Functional Proteomic Pattern Identification under Low Dose Ionizing Radiation

The goal of this study is to explore and to understand the dynamic responses of signaling pathways to low dose ionizing radiation (IR). Low dose radiation (10 cGy or lower) affects several signaling pathways including DNA repair, survival, cell cycle, cell growth, and cell death. To detect the possibly regulatory protein/kinase functions, an emerging reverse-phase protein microarray (RPPM) in conjunction with quantum dots nano-crystal technology is used as a quantitative detection system. The dynamic responses are observed under different time points and radiation doses. To quantitatively determine the responsive protein/kinases and to discover the network motifs, we present a Discriminative Network Pattern Identification System (DiNPIS). Instead of simply identifying proteins contributing to the pathways, this methodology takes into consideration of protein dependencies which are represented as Strong Jumping Emerging Patterns (SJEP). Furthermore, infrequent patterns though occurred will be considered irrelevant. The whole framework consists of three steps: protein selection, protein pattern identification, and pattern annotation. Computational results of analyzing ATM (ataxia-telangiectasia mutated) cells treated with six different IR doses up to 72 hours are presented.