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The processes taking place inside the living cell are now understood to thepoint where predictive computational models can be used to gain detailedunderstanding of important biological phenomena. A key challenge is toextrapolate this detailed knowledge of the individual cell to be able toexplain at the population level how cells interact and respond with each otherand their environment. In particular, the goal is to understand how organismsdevelop, maintain and repair functional tissues and organs.  In this paper we propose a novel computational framework for modelingpopulations 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 enablepropagation 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 veryflexible and may straightforwardly be coupled with continuous-time descriptionsof biochemical signalling within, and between, individual cells.

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