diseases present a huge burden on society and their prevalence continues to rise. Emergent function is produced not only by the sum of the function of its individual components but also by the complex feedback and interactions occurring across the biological scales - from genes to proteins, cells, tissue and whole organ - and back again. Computational modeling provides the necessary framework for pulling apart and putting back together the pieces of the body and organ systems so that we can fully understand how they function in both health and disease. In this review, we discuss models of lung tissue mechanics spanning from the protein level (the extracellular matrix) through to the level of cells, tissue and whole organ, many of which have been developed in isolation. This is a vital step in the process but to understand the emergent behavior of the lung, we must work towards integrating these component parts and accounting for feedback across the scales, such as mechanotransduction. These interactions will be key to unlocking the mechanisms occurring in disease and in seeking new pharmacological targets and improving personalized healthcare.
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Affiliation(s)
- Kelly S Burrowes
- Department of Chemical and Materials Engineering, University of Auckland, 2-6 Park Avenue, Auckland 1023, New Zealand; Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Auckland 1010, New Zealand.
| | - Amin Iravani
- Department of Chemical and Materials Engineering, University of Auckland, 2-6 Park Avenue, Auckland 1023, New Zealand.
| | - Wendy Kang
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Auckland 1010, New Zealand.
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16
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Affiliation(s)
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, California
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17
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Precision gaming for health: Computer games as digital medicine. Methods 2018; 151:28-33. [PMID: 30273711 DOI: 10.1016/j.ymeth.2018.09.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 08/14/2018] [Accepted: 09/27/2018] [Indexed: 11/22/2022] Open
Abstract
Health based games have significant potential as therapeutic interventions due to the inherent mechanisms associated with social and individual game play and their capacity for sensor integration, data capture analysis and patient feedback. Moreover, they are low cost and they can be deployed at the point of care across an evolving digital ecosystem. However, a robust evidence base to support their wider adoption as a clinical intervention for chronic diseases is lacking and significant methodological barriers exist for health games developers creating efficacious 'digital medicines'. Game design is complex and it must utilise validated game mechanics balanced with a creative and engaging game design. The aim of this review is therefore to outline the fundamental steps of game development for health professionals and to critically appraise the methodology for assessing health games as medical interventions. This requires (1) The adoption of clearly defined global language for health games development based on a targeted function as therapeutic agents. (2) The development of multidisciplinary teams with a broad portfolio of development and clinical skill sets. (3) The creation of health game engines specifically built to facilitate clinical game development. (4) Robust trial design and assessment of translational impact: If games are to be prescribed, their efficacy and toxicity must be based on a rigorous assessment of their use within a real world clinical environment. Trials for precision health games have specific challenges around blinding, learning curves, bias and confounding that are particularly problematic. We propose the adoption of the IDEAL-GAMES framework for game development that systematically assess and validates games through open registries. In conclusion we propose a new framework for assessing the robustness and clinical efficacy of games for health as clinical interventions in the clinical environment.
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18
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Neufeld E, Lloyd B, Schneider B, Kainz W, Kuster N. Functionalized Anatomical Models for Computational Life Sciences. Front Physiol 2018; 9:1594. [PMID: 30505279 PMCID: PMC6250781 DOI: 10.3389/fphys.2018.01594] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 10/24/2018] [Indexed: 11/20/2022] Open
Abstract
The advent of detailed computational anatomical models has opened new avenues for computational life sciences (CLS). To date, static models representing the anatomical environment have been used in many applications but are insufficient when the dynamics of the body prevents separation of anatomical geometrical variability from physics and physiology. Obvious examples include the assessment of thermal risks in magnetic resonance imaging and planning for radiofrequency and acoustic cancer treatment, where posture and physiology-related changes in shape (e.g., breathing) or tissue behavior (e.g., thermoregulation) affect the impact. Advanced functionalized anatomical models can overcome these limitations and dramatically broaden the applicability of CLS in basic research, the development of novel devices/therapies, and the assessment of their safety and efficacy. Various forms of functionalization are discussed in this paper: (i) shape parametrization (e.g., heartbeat, population variability), (ii) physical property distributions (e.g., image-based inhomogeneity), (iii) physiological dynamics (e.g., tissue and organ behavior), and (iv) integration of simulation/measurement data (e.g., exposure conditions, “validation evidence” supporting model tuning and validation). Although current model functionalization may only represent a small part of the physiology, it already facilitates the next level of realism by (i) driving consistency among anatomy and different functionalization layers and highlighting dependencies, (ii) enabling third-party use of validated functionalization layers as established simulation tools, and (iii) therefore facilitating their application as building blocks in network or multi-scale computational models. Integration in functionalized anatomical models thus leverages and potentiates the value of sub-models and simulation/measurement data toward ever-increasing simulation realism. In our o2S2PARC platform, we propose to expand the concept of functionalized anatomical models to establish an integration and sharing service for heterogeneous computational models, ranging from the molecular to the organ level. The objective of o2S2PARC is to integrate all models developed within the National Institutes of Health SPARC initiative in a unified anatomical and computational environment, to study the role of the peripheral nervous system in controlling organ physiology. The functionalization concept, as outlined for the o2S2PARC platform, could form the basis for many other application areas of CLS. The relationship to other ongoing initiatives, such as the Physiome Project, is also presented.
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Affiliation(s)
- Esra Neufeld
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | - Bryn Lloyd
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | | | - Wolfgang Kainz
- Division of Biomedical Physics, OSEL, CDRH, Food and Drug Administration, Silver Spring, MD, United States
| | - Niels Kuster
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland.,Swiss Federal Institute of Technology (ETHZ), Zurich, Switzerland
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19
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Dow JA, Pandit A, Davies SA. New views on the Malpighian tubule from post-genomic technologies. CURRENT OPINION IN INSECT SCIENCE 2018; 29:7-11. [PMID: 30551828 DOI: 10.1016/j.cois.2018.05.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 05/18/2018] [Indexed: 06/09/2023]
Abstract
Successful insect diversification depends at least in part on the ability to osmoregulate successfully across a broad range of ecological niches. First described in the 17th Century, and Malpighian tubules have been studied physiologically for 70 years. However, our understanding has been revolutionized by the advent of genomics, transcriptomics, proteomics and metabolomics. Such technologies are natural partners with (though do not obligatorily require) model organisms and transgenic technologies. This review describes the recent impact of multi-omic technologies on our understanding or renal function and control in insects.
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Affiliation(s)
- Julian At Dow
- Institute of Molecular, Cell & Systems Biology, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom.
| | - Aniruddha Pandit
- Institute of Molecular, Cell & Systems Biology, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Shireen A Davies
- Institute of Molecular, Cell & Systems Biology, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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20
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Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, Benyo B, Moeller K, Safaei S, Tawhai M, Hunter P, Desaive T. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.
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Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
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21
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Affiliation(s)
- Can Ince
- Department of Intensive Care, Erasmus MC, University Medical Center Rotterdam, 's-Gravendijkwal 230, 3015 CE, Rotterdam, The Netherlands.
- Department of Translational Physiology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
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22
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Gawthrop PJ, Crampin EJ. Energy-based analysis of biomolecular pathways. Proc Math Phys Eng Sci 2017; 473:20160825. [PMID: 28690404 DOI: 10.1098/rspa.2016.0825] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 05/26/2017] [Indexed: 01/03/2023] Open
Abstract
Decomposition of biomolecular reaction networks into pathways is a powerful approach to the analysis of metabolic and signalling networks. Current approaches based on analysis of the stoichiometric matrix reveal information about steady-state mass flows (reaction rates) through the network. In this work, we show how pathway analysis of biomolecular networks can be extended using an energy-based approach to provide information about energy flows through the network. This energy-based approach is developed using the engineering-inspired bond graph methodology to represent biomolecular reaction networks. The approach is introduced using glycolysis as an exemplar; and is then applied to analyse the efficiency of free energy transduction in a biomolecular cycle model of a transporter protein [sodium-glucose transport protein 1 (SGLT1)]. The overall aim of our work is to present a framework for modelling and analysis of biomolecular reactions and processes which considers energy flows and losses as well as mass transport.
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Affiliation(s)
- Peter J Gawthrop
- Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Victoria 3010, Australia
| | - Edmund J Crampin
- Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Victoria 3010, Australia.,School of Mathematics and Statistics, Melbourne School of Engineering, University of Melbourne, Victoria 3010, Australia.,School of Medicine, Melbourne School of Engineering, University of Melbourne, Victoria 3010, Australia.,ARC Centre of Excellence in Convergent Bio-Nano Science, Melbourne School of Engineering, University of Melbourne, Victoria 3010, Australia
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23
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Gawthrop PJ. Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction. IEEE Trans Nanobioscience 2017; 16:177-188. [PMID: 28252411 DOI: 10.1109/tnb.2017.2674683] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Engineering systems modeling and analysis based on the bond graph approach has been applied to biomolecular systems. In this context, the notion of a Faraday-equivalent chemical potential is introduced which allows chemical potential to be expressed in an analogous manner to electrical volts thus allowing engineering intuition to be applied to biomolecular systems. Redox reactions, and their representation by half-reactions, are key components of biological systems which involve both electrical and chemical domains. A bond graph interpretation of redox reactions is given which combines bond graphs with the Faraday-equivalent chemical potential. This approach is particularly relevant when the biomolecular system implements chemoelectrical transduction - for example chemiosmosis within the key metabolic pathway of mitochondria: oxidative phosphorylation. An alternative way of implementing computational modularity using bond graphs is introduced and used to give a physically based model of the mitochondrial electron transport chain To illustrate the overall approach, this model is analyzed using the Faraday-equivalent chemical potential approach and engineering intuition is used to guide affinity equalisation: a energy based analysis of the mitochondrial electron transport chain.
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