The Cell Physiome: What do we need in a computational physiology framework for predicting single cell biology?

TL;DR

This paper advocates for a comprehensive computational framework called the Cell Physiome, emphasizing biophysics-based models and bond graphs to understand single cell biology through detailed, integrative simulations.

Contribution

It introduces the Cell Physiome concept, reviews current progress, and proposes bond graphs as a modular approach for creating detailed, energy-balanced models of single cell physiology.

Findings

Bond graphs enable integration of multiple physical processes in cell models.

Spatially detailed models reveal new mechanisms in cell biology.

Progress and challenges in developing cell physiome models are discussed.

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: 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 towards 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 re-usability of computational models of cells at scale. We conclude with a look forward into steps that will help fully realize this exciting new field of mechanistic biomedical data science.