An algorithm for score aggregation over causal biological networks based on random walk sampling

Vasilyev, Dmitry; Thomson, Timothy M.; Frushour, Brian P.; Martin, Florian; Sewer, Alain

doi:10.1186/1756-0500-7-516

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Some of them utilize Floyd-Warshall algorithm [ 34 ] to compute shortest paths between network’s components, with the aim of representing the underlying molecular mechanisms of genetic interactions [ 28 ]. Other approaches, such as the SST algorithm described in [ 29 ], focus on calculating Spanning Trees to model significant quantities in biochemical networks. While these approaches address different problems, ours addresses the network reconstruction based on proteomic data and prior knowledge of protein connectivity.…”

Section: Introductionmentioning

confidence: 99%

Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory

et al. 2015

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Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights.

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Section: Introductionmentioning

confidence: 99%

Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory

et al. 2015

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“…This property means that the edge-based relative sign between any two nodes must be unambiguous (i.e., must not depend of the specific path relating the two nodes). As most networks do not satisfy this condition (e.g., negative feedback loops are not causally consistent), the aggregation option would require additional processing to be operative [32].…”

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confidence: 99%

Developing Network-Based Systems Toxicology by Combining Transcriptomics Data with Literature Mining and Multiscale Quantitative Modeling

Sewer¹,

Talikka²,

Martin³

et al. 2018

Bioinformatics in the Era of Post Genomics and Big Data

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We describe how the genome-wide transcriptional profiling can be used in networkbased systems toxicology, an approach leveraging biological networks for assessing the health risks of exposure to chemical compounds. Driven by the technological advances changing the ways in which data are generated, systems toxicology has allowed traditional toxicity endpoints to be enhanced with far deeper levels of analysis. In combination, new experimental and computational methods have offered the potential for more effective, efficient, and reliable toxicological testing strategies. We illustrate these advances by the "network perturbation amplitude" methodology that quantifies the effects of exposure treatments on biological mechanisms represented by causal networks. We also describe recent developments in the assembly of highquality causal biological networks using crowdsourcing and text-mining approaches. We further show how network-based approaches can be integrated into the multiscale modeling framework of response to toxicological exposure. Finally, we combine biological knowledge assembly and multiscale modeling to report on the promising developments of the "quantitative adverse outcome pathway" concept, which spans multiple levels of biological organization, from molecules to population, and has direct relevance in the context of the "Toxicity Testing in the 21st century" vision of the US National Research Council.

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Quantifying the Biological Impact of Active Substances Using Causal Network Models

Sewer

Martin

Schlage

et al. 2015

Methods in Pharmacology and Toxicology

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An algorithm for score aggregation over causal biological networks based on random walk sampling

Cited by 4 publications

References 22 publications

Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory

Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory

Developing Network-Based Systems Toxicology by Combining Transcriptomics Data with Literature Mining and Multiscale Quantitative Modeling

Quantifying the Biological Impact of Active Substances Using Causal Network Models

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