Abstract
BackgroundFeedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks.ResultsHiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition.ConclusionsHiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop’s usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop.
Highlights
Feedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells
HiLoop has three modules: (1) Detection and Visualization: it enumerates the appearances of a network structure in a given network, and presents them in intuitive ways; (2) Enrichment: it computes the enrichment of a network structure or its related statistics based on a background population of random networks; and (3) Modeling: it constructs dynamic models with chosen network or subnetworks, and simulates the models with random parameter sets
The ratio of positive feedback loops to negative feedback loops is significantly high in the epithelial-mesenchymal transition (EMT) network (p < 1 0–5), but not in the other two networks. These results suggest that the EMT network contains high-feedback motifs that may contribute to its characteristic dynamics in a collective fashion, whereas similar dynamics of the T cell network or in the broader TRRUST2 network may be driven by individual genes or modules [35]
Summary
Feedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. More recent theoretical and experimental studies have revealed that systems of more than two interconnected feedback loops have additional roles in controlling cell dynamics, including low-rate and irreversible differentiation of adipocytes, stable intermediate cell states between epithelial and mesenchymal lineages, and stepwise lineage decision of T-cells [4,5,6,7]. We define these interconnected feedback loops as high-feedback loops, a term generalized from a definition in [5] (Fig. 1a). It is unclear whether such high-feedback loops with functional dynamical properties exist widely in gene regulatory networks
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