Evidence map›Paper›PMID 30241537›Full record

ArticleBMC systems biology2018

An ensemble of mathematical models showing diauxic growth behaviour.

Andreas Kremling, Johannes Geiselmann, Delphine Ropers, Hidde de Jong

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.1field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 28 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Andreas KremlingSystems Biotechnology, Technical University of Munich, Boltzmannstrasse 15, Garching b. München, 85748, Germany. a.kremling@tum.de.ORCID 0000-0001-9533-6786
Johannes GeiselmannLaboratoire Interdisciplinaire de Physique, Université Grenoble Alpes, 140 avenue de la Physique, Saint Martin d'Hères, 38402, France.
Delphine RopersInria, Université Grenoble Alpes, 655 avenue de l'Europe, Saint Ismier Cedex, 38334, France.
Hidde de JongInria, Université Grenoble Alpes, 655 avenue de l'Europe, Saint Ismier Cedex, 38334, France.
Centre Inria de l'Université Grenoble Alpes · FRLaboratoire Interdisciplinaire de Physique · FRTechnical University of Munich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCarbon catabolite repression (CCR) controls the order in which different carbon sources are metabolised. Although this system is one of the paradigms of regulation in bacteria, the underlying mechanisms remain controversial. CCR involves the coordination of different subsystems of the cell - responsible for the uptake of carbon sources, their breakdown for the production of energy and precursors, and the conversion of the latter to biomass. The complexity of this integrated system, with regulatory mechanisms cutting across metabolism, gene expression, and signalling, has motivated important modelling efforts over the past four decades, especially in the enterobacterium Escherichia coli.

resultsStarting from a simple core model with only four intracellular metabolites, we develop an ensemble of model variants, all showing diauxic growth behaviour during a batch process. The model variants fall into one of the four categories: flux balance models, kinetic models with growth dilution, kinetic models with regulation, and resource allocation models. The model variants differ from one another in only a single aspect, each breaking the symmetry between the two substrate assimilation pathways in a different manner, and can be quantitatively compared using a so-called diauxic growth index. For each of the model variants, we predict the behaviour in two new experimental conditions, namely a glucose pulse for a culture growing in minimal medium with lactose and a batch culture with different initial concentrations of the components of the transport systems. When qualitatively comparing these predictions with experimental data for these two conditions, a number of models can be excluded while other model variants are still not discriminable. The best-performing model variants are based on inducer inclusion and activation of enzymatic genes by a global transcription factor, but the other proposed factors may complement these well-known regulatory mechanisms.

conclusionsThe model ensemble presented here offers a better understanding of the variety of mechanisms that have been proposed to play a role in CCR. In addition, it provides an educational resource for systems biology, as it gives an introduction to a broad range of modeling approaches in the context of a simple but biologically relevant example.

Indexed as

Models, BiologicalBacteriaCarbonCell ProliferationIntracellular SpaceKineticsMetabolic Flux AnalysisCarbonCarbon catabolite repressionDynamical modelingEscherichia coliFlux balance modelsKinetic modelsResource allocation models

Identifiers

PMID30241537
PMCPMC6151013
OpenAlexW2889868111

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.