title: Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses.
abstract: A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.
authors.affiliation: Department of Biological Sciences, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
authors.forename: Debasish
authors.initials: D
authors.lastname: Mondal
authors.affiliation: Department of Genetics, Bioinformatics and Computational Biology Ph. D program, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
authors.forename: Edward
authors.initials: E
authors.lastname: Dougherty
authors.affiliation: Department of Physics, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America; Department of Computer Science, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
authors.forename: Abhishek
authors.initials: A
authors.lastname: Mukhopadhyay
authors.affiliation: Department of Genetics, Bioinformatics and Computational Biology Ph. D program, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory and Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
authors.forename: Adria
authors.initials: A
authors.lastname: Carbo
authors.affiliation: Department of Molecular & Cellular Biology, University of Arizona, Tucson, Arizona, United States of America.
authors.forename: Guang
authors.initials: G
authors.lastname: Yao
authors.affiliation: Department of Biological Sciences, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America; Department of Physics, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America; Beijing Computational Science Research Center, Beijing, China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xing
mesh_terms: D000465:Algorithms; D000818:Animals; D057005:Bioengineering / Q000379:methods*; D049109:Cell Proliferation / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D050684:E2F Transcription Factors / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D015203:Reproducibility of Results; D016160:Retinoblastoma Protein / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics; D049490:Systems Biology / Q000379:methods*
chemical_list: D050684:E2F Transcription Factors; D016160:Retinoblastoma Protein