title: Retrospective optimization of time-dependent fermentation control strategies using time-independent historical data.
abstract: We have previously shown the usefulness of historical data for fermentation process optimization. The methodology developed includes identification of important process inputs, training of an artificial neural network (ANN) process model, and ultimately use of the ANN model with a genetic algorithm to find the optimal values of each critical process input. However, this approach ignores the time-dependent nature of the system, and therefore, does not fully utilize the available information within a database. In this work, we propose a method for incorporating time-dependent optimization into our previously developed three-step optimization routine. This is achieved by an additional step that uses a fermentation model (consisting of coupled ordinary differential equations (ODE)) to interpret important time-course features of the collected data through adjustments in model parameters. Important process variables not explicitly included in the model were then identified for each model parameter using automatic relevance determination (ARD) with Gaussian process (GP) models. The developed GP models were then combined with the fermentation model to form a hybrid neural network model that predicted the time-course activity of the cell and protein concentrations of novel fermentation conditions. A hybrid-genetic algorithm was then used in conjunction with the hybrid model to suggest optimal time-dependent control strategies. The presented method was implemented upon an E. coli fermentation database generated in our laboratory. Optimization of two different criteria (final protein yield and a simplified economic criteria) was attempted. While the overall protein yield was not increased using this methodology, we were successful in increasing a simplified economic criterion by 15% compared to what had been previously observed. These process conditions included using 35% less arabinose (the inducer) and 33% less typtone in the media and reducing the time required to reach the maximum protein concentration by 10% while producing approximately the same level of protein as the previous optimum.
authors.affiliation: Department of Chemical Engineering and Materials Science, University of California, One Shields Avenue, Davis, 95616, USA.
authors.forename: Matthew C
authors.initials: MC
authors.lastname: Coleman
authors.forename: David E
authors.initials: DE
authors.lastname: Block
mesh_terms: D019149:Bioreactors / Q000382:microbiology; D004926:Escherichia coli / Q000378:metabolism; D005285:Fermentation; D007218:Industrial Microbiology / Q000379:methods*; D007700:Kinetics; D008954:Models, Biological; D016571:Neural Networks, Computer; D016011:Normal Distribution; D012189:Retrospective Studies; D013997:Time Factors