Computational phosphorylation network reconstruction: methods and resources.
The succession of protein activation and deactivation mediated by phosphorylation and dephosphorylation events constitutes a key mechanism of molecular information transfer in cellular systems. To deduce the details of those molecular information cascades and networks has been a central goal pursued by both experimental and computational approaches. Many computational network reconstruction methods employing an array of different statistical learning methods have been developed to infer phosphorylation networks based on different types of molecular data sets such as protein sequence, protein structure, or phosphoproteomics data. In this chapter, different computational network inference methods and resources for biological network reconstruction with a particular focus on phosphorylation networks are surveyed.
Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, 14476, Germany, duan@embl.de.
Guangyou
G
Duan
Dirk
D
Walther
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D010766:Phosphorylation; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D040901:Proteomics / Q000379:methods; D015398:Signal Transduction
D011506:Proteins