Metabolic function and aging are complex traits that involve entire networks of changes at the molecular level, driven by genetic and environmental perturbations. To fully understand these changes, a systems biology approach needs to be taken. Using systems biology, we can investigate the myriad of interactions between the components of complex biological systems (e.g. an aging organism), and determine how these interactions give rise to the function and behavior of that system. The growth and development in the last decade of accurate and reliable mass data collection techniques (such as genomics and proteomics) has greatly enhanced our comprehension of molecular networks and cell signaling pathways. At the same time however, these technological advances have also increased the difficulty of satisfactorily analyzing and interpreting these ever-expanding datasets. At the present time, multiple diverse scientific communities including molecular, genetic, proteomic, bioinformatic, cell biological, and animal behavioral, are converging upon a common endpoint, i.e. the measurement, interpretation and potential prediction of molecular and behavioral activity from mass datasets. Our ever increasing appreciation of the complexity of biological systems has necessitated the generation of a new branch of informatics that more closely associates molecular functions to behavioral responses in living organisms. As currently systems biology analytical tools are lacking or need further development, we are aiming to further develop and expand existing proteomics techniques and we are creating bioinformatics software programs that can aid systems biology research. These include novel bioinformatics software programs that can cluster, visualize and functionally group large datasets, and proteomics techniques that can analyze proteins with low expression levels.
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