How Computation Is Helping Unravel the Dynamics of Morphogenesis - Pastor-Escuredo, David; del Álamo, Juan C. (Frontiers in Physics, 2020)
The growing availability of imaging data, calculation power, and algorithm sophistication are transforming the study of morphogenesis into a computation-driven discipline. In parallel, it has become clear that mechanics govern many processes that determine the cell fate map. High-resolution microscopy and image processing provide digital representations of embryos that facilitate quantifying their mechanics with computational methods. Moreover, innovations in in-vivo sensing and tissue manipulation can now characterize cell-scale processes to feed larger-scale representations. A variety of mechanical formalisms have been proposed to provide mechanistic insight about cellular biophysics and its links with biochemical and genetic factors. However, there are still limitations derived from the dynamic nature embryonic tissue and its spatio-temporal heterogeneity. In addition, the increasing complexity and variety of implementations makes it difficult to harmonize and cross-validate models. The solution to these challenges could come from integrating novel experimental measurements of embryonic mechanical properties into the models. Machine learning also has great potential to improve these limitations by identifying morphomechanical domains, i.e., spatio-temporally connected groups of cells with similar evolution, and determining their relationship with biomechanical events. Emerging, model-driven, deep learning architectures can facilitate the discovery of causal links in developmental processes, and at the same time, are becoming transparent and interpretable. We anticipate that these new tools will lead to multi-scale models with the necessary accuracy and flexibility to formulate hypotheses for in-vivo and in-silico testing. These methods have promising applications for tissue engineering, stem cell research, disease, and drug studies, and synthetic life.