Robert B Heckendorn | Search PubMed

PubMed2006

Gene knockout experiments to quantify a G2/M genetic network simulation for mammary cancer susceptibility.

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.

Animals↗

PubMed2002

Embedded landscapes.

In this paper we introduce embedded landscapes as an extension of NK landscapes and MAXSAT problems. This extension is valid for problems where the representation can be expressed as a simple sum of subfunctions over subsets of the representation domain. This encompasses many additive constraint problems and problems expressed as the interaction of subcomponents, where the critical features of the subcomponents are represented by subsets of bits in the domain. We show that embedded landscapes of fixed maximum epistasis K are exponentially sparse in epistatic space with respect to all possible functions. We show we can compute many important statistical features of these functions in polynomial time including all the epistatic interactions and the statistical moments of hyperplanes about the function mean and hyperplane mean. We also show that embedded landscapes of even small fixed K can be NP-complete. We can conclude that knowing the epistasis and many of the hyperplane statistics is not enough to solve the exponentially difficult part of these general problems and that the difficulty of the problem lies not in the epistasis itself but in the interaction of the epistatic parts.

Algorithms↗