Identifying the genes and genetic interrelationships underlying the impact of calorie restriction on maximum lifespan: an artificial intelligence-based approach.

Novel artificial intelligence methodologies were applied to analyze gene expression microarray data gathered from mice under a calorie restriction (CR) regimen. The data were gathered from three previously published mouse studies; these datasets were merged together into a single composite dataset for the purpose of conducting a broader-based analysis. The result was a list of genes that are important for the impact of CR on lifespan, not necessarily in terms of their individual actions but in terms of their interactions with other genes. Furthermore, a map of gene interrelationships was provided, suggesting which intergene interactions are most important for the effect of CR on life extension. In particular our analysis showed that the genes Mrpl12, Uqcrh, and Snip1 play central roles regarding the effects of CR on life extension, interacting with many other genes (which the analysis enumerates) in carrying out their roles. This is the first time that the genes Snip1 and Mrpl12 have been identified in the context of aging. In a follow-up analysis aimed at validating these results, the analytic process was rerun with a fourth dataset included, yielding largely comparable results. Broadly, the biological interpretation of these analytical results suggests that the effects of CR on life extension are due to multiple factors, including factors identified in prior theories of aging, such as the hormesis, development, cellular, and free radical theories.

Genetic algorithm for analysis of mutations in Parkinson's disease.

OBJECTIVE: Mitochondrial genetics has unique features that impede analysis of the biological significance of mitochondrial mutations. Simple searches for differences in total mutational load between normal and pathological samples have been frequently unrewarding, raising the possibility that more complex patterns of mutations may be responsible for some conditions. We explore this possibility in the context of Parkinson's disease (PD). METHODS AND MATERIALS: We report the development of a modified genetic algorithm suited for detection of biologically meaningful patterns of mitochondrial mutations. The algorithm is applied to a database of mutations derived from biological samples, and verified by the use of shuffled data, and repeated leave-one-out testing. RESULTS: It is possible to derive, from a very small sample, multiple accurate classifier functions that correlate with biological features. The methodology is validated statistically through experiments with fabricated data. CONCLUSION: This algorithm might be generally applicable to conditions where interactions among multiple mitochondrial DNA mutations are important. The patterns embodied in the classifier functions obtained should be the subject of further experimental study.