Computational horizons in cancer (CHIC): Developing meta- and hyper-multiscale models and repositories for in Silico Oncology - A brief technical outline of the project

Stamatakos, Georgios S.; Dionysiou, Dimitra D.; Misichroni, Fay; Graf, Norbert; Gool, Stefaan Van; Bohle, Rainer-Maria; Dong, Feng; Viceconti, Marco; Marias, Kostas; Sakkalis, Vangelis; Forgó, Nikolaus; Radhakrishnan, Ravi; Byrne, Helen M.; Guiot, Caterina; Buechler, P; Neri, Elias; Bucur, Anca; Bono, Bernard de; Testi, Debora; Tsiknakis, Manolis

doi:10.1109/iarwisoci.2014.7034630

Cited by 5 publications

(10 citation statements)

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“…Clinically-driven in silico multi-scale cancer models simulate in vivo tumor growth and response to therapies across biocomplexity scales, within a clinical environment, towards evaluating efficacious treatment combinations. To facilitate the development of multiscale cancer models, several large-scale program projects have been launched (Supplementary Table 12) such as Advancing Clinico-Genomic Trials on Cancer (ACGT) (312), Clinically Oriented Translational Cancer Multilevel Modeling (ContraCancrum) (313), Personalized Medicine (pmedicine) (314), Transatlantic Tumor Model Repositories (TUMOR) (315), and Computational Horizons In Cancer (CHIC) (316), amongst others (Figure 5). Here, we review and evaluate five salient projects for multi-scale cancer modeling towards their clinical deployment.…”

Section: Pipelining Panomics Data Towards In Silico Clinical Systems Oncologymentioning

confidence: 99%

“…Another transatlantic project Computational Horizons In Cancer (CHIC) project (316) launched in 2013, aimed to provide an oncosimulator modeling platform for in silico oncology. CHIC was initiated to develop and implement predictive oncology and individualized multi-scale cancer modeling tools towards assisting quantitative cancer research and personalized cancer therapy.…”

Section: Chic Projectmentioning

confidence: 99%

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Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

Gondal

Chaudhary

2021

Preprint

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Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built on top of this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- or multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multiscale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes by highlighting that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

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Section: Pipelining Panomics Data Towards In Silico Clinical Systems Oncologymentioning

confidence: 99%

Section: Chic Projectmentioning

confidence: 99%

Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

Gondal

Chaudhary

2021

Preprint

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“…In order to demonstrate the capabilities of IAPETUS to cooperate with external data sources and exchange data as needed, the nephro blastoma high-end use case, as defined in MyHealth-Avatar, 29,30 required the establishment of a collaboration with the Computational Horizons in Cancer (CHIC) project. 5 The nephroblastoma Oncosimulator needs preprocessed imaging data to operate. 17,18 To meet this demand, IAPETUS was linked to a clinical data repository developed to hold the data used in CHIC, 31 which acts as one of the third-party sources of data.…”

Section: Json Content Is Sent Through a Validator To Make The Proper Indentationsmentioning

confidence: 99%

“…4 Appropriate reuse of already existing elementary biomodels has led to the creation of complex and highly refined models known as hypermodels. 5 A long-term goal of in silico or computational medicine is to provide clinicians with as many validated and clinically meaningful models as possible so that they can be efficiently supported in their decision-making procedure. Achieving such a goal involves a large number of stakeholders at different time points of a model life cycle.…”

Section: Introductionmentioning

confidence: 99%

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A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine

et al. 2016

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The plethora of available disease prediction models and the ongoing process of their application into clinical practice – following their clinical validation – have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work. In addition, the end users should be provided with all the necessary interfaces for model execution and effortless result retrieval. We therefore propose a software infrastructure, based on a tool, model and data repository that handles the storage of models and pertinent execution-related data, along with functionalities for execution management, communication with third-party applications, user-friendly interfaces to access and use the infrastructure with minimal effort and basic security features.

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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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Computational horizons in cancer (CHIC): Developing meta- and hyper-multiscale models and repositories for in Silico Oncology - A brief technical outline of the project

Abstract: This paper briefly outlines the aim, the objectives, the architecture and the main building blocks of the ongoing large scale integrating transatlantic research project CHIC (http://chic-vph.eu/).

Cited by 5 publications

References 4 publications

Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine

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

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