Epithelial-mesenchymal transition (EMT) is a normal cell developmental program that occurs aberrantly in numerous carcinomas and promotes chemoresistance. We hypothesize that various extracellular stimuli drive EMT through a conserved set of kinase-regulated signaling pathways, and the strength and duration of signaling through a critical set of those pathways dictate the degree to which EMT will occur. This allows us to develop a mathematical model of EMT in response to extracellular stimuli (EMT agonists) in human pancreas cancer cells, formulate EMT regulation as a model-based control problem, and solve the control problem to obtain the optimal schedule and dosage of combination therapies required to achieve the desired level of EMT. To model the overall ligand-signal-phenotype system, we decomposed it into two subsystems in series. (i) A “signal response” subsystem (f1), whose inputs are the ligand concentrations, and the responses are the relative abundance of the signaling molecules, is followed by (ii) a “phenotype response” subsystem (f2), for which the signals-the responses from f1-serve as the stimuli which then stimulate the mesenchymal phenotypes as the response of f2. This natural decomposition facilitates modeling the overall system as a convolution of the two subsystems, which also allows us to understand the dynamics of the intermediate signal responses. In this presentation, we discuss our preliminary results on the identification of the ligand-signal-phenotype system model and on the solution of the control problem. We estimated the model parameters of f1 (an autoregressive model with exogenous input) and f2 (a PLSR model) using time-series data of the responses of signaling proteins and of the final phenotype measurements (E-cadherin and Vimentin) obtained from experiments performed with five different ligand dosages (EGF, HGF and TGF-β). Subsequently, we determined the optimal ligand dosage trajectory and implemented the prescribed solution via simulation in MATLAB. The results of this study provide a new set of experimentally testable hypotheses on the time-dependent effects of exogenous ligands on a key set of intracellular signaling pathways and, subsequently, the effects of these signals on EMT. They also provide a new paradigm for developing a predictive understanding of how multivariate signaling processes control complex cell phenotypes by integrating data-driven modeling approaches such as PLSR with dynamic control system models.

Model-based control of epithelial-mesenchymal transition through signaling regulation in pancreas cancer cells

ACS Fall 2021

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