title: Homologous control of protein signaling networks.
abstract: In a previous paper we introduced a method called augmented sparse reconstruction (ASR) that identifies links among nodes of ordinary differential equation networks, given a small set of observed trajectories with various initial conditions. The main purpose of that technique was to reconstruct intracellular protein signaling networks. In this paper we show that a recursive augmented sparse reconstruction generates artificial networks that are homologous to a large, reference network, in the sense that kinase inhibition of several reactions in the network alters the trajectories of a sizable number of proteins in comparable ways for reference and reconstructed networks. We show this result using a large in-silico model of the epidermal growth factor receptor (EGF-R) driven signaling cascade to generate the data used in the reconstruction algorithm. The most significant consequence of this observed homology is that a nearly optimal combinatorial dosage of kinase inhibitors can be inferred, for many nodes, from the reconstructed network, a result potentially useful for a variety of applications in personalized medicine.
authors: Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA 20110, USA. dnapolet@gmu.edu
authors: D
authors: D
authors: Napoletani
authors: M
authors: M
authors: Signore
authors: T
authors: T
authors: Sauer
authors: L
authors: L
authors: Liotta
authors: E
authors: E
authors: Petricoin
mesh_terms: D000465:Algorithms; D047428:Protein Kinase Inhibitors / Q000378:metabolism; D011506:Proteins / Q000378:metabolism*; D012015:Reference Standards; D015398:Signal Transduction
chemical_list: D047428:Protein Kinase Inhibitors; D011506:Proteins