Equilibrium switching in nonlinear interaction networks with concurrent antagonism
- Published
- Accepted
- Subject Areas
- Bioinformatics, Biophysics, Computational Biology, Mathematical Biology, Computational Science
- Keywords
- biological switch, regulatory network, species competition, sigmoid kinetics, multi-stability, repressilator
- Copyright
- © 2014 Rabajante
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. Equilibrium switching in nonlinear interaction networks with concurrent antagonism. PeerJ PrePrints 2:e382v1 https://doi.org/10.7287/peerj.preprints.382v1
Abstract
In this paper, we examine a nonlinear concurrent decision-making model (CDM) of interaction networks that involve more than two antagonistic components (e.g., proteins, species, communities, mental choices). The model assumes sigmoid kinetics where every component stimulates itself but represses all others. We are able to prove general dynamical properties of the CDM (e.g., location and stability of steady states) for any dimension of the state space even if the reciprocal antagonism between two components is asymmetric. There are cases where asymmetric interaction generates oscillatory behavior. Some parameters can serve as biological regulators for inducing steady state switching by leading a temporal state to escape an undesired equilibrium. Increasing the maximal growth rate and decreasing the decay rate can expand the basin of attraction of a steady state with the desired component having the dominant value. We further show that perpetually adding an external stimulus can shutdown multi-stability of the system that increases the robustness of the system against stochastic noise.
Author Comment
This is an early preprint version only.