Bond Graph Model of Cerebral Circulation: Toward Clinically Feasible Systemic Blood Flow Simulations

Safaei, Soroush; Blanco, Pablo; Müller, Lucas O.; Hellevik, Leif Rune; Hunter, Peter

doi:10.3389/fphys.2018.00148

Cited by 39 publications

(69 citation statements)

References 24 publications

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“…Particularly the VPH Physiome Project pursues a vision where models and sub-models are reproducible, reusable, and discoverable (Nickerson and Hunter, 2017 ), e.g., by elaborating standardized markup-language descriptions of computational models, data, and measurement protocols. The project aims to describe the Physiome through ordinary and partial differential equations (ODEs and PDEs) that are made compatible, e.g., by enforcing energy and mass fluxes through Bond-graph theory (Paynter, 1961 ; Safaei et al, 2018 ), and by employing standardized naming conventions/ontologies and formats (e.g., CellML, SBML Chaouiya et al, 2013 , FieldML) that are in turn compatible with general purpose solvers such as OpenCOR and OpenCMISS (Hunter, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Functionalized Anatomical Models for Computational Life Sciences

Neufeld

Lloyd

Schneider³

et al. 2018

Front. Physiol.

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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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Section: Discussionmentioning

confidence: 99%

Functionalized Anatomical Models for Computational Life Sciences

Neufeld

Lloyd

Schneider³

et al. 2018

Front. Physiol.

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“…Complex physiome models capture the heart alone, but have also been created for the entire heart and circulation [60,69]. Increasingly, there are simplified systems and bond graph approaches to make these models computationally tractable and lower intensity [85][86][87]. However, these models still suffer from a level of complexity that does not permit ready identification [14], limiting their use in the context reviewed here.…”

Section: Dynamic System Models Of the Cardiovascular Systemmentioning

confidence: 99%

Model-based management of cardiovascular failure: Where medicine and control systems converge

Desaive

Horikawa

Ortiz

et al. 2019

Annual Reviews in Control

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Cardiovascular disease is growing epidemically worldwide, multiplying the impact of increasingly aging populations on intensive care unit (ICU) demand. It is the leading cause of ICU admission, length of stay, mortality and, as a result, cost. Hence, there is a significant need to bring the gains in productivity enabled by automation and control systems technologies, which have arisen in so many sectors, to medicine and this field in particular. This review presents the background to the problem and the main issues arising in care from a control systems technology perspective. It then presents a vision of a more automated future with specific goals in the areas of dynamic systems modeling, system identification and control. These areas are reviewed, followed by specific recommendations for the field, where control systems expertise can be leveraged to best advantage.

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“…The primary paper Safaei et al (2018) aimed to achieve two goals in cardiovascular system modelling: making the model subject-specific and fast enough to be useful in a clinical setting. An existing cardiovascular model, Anatomically Detailed Arterial Network (ADAN) model Blanco et al (2015), utilises a detailed geometry of the cardiovascular system and simulates the blood flow by solving 1D version of the Navier-Stokes equations in compliant domains.…”

Section: Introductionmentioning

confidence: 99%