In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.
Recommended citation: R. Vasan, M. P. Rowan, C. T. Lee, G. R. Johnson, P. Rangamani, and M. J. Holst$ "Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations". Frontiers in Physics 7 (January 2020). pp. 1-24