Biochemistry, Department of


Document Type


Date of this Version



Integrative and Comparative Biology 61:6 (December 2021), pp. 2255–2266.

doi: 10.1093/icb/icab165


Copyright © 2021 Katharine A. White, Kira D. McEntire, Nicole R. Buan, Lecia Robinson, and Elisar Barbar. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. Used by permission.


Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers, and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize the integration of mathematical modeling and biology. In this work, we explore the urgent need to expand the use of existing mathematical models across biological scales, develop models that are robust to biological heterogeneity, harness feedback loops within the iterative-modeling process, and nurture a cultural shift toward interdisciplinary and cross-field interactions. Better integration of biological experimentation and robust mathematical modeling will transform our ability to understand and predict complex biological systems.