title: Linear matrix inequalities approach to reconstruction of biological networks.
abstract: The general problem of reconstructing a biological interaction network from temporal evolution data is tackled via an approach based on dynamical linear systems identification theory. A novel algorithm, based on linear matrix inequalities, is devised to infer the interaction network. This approach allows to directly taking into account, within the optimisation procedure, the a priori available knowledge of the biological system. The effectiveness of the proposed algorithm is statistically validated, by means of numerical tests, demonstrating how the a priori knowledge positively affects the reconstruction performance. A further validation is performed through an in silico biological experiment, exploiting the well-assessed cell-cycle model of fission yeast developed by Novak and Tyson.
authors.affiliation: School of Computer and Biomedical Engineering, Universià degli Studi Magna Grxcia di Catanzaro, Viale Europa, Campus di Germaneto, Catanzaro 88100, Italy. carlo.cosentino@unicz.it
authors.forename: C
authors.initials: C
authors.lastname: Cosentino
authors.forename: W
authors.initials: W
authors.lastname: Curatola
authors.forename: F
authors.initials: F
authors.lastname: Montefusco
authors.forename: M
authors.initials: M
authors.lastname: Bansal
authors.forename: D
authors.initials: D
authors.lastname: di Bernardo
authors.forename: F
authors.initials: F
authors.lastname: Amato
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016014:Linear Models; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome