SiGNet: A signaling network data simulator to enable signaling network inference

<div><p>Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network’s behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (<u>Si</u>gnal <u>G</u>enerator for <u>Net</u>works): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.</p></div

Using the SiGNet plugin to simulate experimental data for a biological network.

<p><b>(A</b> to <b>D)</b> Screen snapshots showing how SiGNet is used. After a network is imported into or drawn out in Cytoscape (A), the user determines the number of ‘replicates’, ‘time points’, the amount of noise and whether nodes are inhibited or activated (B). SiGNet then generates simulated data for each replicate and time point. Here (C), nodes are colored according to their average value at time point 1. The data are exported and used for network inference (D). The accuracy of predicted edges can be benchmarked against the structure of the network used in A and scores of sensitivity, precision and recall can be calculated. A more detailed walkthrough example of how SiGNet can be used to aid benchmarking of inference techniques is available at signet.icr.ac.uk.</p

SiGNet simulates node responses to signaling interactions of different strengths and types.

<p>(<b>A</b>) Schematic showing the structure of a small network where node A weakly inhibits node B, inhibits node C, strongly inhibits node D, weakly activates node E, activates node F and strongly activates node G. (<b>B</b>) Graph showing the signaling output from each of the nodes shown in (A) over time, as predicted by SiGNet. Data are mean values calculated from ten ‘experimental replicates’ produced using SiGNet. (<b>C</b>) Schematic showing two networks, ABC and DEF, with identical network structures. The ABC network is perturbed by the experimental inhibition of A. (<b>D</b>) Graphs showing the signaling output of nodes A, B and C (upper) and nodes D, E and F (lower). The inhibition of node A is removed at time point 1.</p

Comparing SiGNet to tools that simulate transcriptional network datasets.

<p>Comparing SiGNet to tools that simulate transcriptional network datasets.</p

Simulating the effect of experimental perturbation on a real biological signaling network in SiGNet.

<p>SiGNet was used to simulate the effect of EGF treatment on EGFR and its downstream proteins, and the simulated data tested against published experimental data [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177701#pone.0177701.ref032" target="_blank">32</a>]. (<b>A</b>) Schematic showing the network structure, which was based on interactions reported by Blagoev et al. The network was drawn in Cytoscape and used as an input for SiGNet. (<b>B</b>) Data simulated for the network in (A) using SiGNet, with and without the optional decay function. Data shown are mean values calculated from ten ‘experimental replicates’. Pearson correlations between simulated and real data are shown. Additional simulations of this network incorporating simple feedback mechanisms are illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177701#pone.0177701.s002" target="_blank">S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177701#pone.0177701.s003" target="_blank">S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177701#pone.0177701.s004" target="_blank">S4</a> Figs.</p