project . 2022 - 2027 . On going


Scalable systems modelling to simulate body-level interplays among non-communicable diseases
Open Access mandate for Publications and Research data
European Commission
Funder: European CommissionProject code: 101044828 Call for proposal: ERC-2021-COG
Funded under: HE | ERC | HORIZON-ERC\HORIZON-AG Overall Budget: 1,998,860 EURFunder Contribution: 1,998,860 EUR
Status: On going
01 Nov 2022 (Started) 31 Oct 2027 (Ending)

Non-communicable diseases (NCD) that involve load-bearing organs emerge silently according to complex mechanisms that are likely to involve inter-disease systemic communications. Clinical explorations cannot apprehend such intricate emergence, but I postulate that multiscale in silico models can. The digital twin for health has progressed a lot in the last decades, but multi-disease transversal modelling has not happened. It requires unique developments to couple small to large-scale model components with appropriate balance of phenomenological and mechanistic approaches, to cope with overwhelming biological complexity, preserve interpretability and incorporate real-world data. This is the niche of O-Health that proposes a scalable ecosystem of multiscale NCD models interrelated through a systemic model of low-grade inflammation. The project tackles such vertical and transversal physiology-based computational modelling through four major NCD, lung emphysema, atherosclerosis, intervertebral disc degeneration and knee osteoarthritis that affect load-bearing organs at different anatomical locations. The cellular /molecular scale components of each NCD model will vertically share predicted variables with an interface model of endothelial cell dysfunction that communicates with a transversal model of body-wide systemic communications. The O-health ecosystem will be modular and interoperable. Mechanistic modelling will be covered by finite element models at the organ /tissue scales and by agent-based (AB) models at the cell /molecular scales. AB models will incorporate high-level interaction graphs for interpretable phenomenological modelling where necessary. Graphs will merge knowledge projection and correlation models extracted from longitudinal population cohort data, also used to evaluate O-Health. Interoperability will be ensured through standard languages such as Field and Systems Biology Mark-up Languages, enabling the scalability of the O-Health ecosystem.

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