title: Reverse engineering of dynamic networks.
abstract: We consider the problem of reverse-engineering dynamic models of biochemical networks from experimental data using polynomial dynamic systems. In earlier work, we developed an algorithm to identify minimal wiring diagrams, that is, directed graphs that represent the causal relationships between network variables. Here we extend this algorithm to identify a most likely dynamic model from the set of all possible dynamic models that fit the data over a fixed wiring diagram. To illustrate its performance, the method is applied to simulated time-course data from a published gene regulatory network in the fruitfly Drosophila melanogaster.
authors.affiliation: Mathematical Biosciences Institute, The Ohio State University, Columbus USA.
authors.forename: B
authors.initials: B
authors.lastname: Stigler
authors.forename: A
authors.initials: A
authors.lastname: Jarrah
authors.forename: M
authors.initials: M
authors.lastname: Stillman
authors.forename: R
authors.initials: R
authors.lastname: Laubenbacher
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome