Evidence map›Paper›PMID 26172732›Full record

ArticlePhysical review. E, Statistical, nonlinear, and soft matter physics2015

Scaling and optimal synergy: Two principles determining microbial growth in complex media.

Francesco Alessandro Massucci, Roger Guimerà, Luís A Nunes Amaral, Marta Sales-Pardo

Abstract read
In one paragraph

Article in Physical review. E, Statistical, nonlinear, and soft matter physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Francesco Alessandro MassucciDepartament d'Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain.
Roger GuimeràDepartament d'Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain.
Luís A Nunes AmaralDepartment of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA.
Marta Sales-PardoDepartament d'Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Spain.

Funding

Developing Capacity to Evaluate Training Programs via Development of Human, Institutional and Social CapitalT32GM008449 · NIGMS · NORTHWESTERN UNIVERSITY · PI LEONARD, JOSHUA NATHANIEL · 1993 to 2023
$7.8M
Howard Hughes Medical InstituteNIGMS NIH HHS T32 GM008449
6 · The paper itself

Abstract

High-throughput experimental techniques and bioinformatics tools make it possible to obtain reconstructions of the metabolism of microbial species. Combined with mathematical frameworks such as flux balance analysis, which assumes that nutrients are used so as to maximize growth, these reconstructions enable us to predict microbial growth. Although such predictions are generally accurate, these approaches do not give insights on how different nutrients are used to produce growth, and thus are difficult to generalize to new media or to different organisms. Here, we propose a systems-level phenomenological model of metabolism inspired by the virial expansion. Our model predicts biomass production given the nutrient uptakes and a reduced set of parameters, which can be easily determined experimentally. To validate our model, we test it against in silico simulations and experimental measurements of growth, and find good agreement. From a biological point of view, our model uncovers the impact that individual nutrients and the synergistic interaction between nutrient pairs have on growth, and suggests that we can understand the growth maximization principle as the optimization of nutrient synergies.

Indexed as

GrowthMicrobiological PhenomenaModels, BiologicalBiomassComputer Simulation

Identifiers

PMID26172732
PMCPMC6653625

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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.