How Computation Is Helping Unravel the Dynamics of Morphogenesis

Abstract

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 is accepted that mechanics plays a role in many of the processes determining the cell fate map, providing further opportunities for modeling and simulation. We provide a perspective of this integrative field, discussing recent advances and outstanding challenges to understand the determination of the fate map. At the basis, 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 model cellular biophysics and its links with biochemical and genetic factors. However, there are still limitations derived from the dynamic nature of embryonic tissue and its spatio-temporal heterogeneity. Also, the increasing complexity and variety of implementations make it difficult to harmonize and cross-validate models. The solution to these challenges will likely require integrating novel in vivo measurements of embryonic biomechanics into the models. Machine Learning has great potential to classify spatiotemporally connected groups of cells with similar dynamics. Emerging Deep Learning architectures facilitate the discovery of causal links and are becoming transparent and interpretable. We anticipate 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, identification of therapeutic targets, and synthetic life.

Generation, Transmission, and Regulation of Mechanical Forces in Embryonic Morphogenesis

“…Computational modeling employs in silico models to provide new insights for the mechanical basis of embryonic morphogenesis, and to create predictive capacity to help reveal causal links and contribute to the understanding of embryogenesis. [ 153 ] Agent‐based models are typically utilized to explore single‐cell biomechanical impact, [ 156,157 ] multi‐cellular interactions, [ 158 ] and multi‐cell phenomena at the tissue scale. [ 159 ] However, agent‐based models fail in adequately capturing the changes of complex cell morphology.…”

“…The ability of predicting mechanical behaviors using these models can help uncover causal relations and enable new understanding of fundamental biomechanical mechanism of embryogenesis. [ 153 ] The growing field of artificial intelligence has led to the development of agent‐based models, which examine the behavior of many small, autonomous agents and the resulting behavior over the entire system. [ 154 ] A relatively early use of agent‐based models as they apply to cell migration and mechanics is a study on cell dedifferentiation and epithelial‐mesenchymal transition as it applied to cancers.…”

“…Vertex models can also capture and visualize topological changes in the cell environment. [ 153 ] Yu and Fernandez‐Gonzalez [ 166 ] investigated the introduction of vertex models into epithelial morphogenesis. Cells are configured as polygons and are composed of edges and nodes in the vertex model.…”

Cell Response in Free-Packed Granular Systems

“…Recently, computational analysis has gained momentum as a complementary tool to understand and characterize cell adhesion processes 61,62 and their role in morphogenesis 63,64 . To shed light on the role of the cell/microparticles interaction we performed particle-based simulations.…”

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

“…To answer such questions, various conceptual and computational models have been developed [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Lewis Wolpert, a pioneer in this field, introduced the concept of “positional information”, where a morphogen gradient develops along an axis that the cells could use to decode their relative positions and make morphogenetic decisions in embryogenesis or regeneration [ 25 ].…”