title: Discriminating direct and indirect connectivities in biological networks.
abstract: Reverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks.
authors.affiliation: Bioengineering Department, University of Texas at Dallas, Richardson, TX 75080; Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080;
authors.forename: Taek
authors.initials: T
authors.lastname: Kang
authors.affiliation: Bioengineering Department, University of Texas at Dallas, Richardson, TX 75080; Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080;
authors.forename: Richard
authors.initials: R
authors.lastname: Moore
authors.affiliation: Bioengineering Department, University of Texas at Dallas, Richardson, TX 75080; Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080;
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Mathematics and Center for Quantitative Biology, Rutgers-The State University of New Jersey, Piscataway, NJ 08854; bleris@utdallas.edu eduardo.sontag@rutgers.edu.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Sontag
authors.affiliation: Bioengineering Department, University of Texas at Dallas, Richardson, TX 75080; Center for Systems Biology, University of Texas at Dallas, Richardson, TX 75080; Electrical Engineering Department, University of Texas at Dallas, Richardson, TX 75080 bleris@utdallas.edu eduardo.sontag@rutgers.edu.
authors.forename: Leonidas
authors.initials: L
authors.lastname: Bleris
mesh_terms: D000465:Algorithms; D057809:HEK293 Cells; D006801:Humans; D008954:Models, Biological; D058615:Synthetic Biology
keywords: direct and indirect connectivities; human cells; nonparametric resampling; reverse engineering; synthetic biology