Reverse Engineering Molecular Hypergraphs
| Autor: | Craig Estep, David J. Badger, Christopher L. Poirel, Ahsanur Rahman, T. M. Murali |
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| Rok vydání: | 2013 |
| Předmět: |
Reverse engineering
Saccharomyces cerevisiae Proteins Theoretical computer science Systems biology Saccharomyces cerevisiae Biology Network topology computer.software_genre Models Biological Article Protein Interaction Mapping Genetics Computer Simulation Cluster analysis Applied Mathematics Molecular biophysics Data set ComputingMethodologies_PATTERNRECOGNITION Noise (video) Precision and recall Algorithm computer Algorithms Signal Transduction MathematicsofComputing_DISCRETEMATHEMATICS Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 10:1113-1124 |
| ISSN: | 1545-5963 |
| DOI: | 10.1109/tcbb.2013.71 |
| Popis: | Analysis of molecular interaction networks is pervasive in systems biology. This research relies almost entirely on graphs for modeling interactions. However, edges in graphs cannot represent multiway interactions among molecules, which occur very often within cells. Hypergraphs may be better representations for networks having such interactions, since hyperedges can naturally represent relationships among multiple molecules. Here, we propose using hypergraphs to capture the uncertainty inherent in reverse engineering gene-gene networks. Some subsets of nodes may induce highly varying subgraphs across an ensemble of networks inferred by a reverse engineering algorithm. We provide a novel formulation of hyperedges to capture this uncertainty in network topology. We propose a clustering-based approach to discover hyperedges. We show that our approach can recover hyperedges planted in synthetic data sets with high precision and recall, even for moderate amount of noise. We apply our techniques to a data set of pathways inferred from genetic interaction data in S. cerevisiae related to the unfolded protein response. Our approach discovers several hyperedges that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns. We also show that these complexes are not discovered by an algorithm that computes frequent and dense subgraphs. |
| Databáze: | OpenAIRE |
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