How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

Modular organisation of interaction networks based on asymptotic dynamics

This paper investigates questions related to the modularity in discrete models of biological interaction networks. We develop a theoretical framework based on the analysis of their asymptotic dynamics. More precisely, we exhibit formal conditions under which agents of interaction networks can be grouped into modules. As a main result, we show that the usual decomposition in strongly connected components fulfils the conditions of being a modular organisation. Furthermore, we point out that our framework enables a finer analysis providing a decomposition in elementary modules

Connecting Seed Lists of Mammalian Proteins Using Steiner Trees

Multivariate experiments and genomics studies applied to mammalian cells often produce lists of genes or proteins altered under treatment/disease vs. control/normal conditions. Such lists can be identified in known protein-protein interaction networks to produce subnetworks that “connect” the genes or proteins from the lists. Such subnetworks are valuable for biologists since they can suggest regulatory mechanisms that are altered under different conditions. Often such subnetworks are overloaded with links and nodes resulting in connectivity diagrams that are illegible due to edge overlap. In this study, we attempt to address this problem by implementing an approximation to the Steiner Tree problem to connect seed lists of mammalian proteins/genes using literature-based protein-protein interaction networks. To avoid over-representation of hubs in the resultant Steiner Trees we assign a cost to Steiner Vertices based on their connectivity degree. We applied the algorithm to lists of genes commonly mutated in colorectal cancer to demonstrate the usefulness of this approach

MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model.

peer-reviewedBovine mastitis is an inflammation-driven disease of the bovine mammary gland that costs the global dairy industry several billion dollars per annum. Because disease susceptibility is a multi-factorial complex phenotype, an integrative biology approach is required to dissect the molecular networks involved. Here, we report such an approach, using next generation sequencing combined with advanced network and pathway biology methods to simultaneously profile mRNA and miRNA expression at multiple time-points (0, 12, 24, 36 and 48h) in both milk and blood FACS-isolated CD14+ monocytes from animals infected in vivo with Streptococcus uberis. More than 3,700 differentially expressed (DE) genes were identified in milk-isolated monocytes (MIMs), a key immune cell recruited to the site of infection during mastitis. Up-regulated genes were significantly enriched for inflammatory pathways, while down-regulated genes were enriched for non-glycolytic metabolic pathways. Monocyte transcriptional changes in the blood, however, were more subtle but highlighted the impact of this infection systemically. Genes up-regulated in blood-isolated-monocytes (BIMs) showed a significant association with interferon and chemokine signalling. Furthermore, twenty-six miRNAs were differentially expressed in MIMs and three in BIMs. Pathway analysis revealed that predicted targets of down-regulated miRNAs were highly enriched for roles in innate immunity (FDR < 3.4E-8) in particular TLR signalling, while up-regulated miRNAs preferentially targeted genes involved in metabolism. We conclude that during S. uberis infection miRNAs are key amplifiers of monocyte inflammatory response networks and repressors of several metabolic pathways.This study was funded in part by Teagasc RMIS 6018 and United States Department of Agriculture ARS funding 3625-32000-102-00. NL is supported by a Teagasc Walsh Fellowship

Differential analysis of biological networks

In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest

Elucidation of functional consequences of signalling pathway interactions

<p>Abstract</p> <p>Background</p> <p>A great deal of data has accumulated on signalling pathways. These large datasets are thought to contain much implicit information on their molecular structure, interaction and activity information, which provides a picture of intricate molecular networks believed to underlie biological functions. While tremendous advances have been made in trying to understand these systems, how information is transmitted within them is still poorly understood. This ever growing amount of data demands we adopt powerful computational techniques that will play a pivotal role in the conversion of mined data to knowledge, and in elucidating the topological and functional properties of protein - protein interactions.</p> <p>Results</p> <p>A computational framework is presented which allows for the description of embedded networks, and identification of common shared components thought to assist in the transmission of information within the systems studied. By employing the graph theories of network biology - such as degree distribution, clustering coefficient, vertex betweenness and shortest path measures - topological features of protein-protein interactions for published datasets of the p53, nuclear factor kappa B (NF-κB) and G1/S phase of the cell cycle systems were ascertained. Highly ranked nodes which in some cases were identified as connecting proteins most likely responsible for propagation of transduction signals across the networks were determined. The functional consequences of these nodes in the context of their network environment were also determined. These findings highlight the usefulness of the framework in identifying possible combination or links as targets for therapeutic responses; and put forward the idea of using retrieved knowledge on the shared components in constructing better organised and structured models of signalling networks.</p> <p>Conclusion</p> <p>It is hoped that through the data mined reconstructed signal transduction networks, well developed models of the published data can be built which in the end would guide the prediction of new targets based on the pathway's environment for further analysis. Source code is available upon request.</p

Heuristics for simulated annealing search of active sub-networks in bio-molecular interaction networks

Different kinds of ‘omics’ data for several organisms and bio-molecular interaction networks (e.g. reconstructed networks of biochemical reactions and protein-protein physical interactions) are becoming very common nowadays. These bio-molecular networks are being used as a platform to integrate genome-scale ‘omics’ datasets. Identification of sub-networks in these large networks that show maximum collective response to a perturbation is one the interesting problems to solve by using an integrative analysis. Sub-networks can be hypothesized to represent significant collective biological activity due to the underlying interactions between the bio-molecules. The biological activity can be estimated in several ways- for example coordinated change in the expression level (e.g. mRNA). Identifying these regions reduce complexity of the network to be analyzed in greater detail by revealing the regions that are perturbed by a conditionremoving the interactions that are potentially false-positive and not related to the response under study. As the simulated annealing does not guarantee to find the global optimum and may lead to an incomplete picture of the biological phenomenon, we report a method to estimate the theoretical optimal score curve. The simulated annealing algorithm (SA) used in this study is a slightly modified version of the algorithm by Ideker et al.. Each node in the graph is associated with a binary variable turning the node visible or invisible and therefore inducing several sub-graphs. In the standard formulation, the initial solution is obtained by randomly attributing 0 or 1 to the nodes of the graph. Based in concepts described above, we propose an alternative initialization method to improve the performance of the simulated annealing algorithm.Systems Biology as a Driver for Industrial Biotechnology (SYSINBIO