Scalable systems modelling to simulate body-level interplays among non-communicable diseases

Introduction

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.

Digital Twin for Health

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 an appropriate balance of phenomenological and mechanistic approaches. This is essential to cope with overwhelming biological complexity, preserve interpretability, and incorporate real-world data.

O-Health Ecosystem

This is the niche of O-Health, which 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:

1. Lung emphysema
2. Atherosclerosis
3. Intervertebral disc degeneration
4. Knee osteoarthritis

These diseases affect load-bearing organs at different anatomical locations.

Model Components

The cellular/molecular scale components of each NCD model will vertically share predicted variables with an interface model of endothelial cell dysfunction. This model will communicate with a transversal model of body-wide systemic communications.

Modularity and Interoperability

The O-Health ecosystem will be modular and interoperable. Mechanistic modelling will be covered by:

• Finite element models at the organ/tissue scales
• Agent-based (AB) models at the cell/molecular scales

AB models will incorporate high-level interaction graphs for interpretable phenomenological modelling where necessary.

Data Integration

Graphs will merge knowledge projection and correlation models extracted from longitudinal population cohort data, which will also be 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.