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Annual Review of Biomedical Data Science

Volume 5, 2022

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Abstract

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome Project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science.

Keyword(s): cell architecture, cell mechanics, cell signaling, computational physiology, deep learning, physiome
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/content/journals/10.1146/annurev-biodatasci-072018-021246
2022-08-10
2024-04-25
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/content/journals/10.1146/annurev-biodatasci-072018-021246
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The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?
Annual Review of Biomedical Data Science 5, 341 (2022); https://doi.org/10.1146/annurev-biodatasci-072018-021246
/content/journals/10.1146/annurev-biodatasci-072018-021246
/content/journals/10.1146/annurev-biodatasci-072018-021246
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