MAPK’s networks and their capacity for multistationarity due to toric steady states
•We apply algebraic techniques to study multistationarity in MAPK’s networks.•We show that MAPK’s networks are capable of multistationarity due to their topology.•Our results suggest that a negative feedback does not affect multistationarity.•Our method highlights the robustness of MAPK’s networks....
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Veröffentlicht in: | Mathematical biosciences 2015-04, Vol.262, p.125-137 |
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Sprache: | eng |
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Zusammenfassung: | •We apply algebraic techniques to study multistationarity in MAPK’s networks.•We show that MAPK’s networks are capable of multistationarity due to their topology.•Our results suggest that a negative feedback does not affect multistationarity.•Our method highlights the robustness of MAPK’s networks.
Mitogen-activated protein kinase (MAPK) signaling pathways play an essential role in the transduction of environmental stimuli to the nucleus, thereby regulating a variety of cellular processes, including cell proliferation, differentiation and programmed cell death. The components of the MAPK extracellular activated protein kinase (ERK) cascade represent attractive targets for cancer therapy as their aberrant activation is a frequent event among highly prevalent human cancers. MAPK networks are a model for computational simulation, mostly using ordinary and partial differential equations. Key results showed that these networks can have switch-like behavior, bistability and oscillations. In this work, we consider three representative ERK networks, one with a negative feedback loop, which present a binomial steady state ideal under mass-action kinetics. We therefore apply the theoretical result present in [27] to find a set of rate constants that allow two significantly different stable steady states in the same stoichiometric compatibility class for each network. Our approach makes it possible to study certain aspects of the system, such as multistationarity, without relying on simulation, since we do not assume a priori any constant but the topology of the network. As the performed analysis is general it could be applied to many other important biochemical networks. |
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ISSN: | 0025-5564 1879-3134 |
DOI: | 10.1016/j.mbs.2014.12.010 |