Mechanistic interpretation of biological tissue growth experiments with a computational model

Abstract The growth rates of biological tissues are influenced by the existing substrate geometry, mechanobiological processes and the interplay between them. Disentangling the contributions of geometry and mechanobiology experimentally is challenging, as mechanical properties are difficult to measure and tissue samples provide only static snapshot in time. However, the composition of a tissue preserves cues of the dynamic processes that shaped its architecture. In this work, we present a computational model of tissue growth that captures aspects of the interplay between geometry, mechanics, and stochastic biological processes, which we use to generate synthetic tissue compositions directly comparable with experimental samples. This framework enables quantitative analysis of tissue morphology, inference of underlying growth mechanisms, estimation of dynamic rates from single-time-point data, and investigation of how stochasticity contributes to emergent growth patterns. We demonstrate the applicability of the model to simulate the growth of different tissue types by applying this framework to two distinct tissue growth scenarios: (i) tissue grown within 3D-printed porous scaffolds, and (ii) bone formation in cortical pores.