Background: The transitions between epithelial (E) and mesenchymal (M) cell
phenotypes are essential in many biological processes like tissue development and
cancer metastasis. Previous studies, both modeling and experimental, suggested
that in addition to E and M states, the network responsible for these phenotypes
exhibits intermediate phenotypes between E and M states. The number and
importance of such states is subject to intense discussion in the EMT community.
Results: Previous modeling e orts used traditional bifurcation analysis to explore
the number of the steady states that correspond to E, M and intermediate states
by varying one or two parameters at a time. Since the system has dozens of
parameters that are largely unknown, it remains a challenging problem to fully
describe the potential set of states and their relationship across all parameters.
We use the computational tool DSGRN (Dynamic Signatures Generated by
Regulatory Networks) to explore the intermediate states of an EMT model
network by computing summaries of the dynamics across all of parameter space.
We nd that the only attractors in the system are equilibria, that E and M states
dominate across parameter space, but that bistability and multistability are
common. Even at extreme levels of some of the known inducers of the transition,
there is a certain proportion of the parameter space at which an E or an M state
co-exists with other stable steady states.
Conclusions: Our results suggests that the multistability is broadly present in
EMT network across parameters and thus response of cells to signals may
strongly depend on their history and genetic background.
Keywords: Epithelial-Mesenchymal transition; multistability; network models