Dynamic flux estimation - a novel framework for metabolic pathway analysis
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High-throughput time series data characterizing magnitudes of gene expression, levels of protein activity, and the accumulation of select metabolites in vivo are being generated with increased frequency. These time profiles contain valuable information about the structure, dynamics and underlying regulatory mechanisms that govern the behavior of cellular systems. However, extraction and integration of this information into fully functional, computational and explanatory models has been a daunting task. Three types of issues have prevented successful outcomes in this inverse task of system identification. The first type pertains to the algorithmic and computational difficulties encountered in parameter estimation, be it using a genetic algorithm, nonlinear regression, or any other technique. The second type of issues stems from implicit assumptions that are made about the system topology and/or the functional model representing the biological system. These include the choice of intermediate pathway steps to be accounted for in the model, decisions on the irreversibility of a step, and the inclusion of ill-characterized regulatory signals. The third type of issue arises from the fact that there is often no unique set of parameter values, which when fitted to a model, reproduces the observed dynamics under one or several different sets of experimental conditions. This latter issue raises intriguing questions about the validity of the parameter values and the model itself. The central focus of my research has been to design a workflow for parameter estimation and system identification from biological time series data that resolves the issues outlined above. In this thesis I present the theory and application of a novel framework, called Dynamic Flux Estimation (DFE), for system identification from biological time-series data.