VPH-HF: A software framework for the execution of complex subject-specific physiology modelling workflows

Viceconti, Marco; Bnà, Simone; Tartarini, Daniele; Sfakianakis, Stelios; Grogan, James A.; Walker, Dawn; Gamble, Samuel; Testi, Debora

doi:10.1016/j.jocs.2018.02.009

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…If we broaden our research to solid tumours in general, probably the closest work is that done by the CHIC project led by Prof Stamatakos on the modelling of a brain tumour, glioblastoma 19,20 . The project used as orchestration software the precursor of that used in this study, 21 but used no particularisation strategy. Other authors proposed for similar problems single‐scale models coupling the cellular replication simulated with a cellular automata with the diffusion–reaction problem simulated with a Lattice‐Boltzmann scheme 22 .…”

Section: Discussionmentioning

confidence: 99%

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Vinicius

Quintela

Kasztelnik

et al. 2022

Numer Methods Biomed Eng

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In silico, medicine models are frequently used to represent a phenomenon across multiples space-time scales. Most of these multiscale models require impracticable execution times to be solved, even using high performance computing systems, because typically each representative volume element in the upper scale model is coupled to an instance of the lower scale model; this causes a combinatory explosion of the computational cost, which increases exponentially as the number of scales to be modelled increases. To attenuate this problem, it is a common practice to interpose between the two models a particularisation operator, which maps the upper-scale model results into a smaller number of lower-scale models, and an operator, which maps the fewer results of the lower-scale models on the whole space-time homogenisation domain of upper-scale model. The aim of this study is to explore what is the simplest particularisation / homogenisation scheme that can couple a model aimed to predict the growth of a whole solid tumour (neuroblastoma) to a tissue-scale model of the cell-tissue biology with an acceptable approximation error and a viable computational cost. Using an idealised initial dataset with spatial gradients representative of those of real neuroblastomas, but small enough to be solved without any particularisation, we determined the approximation error and the computational cost of a very simple particularisation strategy based on binning. We found that even such simple algorithm can significantly reduce the computational cost with negligible approximation errors.

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Section: Discussionmentioning

confidence: 99%

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Vinicius

Quintela

Kasztelnik

et al. 2022

Numer Methods Biomed Eng

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“…MUSCLE 2 is able to incorporate at-scale model components written in a variety of programming languages including Java, C, C++, Python and Fortran and is able to directly generate runtime configurations using the MML specification of a multiscale model. Among other things it has been embedded in the VPH Hypermodelling Framework [23]. The newest version, MUSCLE 3, aims to more tightly integrate an extended version of the MML, with better support for dynamic submodel instantiation, surrogate modelling and uncertainty quantification and sensitivity analysis.…”

Section: Recent Developments In Multiscale Computing Softwarementioning

confidence: 99%

Mastering the scales: a survey on the benefits of multiscale computing software

Groen

Knap

Neumann

et al. 2019

Phil. Trans. R. Soc. A.

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In the last few decades, multiscale modelling has emerged as one of the dominant modelling paradigms in many areas of science and engineering. Its rise to dominance is primarily driven by advancements in computing power and the need to model systems of increasing complexity. The multiscale modelling paradigm is now accompanied by a vibrant ecosystem of multiscale computing software (MCS) which promises to address many challenges in the development of multiscale applications. In this paper, we define the common steps in the multiscale application development process and investigate to what degree a set of 21 representative MCS tools enhance each development step. We observe several gaps in the features provided by MCS tools, especially for application deployment and the preparation and management of production runs. In addition, we find that many MCS tools are tailored to a particular multiscale computing pattern, even though they are otherwise application agnostic. We conclude that the gaps we identify are characteristic of a field that is still maturing and features that enhance the deployment and production steps of multiscale application development are desirable for the long-term success of MCS in its application fields. This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.

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“…Conversely, a loosely coupled topology is devoid of cycles, and a model is deemed complete once it has transmitted its information [11]. Details on the technological infrastructure that was developed for the execution of the hypermodel can be found in [12].…”

Section: Introductionmentioning

confidence: 99%

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin

Kolokotroni,

Abler,

Ghosh

et al. 2024

JPM

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The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.

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VPH-HF: A software framework for the execution of complex subject-specific physiology modelling workflows

Cited by 3 publications

References 34 publications

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Mastering the scales: a survey on the benefits of multiscale computing software

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin

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