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Annual Review of Biomedical Engineering

Volume 27, 2025

The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification

  • Lingxia Qiao1,2,***, Ali Khalilimeybodi2,***, Nathaniel J. Linden-Santangeli2,*** and Padmini Rangamani1,2
  • 1Department of Pharmacology, University of California San Diego, La Jolla, California, USA; email: [email protected] 2Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA
    *** *These authors contributed equally to this article
  • Vol. 27:425-447 (Volume publication date May 2025)
  • First published as a Review in Advance on February 19, 2025
  • Copyright © 2025 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

Understanding interaction mechanisms within cells, tissues, and organisms is crucial for driving developments across biology and medicine. Mathematical modeling is an essential tool for simulating such biological systems. Building on experiments, mechanistic models are widely used to describe small-scale intracellular networks. The development of sequencing techniques and computational tools has recently enabled multiscale models. Combining such larger scale network modeling with mechanistic modeling provides us with an opportunity to reveal previously unknown disease mechanisms and pharmacological interventions. Here, we review systems biology models from mechanistic models to multiscale models that integrate multiple layers of cellular networks and discuss how they can be used to shed light on disease states and even wellness-related states. Additionally, we introduce several methods that increase the certainty and accuracy of model predictions. Thus, combining mechanistic models with emerging mathematical and computational techniques can provide us with increasingly powerful tools to understand disease states and inspire drug discoveries.

Keyword(s): drug discovery, mathematical biology, sensitivity analysis, systems biology, systems medicine, uncertainty quantification
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2025-05-01
2025-06-16
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The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification
Annual Review of Biomedical Engineering 27, 425 (2025); https://doi.org/10.1146/annurev-bioeng-102723-065309
/content/journals/10.1146/annurev-bioeng-102723-065309
/content/journals/10.1146/annurev-bioeng-102723-065309
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