Marco Jenzsch

Improving the batch-to-batch reproducibility in microbial cultures during recombinant protein production by guiding the process along a predefined total biomass profile

In industry Escherichia coli is the preferred host system for the heterologous biosynthesis of therapeutic proteins that do not need posttranslational modifications. In this report, the development of a robust high-cell-density fed-batch procedure for the efficient production of a therapeutic hormone is described. The strategy is to guide the process along a predefined profile of the total biomass that was derived from a given specific growth rate profile. This profile might have been built upon experience or derived from numerical process optimization. A surprisingly simple adaptive procedure correcting for deviations from the desired path was developed. In this way the batch-to-batch reproducibility can be drastically improved as compared to the process control strategies typically applied in industry. This applies not only to the biomass but, as the results clearly show, to the product titer also.

PRODUCT FORMATION KINETICS IN A RECOMBINANT PROTEIN PRODUCTION PROCESS

Abstract Protein formation in recombinant protein production cannot yet be modeled in a way sufficiently accurate for process supervision and control. Here we propose using a new hybrid approach based on mass balances for the state variables involved, where the kinetics are represented by artificial neural networks (ANN). We first demonstrate by means of simulations that this method works well even when the networks are trained on noisy process data. Then, secondly, we show that the method is applicable to real fermentation data. As an accompanying example we use an E.coli culture that produces a recombinant protein, namely the green fluorescent protein GFP, which remains dissolved within the cytoplasm. For this case the ANN resulted in a concrete relationship between the specific product formation rate π, the specific growth rate μ and the specific product concentration p/x. The π(μ)-part of the relationship confirms what was obtained with a conventional approach and the additional information about the influence of the specific product concentration characterizes the metabolic load of the cell.

Application of Model Predictive Control to Cultivation Processes for Protein Production with Genetically Modified Bacteria

Abstract Model predictive control is shown to be a reliable method to keep cultures of genetically modified bacteria very close to predetermined profiles of their key physiological variable, the specific biomass growth rate. This was shown experimentally by E.coli bacteria producing the recombinant model protein GFP, which can be monitored quickly and accurately by means of fluorescence spectrometry. In the experiments, the culture was shown to exactly follow a complicated path with considerable jumps in the specific growth rate. They were performed in a standard 10 L Biostat C fermenter. Prior to the control experiments, a process model was developed and validated against data from the process under consideration. This model was then used to determine the corresponding optimal feeding profile required to keep the process at the desired profile of the specific growth rate by means of numerical optimization. The experiments showed that this model predictive control procedure can be routinely applied to protein production processes, when it is possible to provide sufficient online measurement information about the current state of the process. This was shown to be possible using Extended K.alman Filter algorithms running on a simple PC.