TF08: Error Correction Algorithms for DNA Repair: Inference, Analysis, and Intervention

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TF: Error Correction Algorithms for DNA Repair: Inference, Analysis, and InterventionBane Vasiæ, David W. Galbraith, and Michael W. MarcellinThe University of Arizona, TucsonMaintaining integrity of genetic material is vital to the survival of species, and is achieved through deoxyribonucleic acid (DNA) repair, a process in the cell in which DNA-damage is continually monitored and corrected. For example, ionizing radiation can induce single and double strand breaks, the most dangerous type of damage, which if uncorrected leads to cell death, while inaccurate repair can be mutagenic. This research establishes a framework for rigorous treatment of genetic error correction, or more specifically, for inferring the error correction coding system of the living cell and describing its functionality quantitatively and algorithmically. This framework is based on probabilistic graphical models that are used in error correction theory to design codes enabling transmission of information in the presence of very high noise levels and ensuring fault-tolerance and reliable storage of information in systems built of faulty components, which precisely corresponds to the DNA-repair scenario. By combining experimental data with existing knowledge of gene-protein and protein-protein interactions, the investigators are creating global functional interaction networks of genes involved in DNA repair. This enables a study of the error correction algorithms and their dynamics, resulting in a formal logical and causal description of interaction among genes, proteins and inducible factors, or a genetic wiring diagram. Such a wiring diagram can be viewed as a digital logic circuit of a genetic decoder. The investigators study the decoder structure and behavior, and more particularly: (i) inferring the decoder from the existing knowledge and new experiments, (ii) predicting the dynamics of the error control system, and (iii) controlling the dynamics using external factors.