Innovative Computational Methods for Transcriptomic Data Analysis: A Case Study in the Use of FPT for Practical Algorithm Design and Implementation

Tools of molecular biology and the evolving tools of genomics can now be exploited to study the genetic regulatory mechanisms that control cellular responses to a wide variety of stimuli. These responses are highly complex, and involve many genes and gene products. The main objectives of this paper are to describe a novel research program centered on understanding these responses by (i) developing powerful graph algorithms that exploit the innovative principles of fixed parameter tractability in order to generate distilled gene sets; (ii) producing scalable, high performance parallel and distributed implementations of these algorithms utilizing cutting-edge computing platforms and auxiliary resources; (iii) employing these implementations to identify gene sets suggestive of co-regulation; and (iv) performing sequence analysis and genomic data mining to examine, winnow and highlight the most promising gene sets for more detailed investigation. As a case study, we describe our work aimed at elucidating genetic regulatory mechan isms that control cellular responses to low-dose ionizing radiation (IR). A low-dose exposure, as defined here, is an exposure of at most 10 cGy (rads). While the consequences of high doses of radiation are well known, the net outcome of low-dose exposures con tinues to be debated, with support in the literature for both detrimental and beneficial effects. We use genome-scale gene expression data collected in response to low-dose IR exposure in vivo to identify the pathways that are activated or repressed as a tissue responds to the radiation insult. The driving motivation is that knowledge of these path ways will help clarify and interpret physiological responses to IR, which will advance our understanding of the health consequences of low-dose radiation exposures.