Predicting phenotype to mechanotype relationship in cells based on intracellular signaling networks.

Abstract

We have developed a semi-quantitative-computational model to analyze the intracellular signaling network and how it impacts cellular mechanics in the presence of multiple external signals including growth factors, hormones, and extracellular matrix. We use this model to analyze the changes in cellular mechanics to external stimuli and identify the key internal elements of the network that drive specific outcomes within this phase space. The model is built upon Boolean approach to network modeling, where the state of any given node is determined using the state of the connecting nodes and Boolean logic. This allows us to analyze the network behavior without the need to estimate all the various interaction rates between different cellular components. However, such an approach is limited in its ability to predict network dynamics and temporal evolution of the cell state. So, we introduce dynamical aspects using mass-action kinetics as well as chemo-mechanical modulation of reaction rate constants at specific nodes of the Boolean network. Using this hybrid modeling approach, we provide a unique computational model to connect cells biochemical signaling profile to mechanical aspects of traction force generation, adhesion, and migration.