(329a) Model-Based Approach for Multivariate Signaling Regulation of Epithelial-Mesenchymal Transition in Pancreas Cancer Cells
The overall ligandâsignalâphenotype system is decomposed into two subsystems in series: (i) a âsignal responseâ subsystem (f1), whose inputs are the ligand concentrations and the responses are the relative abundance of the signaling molecules; and (ii) a âphenotype responseâ subsystem (f2), for which the signalsâthe responses from f1âserve as the stimulus which then stimulates the mesenchymal phenotypes as the response of f2. This natural decomposition of the system is convenient for modeling the overall system as a convolution of the two subsystems, which also allows us to understand the dynamics of the intermediate signal responses.
Such representation allows us to formulate the determination of the optimal ligand dosage for EMT inhibition as a control problem, where the manipulated variables are concentrations of the ligandsâepidermal growth factor (EGF), hepatocyte growth factor (HGF), and transforming growth factor beta (TGF-Î²); the controlled variables are the indicators of EMT such as the expression and localization of E-cadherin and vimentin, and the circularity of the tumor cell clusters.
In this presentation, we discuss our preliminary results on the identification of the ligandâsignalâphenotype 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 obtained from experiments performed with five different ligand dosages. Subsequently, we determined the optimal ligand dosage trajectory and implemented the prescribed solution via simulation in MATLAB. The results of our study provide a new set of experimentally testable hypotheses for validating the identity of the most important signaling pathways that govern EMT. They also provide a new paradigm for integrating data-driven modeling approaches such as PLSR with dynamic control system models to develop a predictive understanding of how multivariate signaling processes control complex cell phenotypes. The primary benefit of such a paradigm is that it provides a quantitative, model-based framework for using the indicated predictive understanding, in reverse, to determine how best to manipulate the signaling processes to achieve desired phenotypic responses optimally.