Multiscale Computational Modeling for Understanding Proteins and Cells: Genetic Variants, Chromatin Folding, Stochastic Networks, and Tissue Patterning
High-throughput measurements of genetic information, epigenetic modifications, and cellular/tissue morphology across individuals and species presents opportunities for understanding fundamental biology and for improving human health. We first discuss how to integrate genetic variant information, protein structure, and evolution theory to predict effects of mutation and to uncover the underlying molecular mechanism. We then discuss how chromosome conformation capture data can be summarized through computational model of large ensembles of C-SAC chromatin models, and how novel mechanisms of promoter-enhancer interactions and gene regulation might be uncovered. We further discuss recent development of the ACME (accurate chemical master equation) method for computing the exact time-evolving probabilistic landscapes by solving the discrete chemical master equation without Gillespie simulation or Fokker-Planck/Langevin approximations. Finally, we explore how tissue patterning and wound healing can be computationally investigated.