Dashing Growth Curves – a web application for rapid and interactive analysis of microbial growth curves

Reiter, Michael; Vorholt, Julia A.

doi:10.1101/2022.12.16.520708

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…(LogPhase 600, Agilent). Growth rates were determined using the Dashing Growth Curves application 90 . Growth curves were smoothed with a rolling window of 30 and the exponential growth phase was determined manually.…”

Section: Isolation and Growth Characterization Of Mecoli_ref_2mentioning

confidence: 99%

A synthetic methylotrophic Escherichia coli as a chassis for bioproduction from methanol

Reiter,

Bradley,

Büchel

et al. 2024

Nat Catal

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Methanol synthesized from captured greenhouse gases is an emerging renewable feedstock with great potential for bioproduction. Recent research has raised the prospect of methanol bioconversion to value-added products using synthetic methylotrophic Escherichia coli, as its metabolism can be rewired to enable growth solely on the reduced one-carbon compound. Here we describe the generation of an E. coli strain that grows on methanol at a doubling time of 4.3 h—comparable to many natural methylotrophs. To establish bioproduction from methanol using this synthetic chassis, we demonstrate biosynthesis from four metabolic nodes from which numerous bioproducts can be derived: lactic acid from pyruvate, polyhydroxybutyrate from acetyl coenzyme A, itaconic acid from the tricarboxylic acid cycle and p-aminobenzoic acid from the chorismate pathway. In a step towards carbon-negative chemicals and valorizing greenhouse gases, our work brings synthetic methylotrophy in E. coli within reach of industrial applications.

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Section: Isolation and Growth Characterization Of Mecoli_ref_2mentioning

confidence: 99%

A synthetic methylotrophic Escherichia coli as a chassis for bioproduction from methanol

Reiter,

Bradley,

Büchel

et al. 2024

Nat Catal

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“…The interactive web application Dashing Growth Curves was utilized for a preliminary comparison of the growth parameters of the Modified Gompertz and Modified Logistic models to our model parameters 31 . The product of r or maximum growth rate and Y max was calculated for the three models.…”

Section: Analysis Of Model Parametersmentioning

confidence: 99%

Modelling complex growth profiles ofBacteroides fragilisandEscherichia colion various carbohydrates in an anaerobic environment

McGuire

Revuru

Zhang

et al. 2023

Preprint

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Previously published models for microbial growth focus only on either death or growth and are unable to account for differently shaped growth curves. Currently, there is no model capable of incorporating combinations of microbial growth trends. This study creates a bacterial growth model that incorporates growth, death, lag, and tail phases as well as applies this model to the growth trends of Bacteroides fragilis and Escherichia coli on 13 different carbohydrate substances (on more than 3500 growth curves, each with over 100 data points). Growth trends were collected by measuring the optical densities over 72 hours for either B. fragilis or E. coli in a chemically defined media supplemented by a mono- or disaccharide. The Digital Environment to Enable Data-driven Science (DEEDS) platform was utilized to parse data and apply the developed model to obtain parameter values. E. coli was found to grow on the chemically defined media alone while B. fragilis was unable to grow on it alone. E. coli growth was led by 10 mM concentration of substrates while B. fragilis growth was substrate dependent. Bacterial death only occurred for B. fragilis but was found to be dependent on concentration for the two most significant substrates. A singular model was developed that does not require prior knowledge of metabolomics and is capable of incorporating a combination of growth and death trends.

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“…Once data are reorganized, scientists face the challenge of converting raw data into quantitative microbial traits. Many existing computational tools use parametric analyses of microbial growth curve data (Table S1) [3,4,6,[8][9][10][11][12][13][14][15][16][17][18][19]. Parametric analyses fit a mathematical model of microbial population growth to observed data, then extract the fitted parameter values to quantify traits.…”

Section: Introductionmentioning

confidence: 99%

gcplyr: an R package for microbial growth curve data analysis

Blazanin

2023

Preprint

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Characterization of microbial growth is of both fundamental and applied interest. Modern platforms can automate collection of high-throughput microbial growth curves, necessitating the development of computational tools to handle and analyze these data to produce insights. However, existing tools are limited. Many use parametric analyses that require mathematical assumptions about the microbial growth characteristics. Those that use non-parametric or model-free analyses often can only quantify a few traits of interest, and none are capable of importing and reshaping all known growth curve data formats. To address this gap, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in every known format, and reshape it under a flexible and extendable framework so that users can design custom analyses or plot data with popular visualization packages. gcplyr can also incorporate metadata and generate or import experimental designs to merge with data. Finally, gcplyr carries out model-free and non-parametric analyses, extracting a broad range of clinically and ecologically important traits, including initial density, lag time, growth rate and doubling time, carrying capacity, diauxie, area under the curve, extinction time, and more. In sum, gcplyr makes scripted analysis of growth curve data in R straightforward, streamlines common data wrangling and analysis steps, and easily integrates with common visualization and statistical analyses.

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Dashing Growth Curves – a web application for rapid and interactive analysis of microbial growth curves

Cited by 3 publications

References 13 publications

A synthetic methylotrophic Escherichia coli as a chassis for bioproduction from methanol

A synthetic methylotrophic Escherichia coli as a chassis for bioproduction from methanol

Modelling complex growth profiles ofBacteroides fragilisandEscherichia colion various carbohydrates in an anaerobic environment

gcplyr: an R package for microbial growth curve data analysis

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