Multiscale Characterization of Signaling Network Dynamics through Features
| Autor: | Enrico Capobianco, Antonella Travaglione, Elisabetta Marras |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
Saccharomyces cerevisiae Proteins Transcription Genetic MAP Kinase Signaling System Inference Saccharomyces cerevisiae Biology Machine learning computer.software_genre Sensitivity and Specificity Interactome Pheromones Wavelet Gene Expression Regulation Fungal Protein Interaction Mapping Genetics Phosphorylation Molecular Biology Network model Stochastic Processes business.industry Computational Biology Reproducibility of Results Modular design MAP Kinase Kinase Kinases Variety (cybernetics) Complement (complexity) Computational Mathematics Artificial intelligence Data mining business computer Algorithms Biological network Protein Binding Signal Transduction |
| Zdroj: | Statistical Applications in Genetics and Molecular Biology. 10 |
| ISSN: | 1544-6115 2194-6302 |
| DOI: | 10.2202/1544-6115.1657 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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