Abstract
BackgroundAnalyzing the long-term behaviors (attractors) of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability. In this paper we identify the allowed long-term behaviors of a multi-level, 70-node dynamic model of the stomatal opening process in plants.ResultsWe start by reducing the model’s huge state space. We first reduce unregulated nodes and simple mediator nodes, then simplify the regulatory functions of selected nodes while keeping the model consistent with experimental observations. We perform attractor analysis on the resulting 32-node reduced model by two methods: 1. converting it into a Boolean model, then applying two attractor-finding algorithms; 2. theoretical analysis of the regulatory functions. We further demonstrate the robustness of signal propagation by showing that a large percentage of single-node knockouts does not affect the stomatal opening level.ConclusionsCombining both methods with analysis of perturbation scenarios, we conclude that all nodes except two in the reduced model have a single attractor; and only two nodes can admit oscillations. The multistability or oscillations of these four nodes do not affect the stomatal opening level in any situation. This conclusion applies to the original model as well in all the biologically meaningful cases. In addition, the stomatal opening level is resilient against single-node knockouts. Thus, we conclude that the complex structure of this signal transduction network provides multiple information propagation pathways while not allowing extensive multistability or oscillations, resulting in robust signal propagation. Our innovative combination of methods offers a promising way to analyze multi-level models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0327-7) contains supplementary material, which is available to authorized users.
Highlights
Analyzing the long-term behaviors of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability
To obtain a smaller state space, we reduce the size of the network by applying a network reduction technique developed by Saadatpour et al [23] that is proven to preserve the attractors of Boolean models
We find that AnionCh knockout can partially restore stomatal opening inhibited by abscisic acid (ABA), a result not reported by Sun et al, but which is supported by experimental evidence [40]
Summary
Analyzing the long-term behaviors (attractors) of dynamic models of biological systems can provide valuable insight into biological phenotypes and their stability. Representing cellular processes that involve many proteins and small molecules by a signal transduction network can reveal indirect relationships between components and provide new insight [3,4,5]. Such network usually consists of nodes representing biological entities, and edges representing interactions. Modeling allows one to analyze the biological system represented by the network in silico, when performing the relevant experiment is infeasible It helps identify general principles of biological systems [13, 14]
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