Jennifer E Dent1, Valentina Devescovi2, Han Li2, Pietro Di Lena2, Youtao Lu2, Yuanhua Liu2, Christine Nardini2. 1. Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, People's Republic of China, Quintiles, Global Biostatistics, Reading, Berkshire, UK and Department of Computer Science and Engineering - DISI, University of Bologna, Bologna, Italy Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, People's Republic of China, Quintiles, Global Biostatistics, Reading, Berkshire, UK and Department of Computer Science and Engineering - DISI, University of Bologna, Bologna, Italy. 2. Group of Clinical Genomic Networks, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, People's Republic of China, Quintiles, Global Biostatistics, Reading, Berkshire, UK and Department of Computer Science and Engineering - DISI, University of Bologna, Bologna, Italy.
Abstract
MOTIVATION: Mechanotransduction--the ability to output a biochemical signal from a mechanical input--is related to the initiation and progression of a broad spectrum of molecular events. Yet, the characterization of mechanotransduction lacks some of the most basic tools as, for instance, it can hardly be recognized by enrichment analysis tools, nor could we find any pathway representation. This greatly limits computational testing and hypothesis generation on mechanotransduction biological relevance and involvement in disease or physiological mechanisms. RESULTS: We here present a molecular map of mechanotransduction, built in CellDesigner to warrant that maximum information is embedded in a compact network format. To validate the map's necessity we tested its redundancy in comparison with existing pathways, and to estimate its sufficiency, we quantified its ability to reproduce biological events with dynamic simulations, using Signaling Petri Networks. AVAILABILITY AND IMPLEMENTATION: SMBL language map is available in the Supplementary Data: core_map.xml, basic_map.xml. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Mechanotransduction--the ability to output a biochemical signal from a mechanical input--is related to the initiation and progression of a broad spectrum of molecular events. Yet, the characterization of mechanotransduction lacks some of the most basic tools as, for instance, it can hardly be recognized by enrichment analysis tools, nor could we find any pathway representation. This greatly limits computational testing and hypothesis generation on mechanotransduction biological relevance and involvement in disease or physiological mechanisms. RESULTS: We here present a molecular map of mechanotransduction, built in CellDesigner to warrant that maximum information is embedded in a compact network format. To validate the map's necessity we tested its redundancy in comparison with existing pathways, and to estimate its sufficiency, we quantified its ability to reproduce biological events with dynamic simulations, using Signaling Petri Networks. AVAILABILITY AND IMPLEMENTATION: SMBL language map is available in the Supplementary Data: core_map.xml, basic_map.xml. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.