- Article -
| In Silico Biology 6, 0017 (2006); ©2006, Bioinformation Systems e.V. |
Gene knockout experiments to quantify a G2/M genetic network simulation for mammary cancer susceptibility
Armand Bankhead III 1*, Nancy S. Magnuson 2, and Robert B. Heckendorn 1
1 Bioinformatics and Computational Biology; University of Idaho, Moscow, ID 83843
2 School of Molecular Biosciences; Washington State University, Pullman, Washington 99164
* Corresponding author
Email: bank2192@uidaho.edu
Edited by H. Michael; received December 02, 2005; revised and accepted March 21, 2006; published April 22, 2006
Abstract
A G2/M genetic network simulation is trained with tumor incidence data from knockout experiments. The genetic network is implemented using a neural network; knockout genotypes are simulated by removing nodes in the neural network. Two analyses are used to interpret the resulting network weights. We use a novel approach of fixing the network topology that allows knockout TSG (tumor suppressor gene) data from multiple studies to overlap and indirectly inform one another. The trained simulation is validated by reproducing qualitative mammary cancer susceptibilities of ATM, BRCA1, and p53 TSGs. The work described is valuable because it allows TSG mammary cancer susceptibility to be quantified using genetic network topology and in vivo knockout data.
Keywords: genetic network, neural network, mammary cancer, susceptibility, ATM, BRCA1, p53, mouse model, G2/M, cell cycle, knockout, simulation, tumor suppressor gene, TSG, cancer modeling, regulatory pathway, signal transduction