A computational approach for perturbation-induced EMT transitions.

The Epithelial-mesenchymal transition (EMT) is a cellular state transition fundamental to development, wound healing, and cancer metastasis. The gene regulatory mechanisms underlying EMT have been extensively documented, revealing gene regulatory networks (GRNs) involving groups of mutually inhibiting transcription factors and microRNAs. Despite significant progress from both experimental and computational approaches, the details of how the EMT GRN initiates EMT in response to various external inputs is still not well understood. Here, we apply both Boolean and ordinary differential equation (ODE)-based methods to simulate a well-studied 26-node, 100-edge EMT GRN, examining its response to a wide range of single- and double-node perturbations. We evaluate the characteristics of effective EMT-inducing signals, particularly examining the amplifying role of transcriptional noise in determining the likelihood and mean transit time of an EMT. Together, these models establish a complementary framework for understanding and predicting drivers of EMT in the context of a GRN. We anticipate that this framework for a systematic study of in-silico GRN perturbations will be useful in developing increasingly accurate dynamical GRN models for various biological processes.