title: Motif statistics of artificially evolved and biological networks.
abstract: Topological features of gene regulatory networks can be successfully reproduced by a model population evolving under selection for short dynamical attractors. The evolved population of networks exhibit motif statistics, summarized by significance profiles, which closely match those of E. coli, S. cerevsiae, and B. subtilis, in such features as the excess of linear motifs and feedforward loops, and deficiency of feedback loops. The slow relaxation to stasis is a hallmark of a rugged fitness landscape, with independently evolving populations exploring distinct valleys strongly differing in network properties.
authors.affiliation: Department of Physics Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey.
authors.forename: Burçin
authors.initials: B
authors.lastname: Danacı
authors.affiliation: Department of Physics, University of Colorado, Boulder, Colorado 80309, USA.
authors.forename: Mehmet Ali
authors.initials: MA
authors.lastname: Anıl
authors.affiliation: Department of Physics Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey.
authors.forename: Ayşe
authors.initials: A
authors.lastname: Erzan
mesh_terms: D001419:Bacteria / Q000378:metabolism*; D001426:Bacterial Proteins / Q000378:metabolism*; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D053263:Gene Regulatory Networks / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D015398:Signal Transduction / Q000502:physiology
chemical_list: D001426:Bacterial Proteins