The Rawlings Research Group -- Andrew P. Fordyce

James B. Rawlings Research Group

Andrew P. Fordyce
Ph.D., University of Texas at Austin, 1991

Publications

1: Andrew P. Fordyce and James B. Rawlings.
Segregated fermentation model for growth and differentiation of Bacillus licheniformis.
AIChE J., 42(11):3241-3252, November 1996.
2: Andrew P. Fordyce.
Modelling of Antibiotic Production by Bacillus licheniformis Using a Structured/Segregated Approach.
PhD thesis, The University of Texas at Austin, December 1991.
3: Andrew P. Fordyce, James B. Rawlings, and Thomas F. Edgar.
Control strategies for fermentation processes.
In Daniel R. Omstead, editor, Computer Control of Fermentation Processes, pages 165-206. CRC Press, 1990.
4: Andrew P. Fordyce and James B. Rawlings.
Modelling antibiotic production using a structured/segregated approach.
Annual AIChE Meeting, Chicago, Illinois, November 1990.

Thesis Abstract

Modelling of Antibiotic Production by Bacillus licheniformis Using a Structured/Segregated Approach

This study evaluates the effectiveness of a segregated model for prediction of differentiation-related secondary metabolism in a bacterial system. Specifically, this work has examined the production of the peptide antibiotic bacitracin by Bacillus licheniformis in a submerged-culture fermentation system. A segregated model recognizing the three morphological stages of the Bacillus life cycle has been developed. The sporangium biomass has been characterized using an age-population model to reflect the age-dependent antibiotic kinetics. Constitutive relationships governing the rates of vegetative cell reproduction, spore germination, commitment to sporulation, and substrate consumption have been proposed. Based on this model framework, the dynamic cell growth, differentiation, and antibiotic production equations have been developed. Numerical solution techniques have been designed to provide solutions to the steady-state and dynamic fermentation equations.

As a means of comparison between the segregated modelling approach and more common fermentation models, a structured model has been adapted to the bacitracin system. The comparison model utilizes a two-compartment age model to describe cell growth, differentiation and bacitracin production.

Batch, steady state, and step-test fermentation data from a laboratory-scale fermentor have been incorporated into a maximum likelihood parameter estimation scheme for model identification. Confident estimates of the growth and differentiation parameters have been obtained for the segregated model using available measurements. However, the insensitivity of the bacitracin data to the production function only allowed for confident determination of a constant antibiotic production coefficient.

In contrast to the segregated model, the structure of the comparison model prevented complete utilization of the available measurements in the parameter estimation scheme. This created a situation in which the information provided by the applicable measurements was insufficient for confident determination of the model parameters. The two-compartment age model was also unable to adequately describe the delay between fermentor inoculation and the appearance of antibiotic.

Open-loop optimal control studies were utilized to compare the suggested control policies of the two models. The computed strategies were significantly different, with the segregated model suggesting a non-batch feeding policy that predicts improved antibiotic yield of 30% over the comparison model batch policy.

University of Wisconsin
Department of Chemical Engineering
Madison WI 53706