Evidence map›Paper›PMID 39502364›Full record

ArticlebioRxiv : the preprint server for biology2024

Biophysical metabolic modeling of complex bacterial colony morphology.

Ilija Dukovski, Lauren Golden, Jing Zhang, Melisa Osborne, Daniel Segrè, Kirill S Korolev

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

6 authors.

Ilija DukovskiBioinformatics Program, Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.ORCID 0000-0002-5160-1944
Lauren GoldenBroad Institute, Cambridge, MA, USA.ORCID 0000-0003-4160-8488
Jing ZhangBioinformatics Program, Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.
Melisa OsborneBioinformatics Program, Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.ORCID 0000-0002-3945-7134
Daniel SegrèBioinformatics Program, Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.ORCID 0000-0003-4859-1914
Kirill S KorolevBioinformatics Program, Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.ORCID 0000-0003-1988-0645

Funding

Relationship between genealogies and biophysical processes during spatial growth.R01GM138530 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI KOROLEV, KIRILL SERGEEVICH · 2020 to 2024
$1.4M
Spatio-temporal mechanistic modeling of whole-cell tumor metabolismR21CA279630 · NCI · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI DUKOVSKI, ILIJA, KOROLEV, KIRILL SERGEEVICH · 2023 to 2024
$413k
NCI NIH HHS R21 CA279630NIGMS NIH HHS R01 GM138530
6 · The paper itself

Abstract

Microbial colony growth is shaped by the physics of biomass propagation and nutrient diffusion, and by the metabolic reactions that organisms activate as a function of the surrounding environment. While microbial colonies have been explored using minimal models of growth and motility, full integration of biomass propagation and metabolism is still lacking. Here, building upon our framework for Computation of Microbial Ecosystems in Time and Space (COMETS), we combine dynamic flux balance modeling of metabolism with collective biomass propagation and demographic fluctuations to provide nuanced simulations of

Indexed as

Bacterial coloniescoffee-ring effectcoffee-stain effectcolony morphologyCOMETSdynamic Flux Balance AnalysisFlux Balance Analysisgenetic demixinggenome-scale metabolic modelinggrowth instabilitymetabolic maintenancemetabolic ringmicrobial coloniessectorsstoichiometric modeling

Identifiers

PMID39502364
PMCPMC11537321

What OpenQuestion holds

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LicenceCC BY-ND
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Registered trials

None linked

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.