Advanced methodologies for the modeling of metabolic pathway systems based on time series data
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Metabolic pathways are series of enzyme-catalyzed chemical reactions that take place within a cell. These biochemical pathways can be quite elaborate and highly regulated with numerous positive or negative feedback or feed-forward mechanisms, which produce complex dynamical behaviors. Time series data have been more readily available in recent years as a result of the development of new measurement techniques. These techniques offer novel options for inferring the intricate regulatory structure of the metabolic pathways, analyzing the design and function of biological modules, as well as making predictions based on this analysis. The first objective of the proposed research is to advance mathematical methodologies for the study of metabolic and signaling pathways where time series data are available. The second objective is the application of these methodological advances toward a deeper understanding of the glycolytic pathway in the dairy bacterium Lactococcus lactis.