Virginia Tech. Department of Biological SciencesVirginia Bioinformatics InstituteVirginia Tech. Department of Computer ScienceVirginia Tech. Department of Genetics, Bioinformatics and Computational BiologyVirginia Tech. Department of PhysicsVirginia Tech. Center for Modeling Immunity to Enteric Pathogens. Nutritional Immunology and Molecular Medicine LaboratoryUniversity of Arizona. Department of Molecular & Cellular BiologyBeijing Computational Science Research CenterMondal, DebasishDougherty, Edward T.Mukhopadhyay, AbhishekCarbo, AdriaYao, GuangXing, JianhuaCsikász-Nagy, Attila2016-02-162016-02-162014-08-29Mondal D, Dougherty E, Mukhopadhyay A, Carbo A, Yao G, et al. (2014) Systematic Reverse Engineering of Network Topologies: A Case Study of Resettable Bistable Cellular Responses. PLoS ONE 9(8): e105833. doi:10.1371/journal.pone.01058331932-6203http://hdl.handle.net/10919/64827A 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 S1.12 p.application/pdfen-USCreative Commons Attribution 4.0 InternationalNetwork motifsTopologyEngineering and technologyGene regulatory networksRandom walkComputer softwareAlgorithmsGenetic networksArticle - RefereedMondal, DebasishDougherty, Edward T.Mukhopadhyay, AbhishekCarbo, AdriaYao, GuangXing, Jianhuhttp://journals.plos.org/plosone/article?id=10.1371/journal.pone.0105833https://doi.org/10.1371/journal.pone.010583398