The Open Physiology workflow: modeling processes over physiology circuitboards of interoperable tissue units

Bono, Bernard de; Safaei, Soroush; Grenon, Pierre; Nickerson, David; Alexander, S. A.; Helvensteijn, Michiel; Kok, Joost N.; Kokash, Natallia; Wu, Alan; Yu, Tingting; Hunter, Peter; Baldock, Richard

doi:10.3389/fphys.2015.00024

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…rsfs.royalsocietypublishing.org Interface Focus 6: 20150099 systems for body fluids, such as blood, lymph, cerebrospinal fluid, urine, bile, chyme/chyle, pulmonary air, tissue fluid and cytosol; (iii) support the representation of population variation in the arborization patterns of the above hydraulic and pneumatic systems; (iv) semantically link locations with communitysupported reference ontologies of biological structure (e.g. [28][29][30], see also [27] for a fuller discussion of key semantic standards) to ensure interoperability with external community resources; and (v) be amenable to automated inferencing that enables the calculation of transfer routes, given a pair of locations from measurements that correlate.…”

Section: Blueprint: Generalized Requirements For Pathophysiology Pathmentioning

confidence: 99%

Requirements for the formal representation of pathophysiology mechanisms by clinicians

et al. 2016

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Knowledge of multiscale mechanisms in pathophysiology is the bedrock of clinical practice. If quantitative methods, predicting patient-specific behaviour of these pathophysiology mechanisms, are to be brought to bear on clinical decision-making, the Human Physiome community and Clinical community must share a common computational blueprint for pathophysiology mechanisms. A number of obstacles stand in the way of this sharing—not least the technical and operational challenges that must be overcome to ensure that (i) the explicit biological meanings of the Physiome's quantitative methods to represent mechanisms are open to articulation, verification and study by clinicians, and that (ii) clinicians are given the tools and training to explicitly express disease manifestations in direct contribution to modelling. To this end, the Physiome and Clinical communities must co-develop a common computational toolkit, based on this blueprint, to bridge the representation of knowledge of pathophysiology mechanisms (a) that is implicitly depicted in electronic health records and the literature, with (b) that found in mathematical models explicitly describing mechanisms. In particular, this paper makes use of a step-wise description of a specific disease mechanism as a means to elicit the requirements of representing pathophysiological meaning explicitly. The computational blueprint developed from these requirements addresses the Clinical community goals to (i) organize and manage healthcare resources in terms of relevant disease-related knowledge of mechanisms and (ii) train the next generation of physicians in the application of quantitative methods relevant to their research and practice.

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Section: Blueprint: Generalized Requirements For Pathophysiology Pathmentioning

confidence: 99%

Requirements for the formal representation of pathophysiology mechanisms by clinicians

et al. 2016

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“…In addition, it is also possible to automate the sending of retrieved models to a numerical simulator so that the modeller can discover not simply what models are available but what quantitative behaviour is available (de Bono et al . ).…”

Section: Introductionmentioning

confidence: 97%

Modular modelling with Physiome standards

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et al. 2016

The Journal of Physiology

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The ability to produce and customise complex computational models has great potential to have a positive impact on human health. As the field develops towards whole-cell models and linking such models in multi-scale frameworks to encompass tissue, organ, or organism levels, reuse of previous modelling efforts will become increasingly necessary. Any modelling group wishing to reuse existing computational models as modules for their own work faces many challenges in the context of construction, storage, retrieval, documentation and analysis of such modules. Physiome standards, frameworks and tools seek to address several of these challenges, especially for models expressed in the modular protocol CellML. Aside from providing a general ability to produce modules, there has been relatively little research work on architectural principles of CellML models that will enable reuse at larger scales. To complement and support the existing tools and frameworks, we develop a set of principles to address this consideration. The principles are illustrated with examples that couple electrophysiology, signalling, metabolism, gene regulation and synthetic biology, together forming an architectural prototype for whole-cell modelling (including human intervention) in CellML. Such models illustrate how testable units of quantitative biophysical simulation can be constructed. Finally, future relationships between modular models so constructed and Physiome frameworks and tools are discussed, with particular reference to how such frameworks and tools can in turn be extended to complement and gain more benefit from the results of applying the principles.

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“…However, this task requires significant, often invasive amounts of data, which are precluded by the time and immediacy of the critical care bedside. Finally, while computational tools are advancing and becoming standardised [ 16 , 31 , 59 – 64 ], the computational intensity and number of variables can preclude direct, immediate use to personalise and guide care, while enhancing the ability to simulate a range of detailed dysfunction. Thus, the physiological scale and/or complexity of physiome models can limit their immediate impact and patient specificity, particularly if the patient-specific condition varies rapidly.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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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The Open Physiology workflow: modeling processes over physiology circuitboards of interoperable tissue units

Cited by 9 publications

References 24 publications

Requirements for the formal representation of pathophysiology mechanisms by clinicians

Requirements for the formal representation of pathophysiology mechanisms by clinicians

Modular modelling with Physiome standards

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

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