Abstract: The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.
Abstract: The present work deals with analyses of the effects
of bearing curvature and non-Newtonian characteristics on the load capacity of an exponential rectangular squeeze film bearing using
Bingham fluids as lubricants. Bingham fluids are characterized by an
yield value and hence the formation of a “rigid" core in the region
between the plates is justified. The flow is confined to the region
between the core and the plates. The shape of the core has been
identified through numerical means. Further, numerical solutions for
the pressure distribution and load carrying capacity of the bearing
for various values of Bingham number and curvature parameter have
been obtained. The effects of bearing curvature and non-Newtonian
characteristics of the lubricant on the bearing performances have been
discussed.