title: Self-correcting networks: function, robustness, and motif distributions in biological signal processing.
abstract: Statistical properties of large ensembles of networks, all designed to have the same functions of signal processing, but robust against different kinds of perturbations, are analyzed. We find that robustness against noise and random local damage plays a dominant role in determining motif distributions of networks and may underlie their classification into network superfamilies.
authors.affiliation: Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany.
authors.forename: Pablo
authors.initials: P
authors.lastname: Kaluza
authors.forename: Martin
authors.initials: M
authors.lastname: Vingron
authors.forename: Alexander S
authors.initials: AS
authors.lastname: Mikhailov
mesh_terms: D000465:Algorithms; D000818:Animals; D005075:Biological Evolution; D001695:Biology / Q000379:methods*; D001703:Biophysics / Q000379:methods*; D017173:Caenorhabditis elegans; D003198:Computer Simulation; D005786:Gene Expression Regulation; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D020543:Proteome / Q000502:physiology; D015398:Signal Transduction / Q000502:physiology*; D049490:Systems Biology; D013816:Thermodynamics
chemical_list: D020543:Proteome