Learning Proteomic Network Structure by a New Hill Climbing Algorithm

As a progressive, degenerative disease, ataxia telangiectasia (A-T) is caused by a gene mutation (ATM) and is a predisposition to cancer. Understanding the impaired signaling networks caused by ATM will help minimizing the damage and finding effective therapies. The goal of this work is to investigate the dynamic change of ATM-dependent signaling pathways under the treatment of different radiation dosages. A reverse-phase protein microarray (RPPM) in conjunction with quantum dots nano-crystal technology is used for the quantitative measurement. To discover the proteomic pathways affected in ATM cells, a new hill climbing algorithm is developed based on mutual information, the classical hill-climbing method, and the optimization of the local structure. More trusted biology networks are thus defined by the new approach. The study was carried out at different time points under different dosages for cell lines with and without ATM mutation. To validate the performance of the proposed algorithm, comparison experiments were also implemented using public networks.