Use of a genetic algorithm in the development of a synthetic growth medium for <i>Arthrobacter simplex</i> with high hydrocortisone Δ<sup>1</sup>‐dehydrogenase activity

Weuster‐Botz, Dirk; Pramatarova, V.; Spassov, G.; Wandrey, Christian

doi:10.1002/jctb.280640411

Cited by 35 publications

(20 citation statements)

References 13 publications

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“…Consequently, the experimental search for a useful induction procedure applying a global random search method of generations like a Genetic Algorithm was a meaningful approach. The configuration of the simple Genetic Algorithm applied for experimental optimization of IPTG addition was identical to former applications for medium optimization in parallel shake-flask experiments [15,16]. The low number of variables (6 parameters of the feeding functions instead of 12±14 medium components) and the reduced number of levels for each variable (16 instead of more than 100) resulted in the low number of generations performed (3 sets of parallel experiments) compared to the medium optimizations.…”

Section: Discussionmentioning

confidence: 99%

“…This constraint resulted in the following parameter settings: A 1 = ±A 2 = 0.007 ± 0.112 ml min ±1 , B 1 = 0.4 ± 4.8 1 h ±1 , B 2 = 4 ± 8 h ±1 , C 1 = 0 ± 0.7 h, C 2 = 2.85 ± 4.5 h and substrate feed rate: 0.0025±0.04 ml min ±1 . The levels of each parameter were set to 16. Therefore a bit string of length 4 gives the required accuracy for each parameter and a string of 24 bits coded for one experiment.…”

Section: Optimization Of Inducer Profilesmentioning

confidence: 99%

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Parallel Bubble Columns with Fed-Batch Technique for Microbial Process Development on a Small Scale

Altenbach-Rehm

Nell

Arnold³

et al. 1999

Chem. Eng. Technol.

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12 small-scale bubble columns, each and every column with a total volume of 500 ml, individually controlled distribution of sterile and humidified air and a pH-probe, were operated in an incubation chamber with temperature control. Intermittent feeding of substrate or inducer, as well as of base for parallel pH-control was achieved by connecting a precision syringe pump to a valve distributor with one 2/2-way miniature valve for each bubble column. This new parallel bio-reactor technique was applied for optimization of the feed profile of the chemical inducer isopropyl-b-D-thiogalactoside (IPTG) to improve the expression of a foreign protein (guanosin-5©-diphosphate-a-D-mannose-pyrophosphorylase, GDP-manPP) under the control of the lac promoter in Escherichia coli. A mean cell density of 8 g l ±1 of recombinant E. coli was achieved in parallel fed-batch fermentations within 9.5 h due to a sufficient oxygen transfer (k L a of up to 0.2 s ±1) and parallel pH-control. The GDP-manPP activity was improved by more than 100 % compared to the standard IPTG pulse within 3 sets of parallel fed-batch fermentations. A Genetic Algorithm was used for optimization of the IPTG feed profile. Scale-up of the optimized fed-batch process into the stirred tank reactor (scale-up factor of 20) was possible, if all reaction conditions were copied exactly with respect to the reactor volume. A final GDP-manPP activity of 715 U l ±1 was measured 3 h after IPTG induction was finished.

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Section: Discussionmentioning

confidence: 99%

Section: Optimization Of Inducer Profilesmentioning

confidence: 99%

Parallel Bubble Columns with Fed-Batch Technique for Microbial Process Development on a Small Scale

Altenbach-Rehm

Nell

Arnold³

et al. 1999

Chem. Eng. Technol.

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“…Despite the long history of GAs and their current widespread use, there have been only a few examples of using GAs in fermentation technology. For instance, recently, WeusterBotz et al (56)(57)(58)(59) and Zuzek et al (67) used GAs to study medium optimization (for instance, to maximize hydrocortisone ⌬ 1 -dehydrogenase activity in Arthrobacter simplex cultures in a synthetic medium [58]). In our proof of principle experiments we examined whether it is feasible to use GAs to study an experimental system that is significantly more complex.…”

Section: Discussionmentioning

confidence: 99%

Efficient Improvement of Silage Additives by Using Genetic Algorithms

Davies

Merry

Kell

et al. 2000

Appl Environ Microbiol

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The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a "fitness" value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a "cost" element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We found that these combinations compared favorably both with uninoculated silage and with a commercial silage additive. The evolutionary computing methods described here are a convenient and efficient approach for designing silage additives.The ensiling of forage crops in order to obtain winter or buffer feed for ruminant livestock is widely practiced in advanced management systems in temperate regions. The aim is to preserve crops having high moisture contents by encouraging rapid fermentation of water-soluble carbohydrates (WSC) in the crops to lactic acid by epiphytic lactic acid bacteria (LAB), which decreases the pH and inhibits the activities of plant enzymes and pathogenic or spoilage bacteria that could decrease the nutritive value of the silage.Grass is the predominant crop ensiled in Europe, and 50 million tons of grass silage are made each year in the United Kingdom alone (23, 61, 62). As with maize, the main crop ensiled in the United States, high WSC levels and a low buffering capacity in this crop are conducive to rapid acidification by epip...

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“…The first n-DOE algorithm (RBFNN-TGA) includes a radial basis function neural network (RBFNN) modeling technique and a new search algorithm, a truncated genetic algorithm (TGA), to suggest a new set of experiments based on a developing knowledge base. 5 Although others have previously reported the successful use of pure genetic algorithms (GAs) for fermentation optimization 3,[7][8][9] or GAs to find the optimum of a neural network (NN) model for a fermentation process, 10,11 our method differs from these approaches. It uses an iterative approach of developing a NN model with all data available up to the current point in experimentation, followed by a search method (TGA) specifically designed to identify new experiments using the information in the model as a guide.…”

Section: Introductionmentioning

confidence: 92%

Using highly efficient nonlinear experimental design methods for optimization of Lactococcus lactis fermentation in chemically defined media

Zhang

Block

2009

Biotechnology Progress

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Optimization of fermentation media and processes is a difficult task due to the potential for high dimensionality and nonlinearity. Here we develop and evaluate variations on two novel and highly efficient methods for experimental fermentation optimization. The first approach is based on using a truncated genetic algorithm with a developing neural network model to choose the best experiments to run. The second approach uses information theory, along with Bayesian regularized neural network models, for experiment selection. To evaluate these methods experimentally, we used them to develop a new chemically defined medium for Lactococcus lactis IL1403, along with an optimal temperature and initial pH, to achieve maximum cell growth. The media consisted of 19 defined components or groups of components. The optimization results show that the maximum cell growth from the optimal process of each novel method is generally comparable to or higher than that achieved using a traditional statistical experimental design method, but these optima are reached in about half of the experiments (73-94 vs. 161, depending on the variants of methods). The optimal chemically defined media developed in this work are rich media that can support high cell density growth 3.5-4 times higher than the best reported synthetic medium and 72% higher than a commonly used complex medium (M17) at optimization scale. The best chemically defined medium found using the method was evaluated and compared with other defined or complex media at flask- and fermentor-scales.

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Use of a genetic algorithm in the development of a synthetic growth medium for Arthrobacter simplex with high hydrocortisone Δ¹‐dehydrogenase activity

Cited by 35 publications

References 13 publications

Parallel Bubble Columns with Fed-Batch Technique for Microbial Process Development on a Small Scale

Parallel Bubble Columns with Fed-Batch Technique for Microbial Process Development on a Small Scale

Efficient Improvement of Silage Additives by Using Genetic Algorithms

Using highly efficient nonlinear experimental design methods for optimization of Lactococcus lactis fermentation in chemically defined media

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Use of a genetic algorithm in the development of a synthetic growth medium for Arthrobacter simplex with high hydrocortisone Δ1‐dehydrogenase activity

Cited by 35 publications

References 13 publications

Parallel Bubble Columns with Fed-Batch Technique for Microbial Process Development on a Small Scale

Parallel Bubble Columns with Fed-Batch Technique for Microbial Process Development on a Small Scale

Efficient Improvement of Silage Additives by Using Genetic Algorithms

Using highly efficient nonlinear experimental design methods for optimization of Lactococcus lactis fermentation in chemically defined media

Contact Info

Product

Resources

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Use of a genetic algorithm in the development of a synthetic growth medium for Arthrobacter simplex with high hydrocortisone Δ¹‐dehydrogenase activity