# Scalable population-level modeling of biological cells incorporating mechanics and kinetics in continuous time

## TL;DR

This paper introduces a scalable, multiscale computational framework for modeling biological cell populations, integrating mechanics and kinetics to simulate interactions and responses at the tissue level efficiently.

## Contribution

The novel framework combines biomechanical properties with continuous-time biochemical signaling, enabling scalable and flexible population-level cell modeling.

## Key findings

- Framework is highly scalable and efficient.
- Can be coupled with biochemical signaling models.
- Demonstrated flexibility in biological simulations.

## Abstract

The processes taking place inside the living cell are now understood to the point where predictive computational models can be used to gain detailed understanding of important biological phenomena. A key challenge is to extrapolate this detailed knowledge of the individual cell to be able to explain at the population level how cells interact and respond with each other and their environment. In particular, the goal is to understand how organisms develop, maintain and repair functional tissues and organs. In this paper we propose a novel computational framework for modeling populations of interacting cells. Our framework incorporates mechanistic, constitutive descriptions of biomechanical properties of the cell population, and uses a coarse graining approach to derive individual rate laws that enable propagation of the population through time. Thanks to its multiscale nature, the resulting simulation algorithm is extremely scalable and highly efficient. As highlighted in our computational examples, the framework is also very flexible and may straightforwardly be coupled with continuous-time descriptions of biochemical signalling within, and between, individual cells.