Tuesday, October 18, 2011: 2:22 PM
Conrad C (Hilton Minneapolis)
Metabolic flux analysis (MFA) is a key tool for measuring in vivo metabolic fluxes in systems at metabolic steady state. Here, we present an extension to MFA for determining flux dynamics in systems at metabolic non-steady state. The dynamic MFA (DMFA) framework is based on non-stationary stoichiometric mass balances to describe flux transients. We derived exact algebraic solutions to the DMFA problem and developed efficient algorithms for least-squares parameter estimation and statistical analysis in these dynamic models. In DMFA, time-series of concentration measurements and external rate measurements are fitted in a single analysis to determine metabolic transients for the entire culture. Rigorous statistical criteria are employed to automatically identify characteristic metabolic phases. Here, we demonstrate the application of DMFA in three example systems. First, we evaluated the performance of DMFA in a simple three-reaction model in terms of accuracy, precision and flux observability. Next, we analyzed a commercial glucose-limited fed-batch process for 1,3-propanediol production. The DMFA methodology accurately captured the dynamic behavior of the fed-batch fermentation and identified characteristic metabolic phases. Lastly, we demonstrate the application of DMFA without an assumed metabolic model for detecting gross measurement errors in fermentation data and for data reconciliation using carbon and electron balances as constraints. The DMFA approach that we developed here is ideally suited for monitoring metabolic shifts in industrial fermentations.