Abstract
BackgroundActive module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states.MethodsA prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules.ResultsApplication of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation.ConclusionsIntegrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.
Highlights
Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states
Many studies have been performed on the construction of networks from biological systems and the structural and functional features that may respond to related biological information
Network construction methods are varied from calculating pair-wise correlation coefficient of expression data, filtering from existing interaction database, or integrated approaches based on both expression data and metabolic models
Summary
Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. With the development of high-throughput data collection technologies, vast amounts of omics data that cover different species and different levels of biological activities have accumulated exponentially. These varied omics data, including the genome sequencing data (genomics), genome-wide expression profiles (transcriptomics), and protein abundances data (proteomics), provide valuable information concerning the intrinsic mechanisms underlining biological processes. Network construction methods are varied from calculating pair-wise correlation coefficient of expression data (correlation network [5]), filtering from existing interaction database (protein-protein interaction network [6,7,8,9]), or integrated approaches based on both expression data and metabolic models (tissue specific metabolic network [10]).
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