It has been suggested that both EGR-1 and p53 are essential for mediating radiation-induced apoptosis. The effect of priming at a late stage indicates a network modulation through pro-inflammatory responses and proteasomes, which is also consistent with the literature on low dose exposure. Another way to examine experimental data is through enrichment of cellular processes. Initially, both treatment groups are enriched by DNA double strand repair, apoptosis, and cell cycle processes. However, the group receiving the priming dose is also enriched with single strand base excision and mismatch DNA repair. Within the group receiving the priming dose, these processes are modulated with a chromatin remodeling at 8 hour time point. On the other hand, the group receiving no priming dose appears to be poorly enriched in the final stages. The bioinformatics analysis suggests that the priming dose changes the network architecture by delaying the effects of the chromatin remodeling. The first step of our protocol is to eliminate transcripts with little variation, which are maximum folds of change less than 0.5. The net result is a significant Afatinib reduction in the number of candidate transcripts, with those having similar temporal profiles being grouped together. The basic assumption is that co-regulated transcripts have a similar biological basis and is a step towards significant dimensionality reduction through clustering and categorization. Currently, there is an abundance of literature available on the clustering of time-varying expression data that includes predefined templates, autoregressive models, curve-based clustering, and mixture models. Nonetheless, this is not the main theme of our research. Our approach relies on constructing template profiles through consensus clustering, widely used for class discovery, and then leveraging higher level enrichment analysis for evaluation. Consensus uses a voting strategy on the resampled data, from different runs, with a clustering algorithm, and facilitates visualizing of computed clusters for quality control. In our implementation, the clustering algorithm is based on k-means, where the distance measured is one minus the sample correlation. The clustering procedure is repeated for 1000 runs, and each run is performed on the randomly sampled genes with a sampling rate of 0.8. The optimal number of clusters is determined by examining the clustering stability and similarity matrix. It is estimated that over 500 million people suffer from malaria infections annually, resulting in more than 600,000 deaths. A major breakthrough in understanding malaria was made about one hundred years ago by Sir Ronald Ross, who first demonstrated that the malarial parasites, Plasmodium species, were transmitted by mosquitoes. Since Ross’s discovery, one of the most successful methods of malaria prevention and eradication has been through control of the mosquito vector. All of the Plasmodium species that cause human malaria are transmitted by mosquitoes of the genus Anopheles. Of the approximately 430 Anopheles species, about 30–40 are vectors of malaria parasites. Assessment of malaria risks, deployment of vector control techniques, and evaluation of the impact of control measures would benefit from knowledge of the identity, spatial distribution, and abundance of the various vector species. The development of simple, rapid, low-cost, and population structure and may play an important role in the development of effective vector control strategies. All these techniques are laborious, require highly skilled personnel, are applicable only to certain cells, and require laboratory facilities.