A design of experiments approach for the rapid formulation of a chemically defined medium for metabolic profiling of industrially important microbes

Singleton, Chloe; Gilman, James; Rollit, Jessica; Zhang, Kun; Parker, David; Love, John

doi:10.1371/journal.pone.0218208

Cited by 21 publications

(20 citation statements)

References 42 publications

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“…There are three nodes in single hidden layer of ANN. Sigmoid activation functions were used, cross-validation of 19 of the media formulations defined in the second DOE iteration were randomly selected and withheld from ANN training set to do validation studies [59].…”

Section: Stepwise Regression and Artificial Neural Network Modellingmentioning

confidence: 99%

“…Historically several attempts have been made to design defined or semi-defined media recipes that do not require bio nutrient additives. Traditionally, media developed generally utilise one-factor-at-atime (OFAT) where all but one ingredient (factor) is fixed [58,59]. This approach guides towards determining individual factors that influence culture growth.…”

Section: Introductionmentioning

confidence: 99%

“…The full factorial design, DoE is attributed by important principals to allow for robust, valid experimentation. These are known as treatments, replicated, randomised to protect against hidden factors [59]. All treatments must be performed in blocks to control sources of variation.…”

Section: Introductionmentioning

confidence: 99%

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Strategies for Enhancing Product Yield: Design of Experiments (DOE) for Escherichia coli Cultivation

Gupta¹,

Edula²

2021

Fermentation - Processes, Benefits and Risks

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E. coli is considered one of the best model organism for biopharmaceutical production by fermentation. Its utility in process development is employed to develop various vaccines, metabolites, biofuels, antibiotics and synthetic molecules in large amounts based on the amount of yield in shake flasks, bioreactors utilised by batch, fed-batch and continuous mode. Production of the desired molecule is facilitated in the bioreactor by employing strategies to increase biomass and optimised yield. The fermentation is a controlled process utilising media buffers, micronutrients and macronutrients, which is not available in a shake flask. To maximise the production temperature, dissolved oxygen (aerobic), dissolved nitrogen (anaerobic), inducer concentration, feed or supplementation of nutrients is the key to achieving exponential growth rate and biomass. Design of experiments (DOE) is critical for attaining maximum gain, in cost-effective manner. DOE comprises of several strategies likewise Plakett-Burman., Box–Behnken, Artificial Neural Network, combination of these strategies leads to reduction of cost of production by 2–8 times depending on molecules to be produced. Further minimising downstream process for quickly isolation, purification and enrichment of the final product.

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Section: Stepwise Regression and Artificial Neural Network Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Strategies for Enhancing Product Yield: Design of Experiments (DOE) for Escherichia coli Cultivation

Gupta¹,

Edula²

2021

Fermentation - Processes, Benefits and Risks

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“…For cultivation of the pre-cultures, mLB (modified Luria-Bertani) medium was used as previously described [24]. The experiment was performed in a modified ammonium sulphate medium (ASM) [63], comprising 8.7 mM citric acid, 20.2 mM MgSO 4 , 10 mM K 2 SO 4 , 22.6 mM NaH 2 PO 4 , 0.8 mM CaCl 2 , 25 mM (NH 4 ) 2 SO 4 , 4.16 mM glucose and 1 mL each of the filter-sterilized trace elements: 6 mM H 2 SO 4 , 0.1 mM CuSO 4 , 0.2 mM CoSO 4 , 0.5 mM ZnSO 4 , 2.29 mM FeSO 4 , 0.3 mM NiSO 4 , 0.9 mM MnSO 4 and 0.1 mM H 3 BO 3 . The pH was adjusted to 6.8 with 4 M NaOH.…”

Section: Microorganism and Mediamentioning

confidence: 99%

Time-course Transcriptome of Parageobacillus thermoglucosidasius DSM 6285 Grown in the Presence of Carbon Monoxide and Air

Aliyu

Mohr

Cowan

et al. 2020

IJMS

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Parageobacillus thermoglucosidasius is a metabolically versatile, facultatively anaerobic thermophile belonging to the family Bacillaceae. Previous studies have shown that this bacterium harbours co-localised genes coding for a carbon monoxide (CO) dehydrogenase (CODH) and Ni-Fe hydrogenase (Phc) complex and oxidises CO and produces hydrogen (H2) gas via the water-gas shift (WGS) reaction. To elucidate the genetic events culminating in the WGS reaction, P. thermoglucosidasius DSM 6285 was cultivated under an initial gas atmosphere of 50% CO and 50% air and total RNA was extracted at ~8 (aerobic phase), 20 (anaerobic phase), 27 and 44 (early and late hydrogenogenic phases) hours post inoculation. The rRNA-depleted fraction was sequenced using Illumina NextSeq, v2.5, 1x75bp chemistry. Differential expression revealed that at 8 vs.. 20, 20 vs.. 27 and 27 vs.. 44 h post inoculation, 2190, 2118 and 231 transcripts were differentially (FDR < 0.05) expressed. Cluster analysis revealed 26 distinct gene expression trajectories across the four time points. Of these, two similar clusters, showing overexpression at 20 relative to 8 h and depletion at 27 and 44 h, harboured the CODH and Phc transcripts, suggesting possible regulation by O2. The transition between aerobic respiration and anaerobic growth was marked by initial metabolic deterioration, as reflected by up-regulation of transcripts linked to sporulation and down-regulation of transcripts linked to flagellar assembly and metabolism. However, the transcriptome and growth profiles revealed the reversal of this trend during the hydrogenogenic phase.

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“…DoE methods are very amenable to automation, as works by ( Carbonell et al, 2018 ) and ( Rajakumar et al, 2019 ) have demonstrated. They have been adapted to identify the media and culture conditions that maximize yield of the metabolic pathway ( Singleton et al, 2019 ) ( Azubuike et al, 2020 ), or the optimization of a cell-free system ( Spice et al, 2020 ). In the context of metabolic pathways, these methodologies have been applied to choose regulatory elements such as promoters ( Blazeck et al, 2012 ) and 5′UTR ( Salis et al, 2009 ), or to modify other popular dosage parameters such as plasmid copy number ( Ajikumar et al, 2010 ) and codon usage, ( Hanson and Coller, 2018 ; Wu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

Casas

Bultelle

Motraghi

et al. 2022

Front. Bioeng. Biotechnol.

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We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).

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A design of experiments approach for the rapid formulation of a chemically defined medium for metabolic profiling of industrially important microbes

Cited by 21 publications

References 42 publications

Strategies for Enhancing Product Yield: Design of Experiments (DOE) for Escherichia coli Cultivation

Strategies for Enhancing Product Yield: Design of Experiments (DOE) for Escherichia coli Cultivation

Time-course Transcriptome of Parageobacillus thermoglucosidasius DSM 6285 Grown in the Presence of Carbon Monoxide and Air

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

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