Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous Abstract"Historic exposure to herbivores, not constitutive traits, explains plant tolerance to herbivory in the case of two Medicago species (Fabaceae)"    Next AbstractIdentification of aroma-active volatiles in banana Terra spirit using multidimensional gas chromatography with simultaneous mass spectrometry and olfactometry detection »

Stat Appl Genet Mol Biol


Title:Multiscale characterization of signaling network dynamics through features
Author(s):Capobianco E; Marras E; Travaglione A;
Address:CRS4 Bioinformatics
Journal Title:Stat Appl Genet Mol Biol
Year:2011
Volume:20111120
Issue:1
Page Number: -
DOI: 10.2202/1544-6115.1657
ISSN/ISBN:1544-6115 (Electronic) 1544-6115 (Linking)
Abstract:"Inference methods applied to biological networks suffer from a main criticism: as the latter reflect associations measured under static conditions, an evaluation of the underlying modular organization can be biologically meaningful only if the dynamics can also be taken into consideration. The same limitation is present in protein interactome networks. Given the substantial uncertainty characterizing protein interactions, we identify at least three aspects that must be considered for inference purposes: 1. Coverage, which for most organisms is only partial; 2. Stochasticity, affecting both the high-throughput experimental and the computational settings from which the interactions are determined, and leading to suboptimal measurement accuracy; 3. Information variety, due to the heterogeneity of technological and biological sources generating the data. Consequently, advances in inference methods require adequate treatment of both system uncertainty and dynamical aspects. Feasible solutions are often made possible by data (omic) integration procedures that complement the experimental design and the computational approaches for network modeling. We present a multiscale stochastic approach to deal with protein interactions involved in a well-known signaling network, and show that based on some topological network features, it is possible to identify timescales (or resolutions) that characterize complex pathways"
Keywords:"Algorithms Computational Biology/*methods Gene Expression Regulation, Fungal MAP Kinase Kinase Kinases/genetics/metabolism MAP Kinase Signaling System Pheromones/metabolism Phosphorylation Protein Binding Protein Interaction Mapping/*methods Reproducibili;"
Notes:"MedlineCapobianco, Enrico Marras, Elisabetta Travaglione, Antonella eng Research Support, Non-U.S. Gov't Germany 2011/01/01 Stat Appl Genet Mol Biol. 2011 Nov 20; 10(1):/j/sagmb.2011.10.issue-1/1544-6115.1657/1544-6115.1657.xml. doi: 10.2202/1544-6115.1657"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 19-12-2024