Data-driven modelling of biological multi-scale processes

TL;DR

This paper reviews mathematical approaches for multi-scale biological modeling, discusses challenges in data integration and computation, and suggests future directions for inference methods in complex biological systems.

Contribution

It provides a comprehensive overview of current multi-scale modeling techniques, highlights key challenges, and proposes new ideas for tailored inference methods in biological systems.

Findings

Review of state-of-the-art multi-scale modeling approaches

Identification of key challenges in data integration and computational efficiency

Discussion of recent trends like reduced order and surrogate models

Abstract

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.