Predicting phenotype to mechanotype relationships in cells based on intracellular signaling network

Abstract

ABSTRACTCells originating from the same tissue can respond differently to external signals depending on the genotypic and phenotypic state of the cell and its local environment. We have developed a semi-quantitative-computational model to analyze the intracellular signaling network and its outcome in the presence of multiple external signals including growth factors, hormones, and extracellular matrix. We use this model to analyze the cell’s mechanical response to external stimuli and identify the key internal elements of the network that drive specific outcomes within the response space. The model is built upon the 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 modularity in the model and incorporate dynamical aspects, mass-action kinetics, and chemo-mechanical effects on only certain transition rates within specific modules as required, creating a Boolean-Hybrid-Modular (BoHyM) signal transduction model. We present this model as a comprehensive, cell-type agnostic, user-modifiable tool to investigate how extra-and intra-cellular signaling can regulate cellular cytoskeletal components and consequently influence cell-substrate interactions, force generation, and migration. Using this tool, we show how slight changes in signaling network architectures due to phenotypic changes can alter cellular response to stress hormone signaling in an environment-dependent manner. The tool also allows isolating effector proteins driving specific cellular mechanical responses. Ultimately, we show the utility of the tool in analyzing transient chemo-mechanical dynamics of cells in response to time-varying chemical stimuli.