Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

Reverse engineering methods are typically first tested on simulated data from in silico networks, for systematic and efficient performance assessment, before an application to real biological networks. In this paper we present a method for generating biologically plausible in silico networks, which allow realistic performance assessment of network inference algorithms. Instead of using random graph models, which are known to only partly capture the structural properties of biological networks, we generate network structures by extracting modules from known biological interaction networks. Using the yeast transcriptional regulatory network as a test case, we show that extracted modules have a biologically plausible connectivity because they preserve functional and structural properties of the original network. Our method was selected to generate the "gold standard" networks for the gene network reverse engineering challenge of the third DREAM conference (Dialogue on Reverse Engineering Assessment and Methods, Cambridge, MA, 2008)

Network inference by integrating biclustering and feature selection

In order to develop better therapies to combat specific abnormalities present in the gene regulatory network (GRN) of cancer patients, it is crucial to gain a better understanding of regulatory networks in complex biological systems. An important class of methods in systems biology are network inference (NI) methods, which aim to reconstruct a GRN from high-throughput data (e.g. microarrays or next-generation sequencing). GENIE3 is a state-of-the-art method which employs feature selection to identify the best subset of regulators for each gene. While this method is amongst the best performing NI methods, it fails to take into account expected topological properties of a GRN: a GRN consists of modules, each of which consists of genes coregulated by a common set of regulators. We present BiGENIE, a method which takes the modular topology of a GRN into account. By firstly inferring modules – groups of genes coregulated by a common regulator – using several biclustering methods, the overall topology of the network is reconstructed. Subsequently, the regulator genes for each of the modules is inferred using GENIE3

A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data

Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach

State Space Model with hidden variables for reconstruction of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN.</p> <p>Method</p> <p>True GRNs and synthetic gene expression datasets were generated by using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks.</p> <p>Results</p> <p>Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN.</p> <p>Conclusion</p> <p>This study provides useful information in handling the hidden variables and improving the inference precision.</p

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets. Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses. Availability: The methods outlined in this article have been implemented in Matlab and are available on request

Development of a Novel Algorithm to Remove Spurious Edges from Biological Networks Through Functional Enrichment

The field of systems biology has facilitated the modelling of large and complex biological networks. These networks, generated from prior knowledge contained in the corpus of medical and scientific literature, or from experimental data are being used to model differing macromolecule networks associated with distinct disease states. While these networks are vital in understanding disease pathology and possible treatment options, they are rife with spurious interactions. These interactions arise from the methods used to create such networks, where the ability to discriminate between direct and indirect relationships is a challenge. To combat these spurious interactions an algorithm that leverages functional enrichment in biological networks was developed. Here, functional enrichment refers to two or three node functional motifs that are ubiquitous in biological networks. The algorithm developed removes edges from an existing network based on that edge’s involvement in functional motifs relative to every other edge’s involvement. In this work, the application of this algorithm was explored using real-world clinical disease networks. Furthermore, a software package was developed to identify an edge’s membership in functional motifs with respect to the network being explored. The tools developed in this work are the first to critically analyze an edge’s relationship to functional motifs in terms of network inclusion. Therefore, the principles outlined in this work can be employed in future works aimed at removing spurious edges. These principles will also produce higher quality biological networks for the understanding of disease pathology and the development of more effective treatment options