this article is to introduce the ideas and methods of reliability theory to a wider audience interested in understanding the mechanisms of aging, mortality, survival, and longevity. It is also important to review and summarize the recent scienti"c literature on reliability approach to the problem of biological aging (Miller, 1989; Gavrilov & Gavrilova, 1991; Abernethy, 1998; Bains, 2000). The main emphasis here is made on the accomplishments of the reliability approach rather than previous occasional mistakes, because some questionable models (Murphy, 1978; Skurnick & Kemeny, 1978; Koltover, 1983, 1997; Witten, 1985) were already reviewed elsewhere (Gavrilov, 1984, 1987; Gavrilov & Gavrilova, 1991). This theoretical review article also elaborates further some results and ideas published in the book on a related topic (Gavrilov & Gavrilova, 1991)

One of the cornerstones of demography is the mortality rate m(x,t) of individuals of age x at calendar time t. However, from the perspective of evolutionary theory in general (Stearns and Hoekstra 2000) or biodemography more specifically (Gavrilov and Gavrilova 1991; Carey 2001; Carey and Judge 2001) the mortality rate is an evolved quantity, the result of natural selection acting on patterns of growth, behavior, and reproduction. Our understanding of aging has been enormously advanced by genetic studies in the laboratory, to the point that it appears we will be able to understand all aspects of life span and aging with genetic tools. But aging takes place outside the laboratory and is also very much a phenotypic process. Recent studies, showing that the “public mechanisms ” affecting longevity (Martin et al. 1996) are common to a wide variety of organisms (Guarente and Kenyon 2000; Strauss 2001; Zhang and Herman 2002), suggest that we need to understand the role of environment to master our understanding of aging and life span. Furthermore, it is likely that our understanding will be

of aging, an attractive approach is to use DNA microarrays to scan the entire genome for genes that change expression as a function of age or under conditions when longevity is extended. The list of age-regulated genes provides clues about genetic pathways and mechanisms that underlie the aging process. In addition to single-gene analysis, the combined transcriptional profile of aging can act as a molecular phenotype of old age. Over the last 20 years, there has been a great deal of effort to search for biomarkers of aging, and recent studies have shown that expression profiles of aging derived from DNA microarray experiments may provide this long-desired goal. A gene expression signature for aging is a quantitative phenotype that gives a high-resolution view of the aging process, much like using transcriptional profiles of cancer to inform about their severity or malignancy. Previously, one could recognize old versus young individuals in a photograph, or old versus young tissue on a microscope slide. Now it is possible to recognize old versus young genetic networks by analyzing expression levels of the entire set of age-regulated genes (Fig. 1). Unlike photographs or micrographs, expression data from DNA microarrays are quantitative, and thus it is possible to compare age-related transcriptional profiles between different tissues, between different conditions that affect

Motivation: Many aging genes have been found from unbiased screens in model organisms. Genetic interventions promoting longevity are usually quantitative, while in many other biological fields (e.g. development) null mutations alone have been very informative. Therefore, in the case of aging the task is larger and the need for a more efficient genetic search strategy is especially strong. Results: The topology of genetic and metabolic networks is organized according to a scale-free distribution, in which hubs with large numbers of links are present. We have developed a computational model of aging genes as the hubs of biological networks. The computational model shows that, after generalized damage, the function of a network with scale-free topology can be significantly restored by a limited intervention on the hubs. Analyses of data on aging genes and biological networks support the applicability of the model to biological aging. The model also might explain several of the properties of aging genes, including the high degree of conservation across different species. The model suggests that aging genes tend to have a higher number of connections and therefore supports a strategy, based on connectivity, for prioritizing what might otherwise be a random search for aging genes. Contact:

us amount of work, which was done in collaboration with Dr. Natalia Gavrilova, my wife. We were fortunate to make a number of new findings, which have since been cited in the scientific literature. Writing a book was a good method of selfeducation in aging studies. How do I know that this self-education was correct? Well, indicators include the fact that our book was selected and cited by the Encyclopedia Britannica as a recommended reference on longevity. The book also received positive reviews in a dozen scientific journals, including Nature, the British Medical Journal, and BioEssays, and more than 100 citations in the scientific literature. We believe that our research and self-education efforts were not in vain. Another very good test of our scientific credentials occurred five years ago, when Natalia and I immigrated to the United States from Russia and applied for research funding in this new and highly competitive environment. We were lucky to be awarded research grants from

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Research article Genome wide analysis of common and specific stress responses in adult drosophila melanogaster

There are a handful of biological questions that affect all of us directly in everyday life. How are emotions formed, what is the basis for consciousness, and why do we look the way we do? One that strikes particularly close to home is the question of how we age. The sheer complexity of this problem has had many scientists throw up their hands in frustration and most of the postulated theories have been vague and generally have involved ill-defined wear-andtear mechanisms. But the pursuit of the biological basis of aging has been

One of the cornerstones of demography is the mortality rate m(x,t) of individuals of age x at calendar time t. However, from the perspective of evolutionary theory in general (Stearns and Hoekstra 2000) or biodemography more specifically (Gavrilov and Gavrilova 1991; Carey 2001; Carey and Judge 2001) the mortality rate is an evolved quantity, the result of natural selection acting on patterns of growth, behavior, and reproduction. Our understanding of aging has been enormously advanced by genetic studies in the laboratory, to the point that it appears we will be able to understand all aspects of life span and aging with genetic tools. But aging takes place outside the laboratory and is also very much a phenotypic process. Recent studies, showing that the “public mechanisms ” affecting longevity (Martin et al. 1996) are common to a wide variety of organisms (Guarente and Kenyon 2000; Strauss 2001; Zhang and Herman 2002), suggest that we need to understand the role of environment to master our understanding of aging and life span. Furthermore, it is likely that our understanding will be

Life span is an evolved life-history characteristic of an organism that refers to the duration of its life course. Application of the concept is straightforward at both individual and cohort levels: it specifies the period between birth and death for the individual and the average length of life or life expectancy at birth for cohorts (including both real and synthetic cohorts). When life span is applied to a population or a species, however, it requires a modifier to avoid ambiguity (Goldwasser 2001). Maximum observed life span is the highest verified age at death, possibly limited to a particular population, cohort, or species in a specified time period. The overall highest verified age for a species is called its record life span. The theoretical highest attainable age is known as maximum potential life span. Maximum observed life spans (i.e., longevity records) are not synonymous with theoretical maximums for at least two reasons. First, maximum longevity is an inappropriate general concept because an animal dies before the age of infinity. It does so not because it cannot pass some boundary age