Pathway discovery in metabolic networks by subgraph extraction

Motivation: Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to extract relevant pathways from metabolic networks. Although these approaches have been adapted to metabolic networks, they are generic enough to be adjusted to other biological networks as well

Mining Representative Unsubstituted Graph Patterns Using Prior Similarity Matrix

One of the most powerful techniques to study protein structures is to look for recurrent fragments (also called substructures or spatial motifs), then use them as patterns to characterize the proteins under study. An emergent trend consists in parsing proteins three-dimensional (3D) structures into graphs of amino acids. Hence, the search of recurrent spatial motifs is formulated as a process of frequent subgraph discovery where each subgraph represents a spatial motif. In this scope, several efficient approaches for frequent subgraph discovery have been proposed in the literature. However, the set of discovered frequent subgraphs is too large to be efficiently analyzed and explored in any further process. In this paper, we propose a novel pattern selection approach that shrinks the large number of discovered frequent subgraphs by selecting the representative ones. Existing pattern selection approaches do not exploit the domain knowledge. Yet, in our approach we incorporate the evolutionary information of amino acids defined in the substitution matrices in order to select the representative subgraphs. We show the effectiveness of our approach on a number of real datasets. The results issued from our experiments show that our approach is able to considerably decrease the number of motifs while enhancing their interestingness

Approximation algorithms for node-weighted prize-collecting Steiner tree problems on planar graphs

We study the prize-collecting version of the Node-weighted Steiner Tree problem (NWPCST) restricted to planar graphs. We give a new primal-dual Lagrangian-multiplier-preserving (LMP) 3-approximation algorithm for planar NWPCST. We then show a ($2.88 + \epsilon$)-approximation which establishes a new best approximation guarantee for planar NWPCST. This is done by combining our LMP algorithm with a threshold rounding technique and utilizing the 2.4-approximation of Berman and Yaroslavtsev for the version without penalties. We also give a primal-dual 4-approximation algorithm for the more general forest version using techniques introduced by Hajiaghay and Jain

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

This article has been made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 Pey et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. Results: We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. Conclusions: A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.Basque Governmen

PheNetic : network-based interpretation of molecular profiling data

Molecular profiling experiments have become standard in current wet-lab practices. Classically, enrichment analysis has been used to identify biological functions related to these experimental results. Combining molecular profiling results with the wealth of currently available interactomics data, however, offers the opportunity to identify the molecular mechanism behind an observed molecular phenotype. In this paper, we therefore introduce 'PheNetic', a userfriendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatorymechanism that gave explains to the observed molecular phenotype or for the pathways (in) activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser. PheNetic's method and use are illustrated using an example analysis of differential expression results of ampicillin treated Escherichia coli cells. The PheNetic web service is available at http://bioinformatics.intec.ugent.be/phenetic/

Disease Gene Interaction Pathways: A Potential Framework for How Disease Genes Associate by Disease-Risk Modules

BACKGROUND: Disease genes that interact cooperatively play crucial roles in the process of complex diseases, yet how to analyze and represent their associations is still an open problem. Traditional methods have failed to represent direct biological evidences that disease genes associate with each other in the pathogenesis of complex diseases. Molecular networks, assumed as 'a form of biological systems', consist of a set of interacting biological modules (functional modules or pathways) and this notion could provide a promising insight into deciphering this topic. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we hypothesized that disease genes might associate by virtue of the associations between biological modules in molecular networks. Then we introduced a novel disease gene interaction pathway representation and analysis paradigm, and managed to identify the disease gene interaction pathway for 61 known disease genes of coronary artery disease (CAD), which contained 46 disease-risk modules and 182 interaction relationships. As demonstrated, disease genes associate through prescribed communication protocols of common biological functions and pathways. CONCLUSIONS/SIGNIFICANCE: Our analysis was proved to be coincident with our primary hypothesis that disease genes of complex diseases interact with their neighbors in a cooperative manner, associate with each other through shared biological functions and pathways of disease-risk modules, and finally cause dysfunctions of a series of biological processes in molecular networks. We hope our paradigm could be a promising method to identify disease gene interaction pathways for other types of complex diseases, affording additional clues in the pathogenesis of complex diseases

Path finding methods accounting for stoichiometry in metabolic networks

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks