Lars Freier

Biotechnological separation processes are routinely designed and optimized using parallel high-throughput experiments and/or serial experiments. Well-characterized processes can further be optimized using mechanistic models. In all these cases - serial/parallel experiments and modeling - iterative strategies are customarily applied for planning novel experiments/simulations based on the previously acquired knowledge. Process optimization is typically complicated by conflicting design targets… Mehr anzeigen

Biotechnological separation processes are routinely designed and optimized using parallel high-throughput experiments and/or serial experiments. Well-characterized processes can further be optimized using mechanistic models. In all these cases - serial/parallel experiments and modeling - iterative strategies are customarily applied for planning novel experiments/simulations based on the previously acquired knowledge. Process optimization is typically complicated by conflicting design targets, such as productivity and yield. We address these issues by introducing a novel algorithm that combines recently developed approaches for utilizing statistical regression models in multi-objective optimization. The proposed algorithm is demonstrated by simultaneous optimization of elution gradient and pooling strategy for chromatographic separation of a three-component system with respect to purity, yield, and processing time. Gaussian Process Regression Models (GPM) are used for estimating functional relationships between design variables (gradient, pooling) and performance indicators (purity, yield, time). The Pareto front is iteratively approximated by planning new experiments such as to maximize the Expected Hypervolume Improvement (EHVI) as determined from the GPM by Markov Chain Monte Carlo (MCMC) sampling. A comprehensive Monte-Carlo study with in-silico data illustrates efficiency, effectiveness and robustness of the presented Multi-Objective Global Optimization (MOGO) algorithm in determining best compromises between conflicting objectives with comparably very low experimental effort. Weniger anzeigen

The production of bulk enzymes used in food industry or organic chemistry constitutes an important part of industrial biotechnology. The development of production processes for novel proteins comprises a variety of biological engineering and bioprocess reaction engineering factors. The combinatorial explosion of these factors can be effectively countered by combining high-throughput experimentation with advanced algorithms for data analysis and experimental design. We present an experimental… Mehr anzeigen

The production of bulk enzymes used in food industry or organic chemistry constitutes an important part of industrial biotechnology. The development of production processes for novel proteins comprises a variety of biological engineering and bioprocess reaction engineering factors. The combinatorial explosion of these factors can be effectively countered by combining high-throughput experimentation with advanced algorithms for data analysis and experimental design. We present an experimental optimization strategy that merges three different techniques: (1) advanced microbioreactor systems, (2) lab automation, and (3) Kriging-based experimental analysis and design. This strategy is demonstrated by maximizing product titer of secreted green fluorescent protein (GFP), synthesized by Corynebacterium glutamicum, through systematic variation of CgXII minimal medium composition. First, relevant design parameters are identified in an initial fractional factorial screening experiment. Then, the functional relationship between selected media components and protein titer is investigated more detailed in an iterative procedure. In each iteration, Kriging interpolations are used for formulating hypotheses and planning the next round of experiments. For the optimized medium composition, GFP product titer was more than doubled. Hence, Kriging-based experimental analysis and design has been proven to be a powerful tool for efficient process optimization. This article is protected by copyright. All rights reserved Weniger anzeigen

Empirical and mechanistical modeling approaches are often used in order to analyze functional relationships between process factors and system response and to identify process optima. The Kriging method allows to integrate both modeling approaches by combining statistical information on a given data set with a priori defined trend functions. However, trend functions from biotechnology applications are typically nonlinear with respect to the model parameters, which is not supported by standard… Mehr anzeigen

Empirical and mechanistical modeling approaches are often used in order to analyze functional relationships between process factors and system response and to identify process optima. The Kriging method allows to integrate both modeling approaches by combining statistical information on a given data set with a priori defined trend functions. However, trend functions from biotechnology applications are typically nonlinear with respect to the model parameters, which is not supported by standard Kriging. In this paper, we present an extension of the Kriging method for handling nonlinear trend functions by a Taylor based linearization approach which leads to an iterative parameter estimation procedure. Weniger anzeigen