Articles by Lars Freier in JoVE
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology Johannes Hemmerich*1,2, Lars Freier*1,2, Wolfgang Wiechert1,2,3, Eric von Lieres1,2, Marco Oldiges1,2,4 1IBG-1: Biotechnology, Forschungszentrum Jülich, 2Research Center Jülich, Bioeconomy Science Center (BioSC), 3Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 4Institute for Biotechnology, RWTH Aachen University This manuscript describes a generic approach for tailor-made design of microbial cultivation media. This is enabled by an iterative workflow combining Kriging-based experimental design and microbioreactor technology for sufficient cultivation throughput, which is supported by lab robotics to increase reliability and speed in liquid handling media preparation.
Other articles by Lars Freier on PubMed
Multi-objective Global Optimization (MOGO): Algorithm and Case Study in Gradient Elution Chromatography Biotechnology Journal. | Pubmed ID: 28008726 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.
A Framework for Accelerated Phototrophic Bioprocess Development: Integration of Parallelized Microscale Cultivation, Laboratory Automation and Kriging-assisted Experimental Design Biotechnology for Biofuels. | Pubmed ID: 28163783 Even though microalgae-derived biodiesel has regained interest within the last decade, industrial production is still challenging for economic reasons. Besides reactor design, as well as value chain and strain engineering, laborious and slow early-stage parameter optimization represents a major drawback.
Robust Multi-Objective Global Optimization of Stochastic Processes With a Case Study in Gradient Elution Chromatography Biotechnology Journal. | Pubmed ID: 28887910 A novel algorithm for robust multi-objective process optimization under stochastic variability of environmental variables is introduced and applied to a case study in gradient elution chromatography. Process variability is accounted for by simultaneously optimizing several scenarios with random but fixed values of the environmental variables. These iterative optimizations are synchronized by planning the same experiments for all scenarios. Experiments are designed by maximizing the cumulative expected hypervolume improvement as predicted by several Gaussian process regression models. A straightforward method is presented for estimating the expected Pareto front and its variability based on the resulting data that maintains traceability of the corresponding process parameters. This information is required for robust process optimization, that is, determination of Pareto optimal processes that fulfil specific minimal criteria with a certain confidence. The presented algorithm can generally be applied to both in silico and wet lab experiments but involves an increased experimental effort as compared to the deterministic case.