Anodic respiration of Pseudomonas putida KT2440 in a stirred-tank bioreactor

Hintermayer, Sarah Beate; Yu, Shiqin; Krömer, Jens O.; Weuster‐Botz, Dirk

doi:10.1016/j.bej.2016.07.020

Cited by 35 publications

(24 citation statements)

References 43 publications

(37 reference statements)

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“…The sigmoidal Richards function was used to interpolate biomass and substrate concentrations as a function of process time in the batch cultivation processes. Specific growth rates

(μ = c_{normalX}^{- 1} d c_{normalX} d t^{- 1})

, substrate uptake rates

(q_{normalS} = - c_{normalX}^{- 1} d c_{normalS} d t^{- 1})

and biomass yields

(Y_{XS} = μ q_{normalS}^{- 1})

were calculated with the interpolated biomass and substrate concentrations as described previously .…”

Section: Methodsmentioning

confidence: 99%

“…which was derived from Eq. by mirroring it in the abscissa and vertically shifting it by a .

c_{normalS} (t) = a \cdot (1 - {(1 + (d - 1) \cdot e^{- k \cdot (t - b)})}^{\frac{1}{1 - d}})

…”

Section: Methodsmentioning

confidence: 99%

“…− were calculated with the interpolated biomass and substrate concentrations as described previously [28].…”

Section: Reaction Engineering Analysismentioning

confidence: 99%

“…(2) which was derived from Eq. (1) by mirroring it in the abscissa and vertically shifting it by a [28].…”

Section: Interpolation Of Biomass and Substrate Concentrationsmentioning

confidence: 99%

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Experimental validation of in silico estimated biomass yields of Pseudomonas putida KT2440

Hintermayer

Weuster‐Botz

2017

Biotechnology Journal

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Pseudomonas putida is rapidly becoming a microbial cell platform for biotechnological applications. In order to understand genotype-phenotype relationships genome scale models represent helpful tools. However, the validation of in silico predictions of genome scale models is a task that is rarely performed. In this study the theoretical biomass yields of Pseudomonas putida KT2440 were estimated for 57 different carbon sources based on a genome scale stoichiometric model applying flux balance analysis. The batch growth of P. putida KT2440 with six individual carbon sources covering the range of maximal to minimal in silico biomass yields (acetate, glycerol, citrate, succinate, malate and methanol, respectively) was studied in a defined mineral medium in a fully controlled stirred-tank bioreactor on a 3 L scale. The highest growth rate of P. putida KT2440 was measured with succinate as carbon source (0.51 h ). Among the 57 carbon sources tested, glycerol resulted in the highest estimated biomass yield (0.61 molC molC ) which was experimentally confirmed. The comparison of experimental determined biomass yields with a modified version of the model iJP815 showed deviations of only up to 10%. The experimental data generated in this study can also be used in future studies to further improve the genome scale models of P. putida KT2440. Improved models will then help to gain deeper insights in genotype-phenotype relationships.

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“…The sigmoidal Richards function was used to interpolate biomass and substrate concentrations as a function of process time in the batch cultivation processes. Specific growth rates

(μ = c_{normalX}^{- 1} d c_{normalX} d t^{- 1})

, substrate uptake rates

(q_{normalS} = - c_{normalX}^{- 1} d c_{normalS} d t^{- 1})

and biomass yields

(Y_{XS} = μ q_{normalS}^{- 1})

were calculated with the interpolated biomass and substrate concentrations as described previously .…”

Section: Methodsmentioning

confidence: 99%

“…which was derived from Eq. by mirroring it in the abscissa and vertically shifting it by a .

c_{normalS} (t) = a \cdot (1 - {(1 + (d - 1) \cdot e^{- k \cdot (t - b)})}^{\frac{1}{1 - d}})

…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Experimental validation of in silico estimated biomass yields of Pseudomonas putida KT2440

Hintermayer

Weuster‐Botz

2017

Biotechnology Journal

Self Cite

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“…A suitable BES design for scale up might be a stirred tank reactor with an “upgrade kit” to allow bioelectrochemical processes within the reactor. This has been shown for the production of para‐hydroxybenzoate in a single chamber reactor with up to 2.5 L working volume , for current production in a single chamber system with 2.5 L , for current production in a two chamber electro‐stirred tank reactor with up to two liter and for the production of organic acids in a two chamber reactor with up to 2 l . It has also been shown that CFD (Computational Fluid Dynamics) studies for the “upgraded” bioreactors reveal the mixing conditions within the reactor, so it might be possible to use the knowledge gained from CFD simulations for the scale up of the systems .…”

Section: Scale Up In Electrobiotechnologymentioning

confidence: 98%

Same but different–Scale up and numbering up in electrobiotechnology and photobiotechnology

Enzmann

Stöckl

Zeng

et al. 2019

Engineering in Life Sciences

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Facing energy problems, there is a strong demand for new technologies dealing with the replacement of fossil fuels. The emerging fields of biotechnology, photobiotechnology and electrobiotechnology, offer solutions for the production of fuels, energy, or chemicals using renewable energy sources (light or electrical current e.g. produced by wind or solar power) or organic (waste) substrates. From an engineering point of view both technologies have analogies and some similar challenges, since both light and electron transfer are primarily surface‐dependent. In contrast to that, bioproduction processes are typically volume dependent. To allow large scale and industrially relevant applications of photobiotechnology and electrobiotechnology, this opinion first gives an overview over the current scales reached in these areas. We then try to point out the challenges and possible methods for the scale up or numbering up of the reactors used. It is shown that the field of photobiotechnology is by now much more advanced than electrobiotechnology and has achieved industrial applications in some cases. We argue that transferring knowledge from photobiotechnology to electrobiotechnology can speed up the development of the emerging field of electrobiotechnology. We believe that a combination of scale up and numbering up, as it has been shown for several photobiotechnological reactors, may well lead to industrially relevant scales in electrobiotechnological processes allowing an industrial application of the technology in near future.

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Microbial Electrosynthesis—An Inventory on Technology Readiness Level and Performance of Different Process Variants

Fruehauf

Enzmann

Harnisch

et al. 2020

Biotechnology Journal

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The objective of microbial electrosynthesis (MES) is to combine the advantages of electrochemistry and biotechnology in order to produce chemicals and fuels. This combination enables resource-efficient processes by using renewable raw materials and regenerative energies. In the last decade, different MES processes have been described, for example, MES based on biofilms or mediators, electro-fermentation, and secondary MES. This review compares the MES technologies with regard to the reached process performances in terms of key process indicators (i.e., coulombic efficiency (CE), product titre, productivity) and technology readiness level (TRL). Often the underlying mechanism of electron transfer in biofilm-based processes has not been elucidated and can therefore not be optimized. Similarly, technical aspects of electro-fermentation processes and processes with soluble mediators are under investigation and techno-economic assessments are missing. In contrast, the electrochemical production of microbial substrates in secondary MES or hybrid systems show high key process indicators and TRLs up to 7. In summary, the different types of MES processes offer options for today's industrial use, as well as an exciting and future-oriented technology that can be applied in a medium-term perspective.

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Anodic respiration of Pseudomonas putida KT2440 in a stirred-tank bioreactor

Cited by 35 publications

References 43 publications

Experimental validation of in silico estimated biomass yields of Pseudomonas putida KT2440

Experimental validation of in silico estimated biomass yields of Pseudomonas putida KT2440

Same but different–Scale up and numbering up in electrobiotechnology and photobiotechnology

Microbial Electrosynthesis—An Inventory on Technology Readiness Level and Performance of Different Process Variants

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