Augmented sparse reconstruction of protein signaling networks.
The problem of reconstructing and identifying intracellular protein signaling and biochemical networks is of critical importance in biology. We propose a mathematical approach called augmented sparse reconstruction for the identification of links among nodes of ordinary differential equation (ODE) networks, given a small set of observed trajectories with various initial conditions. As a test case, the method is applied to the epidermal growth factor receptor (EGFR) driven signaling cascade, a well-studied and clinically important signaling network. Our method builds a system of representation from a collection of trajectory integrals, selectively attenuating blocks of terms in the representation. The system of representation is then augmented with random vectors, and l(1) minimization is used to find sparse representations for the dynamical interactions of each node. After showing the performance of our method on a model of the EGFR protein network, we sketch briefly the potential future therapeutic applications of this approach.
Napoletani
D
D
Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA 20110, USA. dnapolet@gmu.edu
Sauer
T
T
Struppa
D C
DC
Petricoin
E
E
Liotta
L
L
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004815:Epidermal Growth Factor / Q000378:metabolism; D066246:ErbB Receptors / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D011506:Proteins; D004815:Epidermal Growth Factor; D066246:ErbB Receptors