Efficient improvement of silage additives by using genetic algorithms

Improvement of the quality of maize grain silage by a synergistic action of selected lactobacilli strains

As silage is one of the most important feed sources for dairy cattle it is recommended for farmers to preserve silage by fermentation. Interaction of the five strains of Lactobacillus genera [Lactobacillus buchneri A KKP 2047 p (LB), L. reuteri M KKP 2048 p (LR), L. plantarum K KKP 593 p (LPk), L. plantarum S KKP 2021 p (LPs), L. fermentum N KKP 2020 p (LF)] has been shown aiming to increase the safety of corn grain silage fodder. Experiments were conducted in polyethylene microsilos for 48 days and on production scale in an experimental farm for 3 years. Synergistic activity of the studied bacterial strains in terms of reducing aflatoxin B₁ and ochratoxin A levels was clear in these experimental variants wherein to the inoculants of the LB + LR strains subsequent bacterial strains LPk, LPs and LF were sequentially added. Silages inoculated with five bacterial strains were free from pathogens and showed the lowest yeast and mold count values among all experimental variants. As a result of employing the preparation starter culture for ensiling corn grain there were obtained silages characterized by high stability, microbiological and chemical purity, thus safe in feeding livestock.

On Design Mining: Coevolution and Surrogate Models

Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this article, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design-threads due to the overall complexity of the task. Using an abstract, tunable model of coevolution, we consider strategies to sample subthread designs for whole-system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, we then describe the effective design of an array of six heterogeneous vertical-axis wind turbines.

ASMiGA: an archive-based steady-state micro genetic algorithm

We propose a new archive-based steady-state micro genetic algorithm (ASMiGA). In this context, a new archive maintenance strategy is proposed, which maintains a set of nondominated solutions in the archive unless the archive size falls below a minimum allowable size. It makes the archive size adaptive and dynamic. We have proposed a new environmental selection strategy and a new mating selection strategy. The environmental selection strategy reduces the exploration in less probable objective spaces. The mating selection increases searching in more probable search regions by enhancing the exploitation of existing solutions. A new crossover strategy DE-3 is proposed here. ASMiGA is compared with five well-known multiobjective optimization algorithms of different types-generational evolutionary algorithms (SPEA2 and NSGA-II), archive-based hybrid scatter search, decomposition-based evolutionary approach, and archive-based micro genetic algorithm. For comparison purposes, four performance measures (HV, GD, IGD, and GS) are used on 33 test problems, of which seven problems are constrained. The proposed algorithm outperforms the other five algorithms.

Using a genetic algorithm to drive a microbial ecosystem in a desirable direction

The functioning of natural microbial ecosystems is influenced by various biotic and abiotic conditions. The careful experimental manipulation of environmental conditions can drive microbial ecosystems toward exhibiting desirable types of functionality. Such manipulations can be systematically approached by viewing them as a combinatorial optimization problem, in which the optimal configuration of environmental conditions is sought. Such an effort requires a sound optimization technique. Genetic algorithms are a class of optimization methods that should be suitable for such a task because they can deal with multiple interacting variables and with experimental noise and because they do not require an intricate understanding or modelling of the ecosystem of interest. We propose the use of genetic algorithms to drive undefined microbial ecosystems in desirable directions by combinatorially optimizing sets of environmental conditions. We tested this approach in a model system where the microbial ecosystem of a human saliva sample was manipulated in successive steps to display increasing amounts of azo dye decoloration. The results of our experiments indicated that a genetic algorithm was capable of optimizing ecosystem function by manipulating the presence or absence of a set of 10 chemical supplements. Genetic algorithms hold promise for use as a tool in environmental microbiology for the efficient control of the functioning of natural and undefined microbial ecosystems.

Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.

Screening and selection of lactic acid bacteria strains suitable for ensiling grass

AIM

Lactic acid bacteria (LAB) strains shown to have broad-spectrum antimicrobial activity were screened for potential as grass silage inoculants. The strains capable of rapidly lowering the pH of the grass matrix and with low proteolytic activity were assessed in laboratory-scale silos in a grass matrix containing natural microbial flora.

METHODS AND RESULTS

Screening of nine candidate strains was performed first in a grass extract medium. The four most promising strains were selected on the basis of growth rate in the medium, capacity to reduce pH and ability to limit the formation of ammonia-N. The efficiency of the selected strains was further assessed in a laboratory-scale ensiling experiment. Untreated (no additive) and formic acid served as controls. All tested inoculants improved silage quality compared with untreated. With one exception (Pediococcus parvulus E315) the fermentation losses in the inoculated silages were even lower than in the acid-treated control silage. Pure lactic acid fermentation was obtained in the timothy-meadow fescue silage with all inoculants. The results obtained in the ensiling experiments were consistent with those of the screening procedure, which appeared to predict correctly the potential of LAB as silage inoculants. The strains with a low ammonia production rate in the grass extract medium behaved similarly in the silage. Especially in this respect the strain Lactobacillus plantarum E76 was superior to the other candidates.

CONCLUSIONS

The screening method using grass extract proved to be useful in strain selection.

SIGNIFICANCE AND IMPACT OF THE STUDY

The rapid screening method developed for the LAB strains provides a useful tool for more systematic product development of commercial inoculant preparations. Time consuming and laborious ensiling experiments can be limited only to the most promising strains.

Cultivation of denitrifying bacteria: optimization of isolation conditions and diversity study

An evolutionary algorithm was applied to study the complex interactions between medium parameters and their effects on the isolation of denitrifying bacteria, both in number and in diversity. Growth media with a pH of 7 and a nitrogen concentration of 3 mM, supplemented with 1 ml of vitamin solution but not with sodium chloride or riboflavin, were the most successful for the isolation of denitrifiers from activated sludge. The use of ethanol or succinate as a carbon source and a molar C/N ratio of 2.5, 20, or 25 were also favorable. After testing of 60 different medium parameter combinations and comparison with each other as well as with the standard medium Trypticase soy agar supplemented with nitrate, three growth media were highly suitable for the cultivation of denitrifying bacteria. All evaluated isolation conditions were used to study the cultivable denitrifier diversity of activated sludge from a municipal wastewater treatment plant. One hundred ninety-nine denitrifiers were isolated, the majority of which belonged to the Betaproteobacteria (50.4%) and the Alphaproteobacteria (36.8%). Representatives of Gammaproteobacteria (5.6%), Epsilonproteobacteria (2%), and Firmicutes (4%) and one isolate of the Bacteroidetes were also found. This study revealed a much more diverse denitrifying community than that previously described in cultivation-dependent research on activated sludge.

Evolutionary algorithms and flow cytometry to examine the parameters influencing transconjugant formation

An evolutionary algorithm was used to determine the optimal combination of parameters for transconjugant formation. As a model system, a gfp tagged TOL plasmid pWW0 was chosen to examine transfer from Pseudomonas putida to Escherichia coli. A comparison of flow cytometry results with plating and microscopy showed that the majority of transconjugants were not culturable. The transconjugant ratio therefore was determined by flow cytometry. The evolutionary algorithm showed that the optimal conditions were obtained at 28 degrees C and at the highest nutrient concentrations. This work demonstrates that evolutionary algorithms can be used to find optimal parameter interactions in environmental microbiology.

Cultivation of denitrifying bacteria: optimization of isolation conditions and diversity study

An evolutionary algorithm was applied to study the complex interactions between medium parameters and their effects on the isolation of denitrifying bacteria, both in number and in diversity. Growth media with a pH of 7 and a nitrogen concentration of 3 mM, supplemented with 1 ml of vitamin solution but not with sodium chloride or riboflavin, were the most successful for the isolation of denitrifiers from activated sludge. The use of ethanol or succinate as a carbon source and a molar C/N ratio of 2.5, 20, or 25 were also favorable. After testing of 60 different medium parameter combinations and comparison with each other as well as with the standard medium Trypticase soy agar supplemented with nitrate, three growth media were highly suitable for the cultivation of denitrifying bacteria. All evaluated isolation conditions were used to study the cultivable denitrifier diversity of activated sludge from a municipal wastewater treatment plant. One hundred ninety-nine denitrifiers were isolated, the majority of which belonged to the Betaproteobacteria (50.4%) and the Alphaproteobacteria (36.8%). Representatives of Gammaproteobacteria (5.6%), Epsilonproteobacteria (2%), and Firmicutes (4%) and one isolate of the Bacteroidetes were also found. This study revealed a much more diverse denitrifying community than that previously described in cultivation-dependent research on activated sludge.

Usefulness of length heterogeneity-PCR for monitoring lactic acid bacteria succession during maize ensiling

The use of length-heterogeneity PCR was explored to monitor lactic acid bacteria succession during ensiling of maize. Bacterial diversity was studied during the fermentation of 30-day-old maize in optimal and spoilage-simulating conditions. A length heterogeneity PCR profile database of lactic acid bacteria isolated from the silage and identified by 16S rRNA gene sequencing was established. Although interoperonic 16S rRNA gene length polymorphisms were detected in some isolates, strain analysis showed that most of the lactic acid bacteria species thriving in silage could be discriminated by this method. The length heterogeneity PCR profiles of bacterial communities during maize fermentation were compared with those on a database. Under optimal fermentation conditions all the ecological indices of bacterial diversity, richness and evenness, deduced from community profiles, increased until day thirteen of fermentation and then decreased to the initial values. Pediococcus and Weissella dominated, especially in the first days of fermentation. Lactococcus lactis ssp. lactis and Lactobacillus brevis were mainly found after six days of fermentation. A peak corresponding to Lactobacillus plantarum was present in all the fermentation phases, but was only a minor fraction of the population. Unsuitable fermentation conditions and withered maize leaves in the presence of oxygen and water excess caused an enrichment of Enterococcus sp. and Enterobacter sp.

Closed-Loop, Multiobjective Optimization of Analytical Instrumentation: Gas Chromatography/Time-of-Flight Mass Spectrometry of the Metabolomes of Human Serum and of Yeast Fermentations

The number of instrumental parameters controlling modern analytical apparatus can be substantial, and varying them systematically to optimize a particular chromatographic separation, for example, is out of the question because of the astronomical number of combinations that are possible (i.e., the "search space" is very large). However, heuristic methods, such as those based on evolutionary computing, can be used to explore such search spaces efficiently. We here describe the implementation of an entirely automated (closed-loop) strategy for doing this and apply it to the optimization of gas chromatographic separations of the metabolomes of human serum and of yeast fermentation broths. Without human intervention, the Robot Chromatographer system (i) initializes the settings on the instrument, (ii) controls the analytical run, (iii) extracts the variables defining the analytical performance (specifically the number of peaks, signal/noise ratio, and run time), (iv) chooses (via the PESA-II multiobjective genetic algorithm), and (v) programs the next series of instrumental settings, the whole continuing in an iterative cycle until suitable sets of optimal conditions have been established. Genetic programming was used to remove noise peaks and to establish the basis for the improvements observed. The system showed that the number of peaks observable depended enormously on the conditions used and served to increase them by as much as 3-fold (e.g., to over 950 in human serum) while in many cases maintaining or reducing the run time and preserving excellent signal/noise ratios. The evolutionary closed-loop machine learning strategy we describe is generic to any type of analytical optimization.

Metabolic fingerprinting of salt-stressed tomatoes

The aim of this study was to adopt the approach of metabolic fingerprinting through the use of Fourier transform infrared (FT-IR) spectroscopy and chemometrics to study the effect of salinity on tomato fruit. Two varieties of tomato were studied, Edkawy and Simge F1. Salinity treatment significantly reduced the relative growth rate of Simge F1 but had no significant effect on that of Edkawy. In both tomato varieties salt-treatment significantly reduced mean fruit fresh weight and size class but had no significant affect on total fruit number. Marketable yield was however reduced in both varieties due to the occurrence of blossom end rot in response to salinity. Whole fruit flesh extracts from control and salt-grown tomatoes were analysed using FT-IR spectroscopy. Each sample spectrum contained 882 variables, absorbance values at different wavenumbers, making visual analysis difficult and therefore machine learning methods were applied. The unsupervised clustering method, principal component analysis (PCA) showed no discrimination between the control and salt-treated fruit for either variety. The supervised method, discriminant function analysis (DFA) was able to classify control and salt-treated fruit in both varieties. Genetic algorithms (GA) were applied to identify discriminatory regions within the FT-IR spectra important for fruit classification. The GA models were able to classify control and salt-treated fruit with a typical error, when classifying the whole data set, of 9% in Edkawy and 5% in Simge F1. Key regions were identified within the spectra corresponding to nitrile containing compounds and amino radicals. The application of GA enabled the identification of functional groups of potential importance in relation to the response of tomato to salinity.

Optimization of a feed medium for fed-batch culture of insect cells using a genetic algorithm

Insect cells have been cultured for over 30 years, but their application is still hampered by low cell densities in batch fermentations and expensive culture media. With respect to the culture method, the fed-batch culture mode is often found to give the best yields. However, optimization of the feed composition is usually a laborious task. In this report, the successful use of genetic algorithms (GAs) to optimize the growth of insect cells is described. A feed was developed from 11 different medium components, each used at a wide range of concentrations. The feed was optimized within four sets of 20 experiments. The optimized feed was tested in bioreactors and the addition scheme was further improved. The viable-cell density of HzAm1 (Helicoverpa zea) insect cells improved 550% to 19.5 x 10(6) cells/mL compared to a control fermentation in an optimized commercial medium. No accumulation of waste products was found, and none of the amino acids was depleted. Glucose was depleted, which suggests that even further improvement is possible. We show that GAs are a successful method to optimize a complex fermentation in a relatively short time frame and without the need of detailed information concerning the cellular physiology or metabolism.

Phylogenetic diversity of lactic acid bacteria associated with paddy rice silage as determined by 16S ribosomal DNA analysis

A total of 161 low-G+C-content gram-positive bacteria isolated from whole-crop paddy rice silage were classified and subjected to phenotypic and genetic analyses. Based on morphological and biochemical characters, these presumptive lactic acid bacterium (LAB) isolates were divided into 10 groups that included members of the genera Enterococcus, Lactobacillus, Lactococcus, Leuconostoc, Pediococcus, and WEISSELLA: Analysis of the 16S ribosomal DNA (rDNA) was used to confirm the presence of the predominant groups indicated by phenotypic analysis and to determine the phylogenetic affiliation of representative strains. The virtually complete 16S rRNA gene was PCR amplified and sequenced. The sequences from the various LAB isolates showed high degrees of similarity to those of the GenBank reference strains (between 98.7 and 99.8%). Phylogenetic trees based on the 16S rDNA sequence displayed high consistency, with nodes supported by high bootstrap values. With the exception of one species, the genetic data was in agreement with the phenotypic identification. The prevalent LAB, predominantly homofermentative (66%), consisted of Lactobacillus plantarum (24%), Lactococcus lactis (22%), Leuconostoc pseudomesenteroides (20%), Pediococcus acidilactici (11%), Lactobacillus brevis (11%), Enterococcus faecalis (7%), Weissella kimchii (3%), and Pediococcus pentosaceus (2%). The present study, the first to fully document rice-associated LAB, showed a very diverse community of LAB with a relatively high number of species involved in the fermentation process of paddy rice silage. The comprehensive 16S rDNA-based approach to describing LAB community structure was valuable in revealing the large diversity of bacteria inhabiting paddy rice silage and enabling the future design of appropriate inoculants aimed at improving its fermentation quality.