Lawrence Chew

Reliable protein production in a Pseudomonas fluorescens expression system.

A bottleneck to product development can be reliable expression of active target protein. A wide array of recombinant proteins in development, including an ever growing number of non-natural proteins, is being expressed in a variety of expression systems. A Pseudomonas fluorescens expression platform has been developed specifically for recombinant protein production. The development of an integrated molecular toolbox of expression elements and host strains, along with automation of strain screening is described. Examples of strain screening and scale-up experiments show rapid development of expression strains producing a wide variety of proteins in a soluble active form.

Biomass Inferential Sensor Based on Ensemble of Models Generated by Genetic Programming

A successful industrial application of a novel type biomass estimator based on Genetic Programming (GP) is described in the paper. The biomass is inferred from other available measurements via an ensemble of nonlinear functions, generated by GP. The models are selected on the Pareto front of performance-complexity plane. The advantages of the proposed inferential sensor are: direct implementation into almost any process control system, rudimentary self-assessment capabilities, better robustness toward batch variations, and more effective maintenance. The biomass inferential sensor has been applied in high cell density microbial fermentations at The Dow Chemical Company.

Pseudomonas fluorescens – a robust expression platform for pharmaceutical protein production

Background A bottleneck to protein pharmaceutical production can be efficient expression of the target protein. A Pseudomonas fluorescens-based manufacturing platform for high yield production of non-glycosylated protein pharmaceuticals has been developed. This platform is derived from P. fluorescens biovar I strain MB101 [1]. The systems performance is due to the combination of a robust host strain, the availability of extensive molecular biology and bioinformatics tools, and a well optimized high cell density fermentation process. The Systems Biology tools include a genomics and functional genomics capability, a range of stable plasmid vectors of various copy numbers, non-antibiotic-dependent plasmid maintenance [2], multiple expression cassettes [3] and engineered host strains for stringent control of gene expression, and the ability to export proteins to the cells periplasmic space.

Multivariate Analysis for Quality Improvement of an Industrial Fermentation Process

Abstract The results of a successful implementation of multivariate analysis on multiple fermentaticns are shown in the paper. To minimize batch-to-batch variability of an industrial fermentation process, multi-way partial least squares (PLS) was used. Eighteen baseline batches from different ferrnentors were analyzed. Afier applying diagnostics tools and cmtribution charts, three. batches were clearly identified to be abnormal, and even among the remaining OOtches, inconsistencies were found. The root causes of the varia, ility were determined by a combination of variable importance in the projection plot ani fennentation knowledge. A new fermentation procedure was applied and the quality improvement was demonstrated on 20 new batches. These batches were more consistent as evidenced by the improvement in the model fit (R 2 X = 0.829 for the new batches versus R 2 X = 0.681 for the baseline batches) and in the percent of out-of-specification (3.3% for the new batches versus 20.4% for the baseline batches). The 01line multi-way PLS model was shown to detect bad batches promptly and to determine abnonnal variables accurately. The success of this implementation demonstrates the value of applying multivariate analysis to large-scale industrial batch bioprocesses