Hematopoietic gene promoters subjected to a group-combinatorial study of DNA samples: identification of a megakaryocytic selective DNA signature

Identification of common sub-sequences for a group of functionally related DNA sequences can shed light on the role of such elements in cell-specific gene expression. In the megakaryocytic lineage, no one single unique transcription factor was described as linage specific, raising the possibility that a cluster of gene promoter sequences presents a unique signature. Here, the megakaryocytic gene promoter group, which consists of both human and mouse 5' non-coding regions, served as a case study. A methodology for group-combinatorial search has been implemented as a customized software platform. It extracts the longest common sequences for a group of related DNA sequences and allows for single gaps of varying length, as well as double- and multiple-gap sequences. The results point to common DNA sequences in a group of genes that is selectively expressed in megakaryocytes, and which does not appear in a large group of control, random and specific sequences. This suggests a role for a combination of these sequences in cell-specific gene expression in the megakaryocytic lineage. The data also point to an intrinsic cross-species difference in the organization of 5' non-coding sequences within the mammalian genomes. This methodology may be used for the identification of regulatory sequences in other lineages.

On the Effectiveness of Genetic Search in Combinatorial Optimization

In this paper, we study the efficacy of genetic algorithms in the context of combinatorial optimization. In particular, we isolate the effects of cross-over, treated as the central component of genetic search. We show that for problems of nontrivial size and difficulty, the contribution of cross-over search is marginal, both synergistically when run in conjunction with mutation and selection, or when run with selection alone, the reference point being the search procedure consisting of just mutation and selection. The latter can be viewed as another manifestation of the Metropolis process. Considering the high computational cost of maintaining a population to facilitate cross-over search, its marginal benefit renders genetic search inferior to its singleton-population counterpart, the Metropolis process, and by extension, simulated annealing. This is further compounded by the fact that many problems arising in practice may inherently require a large number of state transitions for a near-optimal solution to be found, making genetic search infeasible given the high cost of computing a single iteration in the enlarged state-space.

Anisotropic Interpolation on Graphs: The Combinatorial Dirichlet Problem

The combinatorial Dirichlet problem is formulated, and an algorithm for solving it is presented. This provides an effective method for interpolating missing data on weighted graphs of arbitrary connectivity. Image processing examples are shown, and the relation to anistropic diffusion is discussed.