Kinetic modeling and sensitivity analysis of acetone–butanol–ethanol production

Shinto, Hideaki; Tashiro, Yukihiro; Mayu, Yamashita; Kobayashi, Genta; Sekiguchi, Tatsuya; Hanai, Taizo; Kuriya, Yuki; Okamoto, Masahiro; Sonomoto, Kenji

doi:10.1016/j.jbiotec.2007.05.005

Cited by 121 publications

(113 citation statements)

References 22 publications

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“…However, these models lack regulatory and dynamic information. A first kinetic simulation model, to describe the dynamics of ABE fermentation in the related Clostridium saccharoperbutylacetonicum 1-4 during growth in batch culture under condition of glucose depletion, was presented in (Shinto et al 2007). A first model of the pH-induced metabolic shift in C. acetobutylicum in phosphate-limited continuous culture, was published by Haus et al (2011), which took into account an adaptation of gene expression and proteome composition to the changing external pH.…”

Section: Kinetic Modelling Of the Metabolic Shiftmentioning

confidence: 99%

Coenzyme A-transferase-independent butyrate re-assimilation in Clostridium acetobutylicum—evidence from a mathematical model

Millat

Voigt

Janssen

et al. 2014

Appl Microbiol Biotechnol

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(2014) Coenzyme A-transferaseindependent butyrate re-assimilation in Clostridium acetobutylicum -evidence from a mathematical model. Applied Microbiology and Biotechnology, 98 (21 A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Coenzyme A-transferase-independent butyrate re-assimilation in Clostridium acetobutylicum -Evidence from a mathematical model Abstract: The hetero-dimeric CoA-transferase CtfA/B is believed to be crucial for the metabolic transition from acidogenesis to solventogenesis in Clostridium acetobutylicum as part of the industrial-relevant acetone-butanol-ethanol (ABE) fermentation. Here, the enzyme is assumed to mediate re-assimilation of acetate and butyrate during a pH-induced metabolic shift and to faciliate the first step of acetone formation from acetoacetyl-CoA. However, recent investigations using phosphate-limited continuous cultures have questioned this common dogma.To adress the emerging experimental discrepancies, we investigated the mutant strain Cac-ctfA398s::CT using chemostat cultures. As a consequence of this mutation, the cells are unable to express functional ctfA and are thus lacking CoA-transferase activity. A mathematical model of the pH-induced metabolic shift, which was recently developed for the wild type, is used to analyse the observed behaviour of the mutant strain with a focus on re-assimilation activities for the two produced acids.Our theoretical analysis reveals that the ctfA mutant still re-assimilates butyrate, but not acetate. Based upon this finding, we conclude that C. acetobutylicum possesses a CoA-tranferase-independent butyrate uptake mechanism that is activated by decreasing pH levels. Furthermore, we observe that butanol formation is not inhibited under our experimental conditions, as suggested by previous batch culture experiments. In concordance with recent batch experiments, acetone formation is abolished in chemostat cultures using the ctfa mutant.

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Section: Kinetic Modelling Of the Metabolic Shiftmentioning

confidence: 99%

Coenzyme A-transferase-independent butyrate re-assimilation in Clostridium acetobutylicum—evidence from a mathematical model

Millat

Voigt

Janssen

et al. 2014

Appl Microbiol Biotechnol

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“…They found that butanol concentration was more related to the type and amount of carbohydrate utilized. A previous report from our laboratory by applying a sensitivity analysis using glucose and xylose as the substrates also revealed that the strain N1-4 could produce higher butanol in a substrate-dependent pathway (Shinto et al 2008;Shinto et al 2007). employing C. thermocellum (Ng and Zeikus 1982) and a cellulolytic mesophilic Clostridium sp.…”

Section: Discussionmentioning

confidence: 63%

Production of Acetone, Butanol and Ethanol as Bioenergy Source Materials by Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) using Different Substrates

Ambarsari¹,

Sonomoto

2013

Microbiol Indones

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The cost of raw materials has been known to be the major limitation on the economic feasibility of acetone-butanol-ethanol (ABE) production. About 60-70% of the total ABE production cost is the cost for substrates (Madihah et al. 2001). Therefore, many researches have been conducted to find the most economical substrates for ABE production, for examples those who used simple sugars such as glucose, lactose, galactose, and xylose (Shinto et al.2008;Keis et al. 2001;Bahl et al. 1986), or moreIn the effort of developing bioenergy source materials, a laboratorium study was performed to investigate the effects of substrate types and concentrations on the production of acetone-butanol-ethanol (ABE) from various substrates by Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) in defined TYA (Tryptone-Yeast extract-Acetate) media. The results demonstrated that the ABE ratios produced by the strain N1-4 were greatly influenced by the types of substrate and the concentrations being used. It was also shown that fermentation of disaccharides (cellobiose and dextrin) by the strain N1-4 could give relatively as high butanol and ABE yields as glucose fermentation after 24 h fermentation time. The strain N1-4 was also found to be able to ferment xylans from birchwood sources producing a relatively high ABE concentration, especially at a prolonged incubation time (after 48 h incubation period). A subsequent experiment using several different concentrations of glucose or cellobiose revealed that the higher the concentration of the substrate being used, then the higher the total ABE concentration or productivity could be obtained but not necessarily in the same increasing ratio. To the best of our knowledge, there was no other previous study about the effect of substrate type and concentration on the ABE ratio by C. saccharoperbutylacetonicum N1-4 (ATCC 13564) using various substrates in defined TYA media, specifically by using xylans and dextrin substrates.Key words: ABE ratio, acetone-butanol-ethanol fermentation, biomass substrate effect, Clostridium saccharoperbutylacetonicum N1-4Dalam rangka mendapatkan bahan baku bioenergi, sebuah studi laboratorium dilakukan untuk mengetahui pengaruh dari jenis dan konsentrasi substrat biomasa terhadap proses fermentasi langsung yang menghasilkan aseton, butanol, dan etanol oleh bakteri Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) dalam media TYA (Tryptone-Yeast extract-Acetate) yang terdefinisi. Hasil penelitian menunjukkan bahwa rasio dari aceton:butanol:etanol (rasio ABE) ternyata sangat dipengaruhi oleh jenis substrat biomass dan konsentrasi yang dipergunakan dalam proses fermentasinya. Data menunjukkan bahwa dengan menggunakan substrat disakarida (yang diwakili oleh dextrin dan cellobiose), strain N1-4 mampu menghasilkan ABE dengan yield yang relatif sama tinggi dengan menggunakan glukosa setelah masa fermentasi 24 jam. Strain N1-4 ternyata juga mampu memproduksi ABE dengan konsentrasi yang cukup tinggi dengan menggunakan substrat xylan yang diekstraksi dari kayu, wa...

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“…Other models were recently proposed in the literature and were tested in this investigation. Shinto et al [12] presented a kinetic model that considers numerous intermediates in the metabolic pathway as well as product and glucose inhibition. However, this model could not be used, as it leads to unrealistically high butanol concentration, far beyond the inhibitory concentration levels.…”

Section: Description Of the Integrated Abe Fermentation-membrane Pervmentioning

confidence: 99%

Artificial Neural Networks as Metamodels for the Multiobjective Optimization of Biobutanol Production

2018

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Process optimization using a physical process or its comprehensive model often requires a significant amount of time. To remedy this problem, metamodels, or surrogate models, can be used. In this investigation, a methodology for optimizing the biobutanol production process via the integrated acetone-butanol-ethanol (ABE) fermentation-membrane pervaporation process is proposed. In this investigation, artificial neural networks (ANNs) were used as metamodels in an attempt to reduce the time needed to circumscribe the Pareto domain and identify the best optimal operating conditions. Two different metamodels were derived from a small set of operating conditions obtained from a uniform experimental design. The first series of metamodels were derived to entirely replace the phenomenological model of the butanol fermentation process by representing the relationship that exists between five operating conditions and four performance criteria. The second series of metamodels were derived to estimate the initial concentrations under steady-state conditions for the eight chemical species within the fermenter in order to expedite convergence of the process simulator. The first series of metamodels led to an accurate Pareto domain and reduced the computation time to circumscribe the Pareto domain by a factor of 2500. The second series of metamodels led to only a small reduction of computation time (a factor of approximately 2) because of the inherently slow convergence of the overall fermentation process.

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Kinetic modeling and sensitivity analysis of acetone–butanol–ethanol production

Cited by 121 publications

References 22 publications

Coenzyme A-transferase-independent butyrate re-assimilation in Clostridium acetobutylicum—evidence from a mathematical model

Coenzyme A-transferase-independent butyrate re-assimilation in Clostridium acetobutylicum—evidence from a mathematical model

Production of Acetone, Butanol and Ethanol as Bioenergy Source Materials by Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) using Different Substrates

Artificial Neural Networks as Metamodels for the Multiobjective Optimization of Biobutanol Production

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