The evolution of systems biology and systems medicine: From mechanistic models to uncertainty quantification

TL;DR

This paper reviews the evolution of systems biology models from simple mechanistic approaches to complex multiscale models, emphasizing uncertainty quantification and their role in understanding disease mechanisms and drug discovery.

Contribution

It provides a comprehensive overview of the progression from classical models to multiscale systems, highlighting recent advances and methods for improving prediction accuracy.

Findings

Multiscale models integrate signaling, gene regulation, and metabolism.

Models reveal new disease mechanisms and potential drug targets.

Uncertainty quantification enhances model reliability.

Abstract

Understanding the mechanisms of interactions within cells, tissues, and organisms is crucial to driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating biological systems and revealing biochemical regulatory mechanisms. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks and uncover biochemical mechanisms in healthy and diseased states. The rapid development of high-throughput sequencing techniques and computational tools has recently enabled models that span multiple scales, often integrating signaling, gene regulatory, and metabolic networks. These multiscale models enable comprehensive investigations of cellular networks and thus reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from classical mechanistic models to larger, multiscale models that integrate multiple layers of cellular networks. We introduce several examples of models of hypertrophic cardiomyopathy, exercise, and cancer cell proliferation. Additionally, we discuss methods that increase the certainty and accuracy of model predictions. Integrating multiscale models has become a powerful tool for understanding disease and inspiring drug discoveries by incorporating omics data within the cell and across tissues and organisms.