DoGeNetS: using optimisation to discriminate regulatory network topologies based on gene expression data

Abstract

Gene regulatory networks (GRNs) determine the dynamics of gene expression. Interest often focuses on the topological structure of a GRN while numerical parameters (e.g. decay rates) are unknown and less important. For larger GRNs, inference of structure from gene expression data is prohibitively difficult. Models are often proposed based on integrative interpretation of multiple sources of information. We have developed DoGeNetS (Discrimination of Gene Network Structures), a method to directly assess candidate models of GRN structure against a target gene expression data set. The transsys language serves to model GRN structures. Numeric parameters are optimised to approximate the target data. Multiple restarts of optimisation yield score sets that provide a basis to statistically discriminate candidate models according to their potential to explain the target data. We demonstrate discrimination power of the DoGeNetS method by relating structural divergence to divergence between gene expression data sets. Known models are used to generate target expression data, and a set of candidate models with a defined structural divergence to the true model is produced. Structural divergence and divergence of expression profiles after optimisation are strongly correlated. We further show that discrimination is possible at noise levels exceeding those typical of contemporary microarray data. DoGeNetS is capable of discriminating the best GRN structure from among a small number of candidates. p values indicate whether differences in divergence of expression are significant. Although this study uses single gene knockouts, the DoGeNetS method can be adapted to simulate a virtually unlimited range of experimental conditions. [Includes supplementary material].

Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat

“…The parameters of the target genes and their products, as well as the identification of the best target topology, among a series of candidate models, were inferred using the DoGeNetS (discrimination of gene network structures) method [56], which addresses the research challenge of quantitatively and objectively comparing candidate structural models where most numerical parameters are not determined. DoGeNetS aims to discriminate computational models of GRN structure according to their ability to reproduce a set of gene expression measurements (synthetically or empirically generated data).…”

Reverse engineering gene regulatory networks: Coupling an optimization algorithm with a parameter identification technique

“…To infer target networks from time series gene profiles, many issues that are related to biological systems cannot simply be addressed by topology reconstruction or parameter estimation alone. As mentioned in [ 7 ], on the one hand, if the topology of a GRN can be reconstructed, it is usually not sufficient for a satisfactory scientific understanding (i.e., lacking the modeling of biological details). On the other hand, the optimized parameters for a given network topology (or mathematical structure) do not enable discrimination of alternative candidates.…”