title: Gene knockout experiments to quantify a G2/M genetic network simulation for mammary cancer susceptibility.
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.
authors.affiliation: Bioinformatics and Computational Biology, University of Idaho, Moscow, ID 83843, USA. ank2192@uidaho.edu
authors.forename: Armand
authors.initials: A
authors.lastname: Bankhead
authors.forename: Nancy S
authors.initials: NS
authors.lastname: Magnuson
authors.forename: Robert B
authors.initials: RB
authors.lastname: Heckendorn
mesh_terms: D000818:Animals; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D002453:Cell Cycle; D002455:Cell Division; D003198:Computer Simulation; D005260:Female; D016195:G2 Phase; D017353:Gene Deletion; D020022:Genetic Predisposition to Disease; D006801:Humans; D051379:Mice; D018345:Mice, Knockout; D023421:Models, Animal; D008957:Models, Genetic