What Can Networks Tell Us About Aging?

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The US is growing older because of millions of baby boomers who already started turning 65. Since susceptibility to diseases increases with age, studying molecular causes of aging gains importance. Human lifespan is long, which, in addition to ethical constraints, makes studying human aging difficult. Therefore, aging is studied in simpler ?model? species, e.g., baker?s yeast. Then, the knowledge about aging is transferred from model species to human. Thus far, this transfer has been restricted to genomic sequence comparison, by identifying regions of similarity between sequences of genes in different species (which are believed to be a consequence of functional relationships between the sequences), and by transferring the knowledge from a gene in model species to a sequence-similar gene in human. However, genes (that is, their protein products) carry out biological function by interacting in complex networked ways with one another, instead of acting alone. Hence, it has been argued in the post-genomic era that the wirings among genes in cellular networks could give biological insights over and above sequences of individual genes. Thus, this project hypothesizes that, analogous to genomic sequence research, biological network research will impact our understanding of aging. For example, since not all genes implicated in aging in model species have sequence-similar genes in human, restricting comparison to sequence may limit the transfer of aging-related knowledge to human. Network comparison can help, as it can find regions of similarities between networks of different species and allow for a transfer of the knowledge between such regions.Intellectual merit: Unlike genomic sequence research, biological network research is in its infancy, for the following reasons. Many network problems (including network comparison) are computationally intractable, and hence, efficient approximate (or heuristic) solutions are needed. The function of many genes remains unknown, and hence, it must be discovered from other, better-characterized genes. Even though cells evolve over time, current methods for analyzing systems-level biological networks deal only with their static representations, because dynamic biological network data can not be obtained easily with current biotechnologies, and because there is a lack of efficient methods for dynamic network analysis. Current biological networks are noisy, with many missing and spurious links, due to limitations of biotechnologies as well as human biases during data collection; thus, methods for network de-noising need to be developed. Hence, this project aims to use sensitive measures of network structure (or topology) to develop new heuristic computational methods for efficient network analysis, which can cope with the complexity of functionally uncharacterized, dynamic, and noisy biological networks. Also, it aims to help in understanding the processes of human aging by enabling exploitation of biological network data. Specifically, the new methods will be used to: transfer the knowledge about aging from model species to human to complement the knowledge obtained from sequence; study dynamic human biological networks (obtained computationally by combining current static networks with age-specific gene expression data) to learn about how cells change with age; and de-noise current networks to produce higher-confidence results.Broader impacts: Understanding aging is of societal importance. Since network research spans many domains, the proposed methods will be implemented into open-source research software, which will also serve as an educational tool. Integration of research and education will be promoted further by training interdisciplinary scientists through novel courses on network research. Research supervision will be offered to K-12, undergraduate, and graduate students, focusing on minorities and women. Interdisciplinary collaborations will be encouraged to allow for wide distribution of the proposed ideas and results.