Multistability in the epithelial-mesenchymal transition network

Abstract

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 epithelial-mesenchymal transition (EMT) community. Results: Previous modeling efforts 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 find 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 suggest that the multistability is broadly present in the EMT network across parameters and thus response of cells to signals may strongly depend on the particular cell line and genetic background.

Hybrid E/M phenotype(s) and stemness: a mechanistic connection embedded in network topology

“…Mechanism-based models of individual axes of plasticity are the building blocks required to answer this question from a dynamical systems perspective. There has been increasing interest in dynamical modeling of EMP [82][83][84][85][86]; similar efforts to model related aspects of EMP such as metabolic reprogramming [87], autophagy [88,89], therapy resistance [90,91], and immune evasion [92] are being attempted too. Coupling these modules to decode the interconnections among different axes of plasticity can help unravel the survival strategies of metastasis-initiating cells and eventually contribute to designing new clinical strategies.…”

Multi-Stability and Consequent Phenotypic Plasticity in AMPK-Akt Double Negative Feedback Loop in Cancer Cells

“…A hallmark of regulatory networks enabling phenotypic switching in cancer cell populations is multi-stability, i.e., the ability of isogenic cells to reversibly acquire diverse phenotypes [5,11,12,[57][58][59][60][61], as reported earlier also for bacterial [62] and viral [63] populations. Multi-stability can enable 'spontaneous' switching among cell phenotypes (different attractors in the Waddington's landscape) due to biological noise (that can operate at multiple levels including transcriptional or conformational [64,65]), and thus facilitate nongenetic heterogeneity [8,66].…”

Multistability and consequent phenotypic plasticity in AMPK-Akt double negative feedback loop in cancer cells

“…A hallmark of regulatory networks enabling phenotypic switching in cancer cell populations is multistability, i.e. the ability of isogenic cells to reversibly acquire diverse phenotypes [5,11,12,[52][53][54][55][56], as reported earlier for bacterial [57] and viral [58] populations too. Multistability can enable 'spontaneous' switching among cell phenotypes (different attractors in the Waddington's landscape) due to biological noise (that can operate at multiple levels including transcriptional or conformational [59,60]), and thus facilitate non-genetic heterogeneity [8,61].…”