Multiscale Spatial and Temporal Signaling and Patterning of Cells and Tissues: Stochastic Control Networks and Tissue Wound Healing

Understanding and manipulating the machineries of cells and tissues requires a multi-scale approach that examines details of genes/proteins and their interactions to elucidate complex behavior of cellular and tissue states, as well as their spatio-temporal pattern formation. We describe new development in the fundamental theory to solve the discrete chemical master equation (dCME) that can account for stochastic control of rare and small copy-number events important for determining cellular fate, highlighting the ACME (accurate chemical master equation) multi-buffer method for exact time-evolving probabilistic landscape computation, without Gillespie simulation or Fokker-Planck/Langevin approximation.  We show how to relate the computed landscape probability to phenomenological characterization of the decision networks such as bi-stability, epigenetic states, and the robustness of wild type versus mutants.  At the cellular level, we describe a dynamic finite-element cell (dFEMC) model and an algorithm for simulating time-evolving spatio-temporal pattern formation of cell populations.  To understand how proteins and networks inside individual cells lead to formation of global tissue patterns, we described in some details on how the process of wound healing can be studied using the dFEMC models, with explicit protein networks controlling growth/dvision, migration, apoptosis, and quiescence embedded inside each cell.  We discuss simulation results of cell-cell/ECM interactions on predicting keartinocyte migration, hyperproliferative zone formation, and the speed of wound closure.

(Joint work with Youfang Cao, Luisa DiPietro, Anna Terebus, and Jieling Zhao. Please visit for further information).

Hosted by the Cell-to-Macroscale Working Group.

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Jie Liang, Professor of Bioengineering, University of Illinois at Chicago, IL