Evidence map›Paper›PMID 30791941›Full record

ArticleBMC systems biology2019

A mathematical model of iron import and trafficking in wild-type and Mrs3/4ΔΔ yeast cells.

Joshua D Wofford, Paul A Lindahl

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.4field-weighted citation impact, top 43% 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

8 citing papers in PubMed, 11 citations in OpenAlex.

  1. Understanding Iron and Oxidative Stress Response inbioRxiv : the preprint server for biology · 2026
    Article
  2. A kinetic mathematical model of comprehensive iron metabolism in a respiring yeast cell: a basic-pathways approach to solving a large system dynamically.Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine · 2026
    Article
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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

2 authors at 1 institution in 1 country.

Joshua D WoffordTexas A&M University, Department of Chemistry, College Station, TX, 77843-3255, USA.
Paul A LindahlTexas A&M University, Department of Chemistry, College Station, TX, 77843-3255, USA. Lindahl@chem.tamu.edu.ORCID 0000-0001-8307-9647
Texas A&M University · US

Funding

Iron Trafficking and Regulation in Biological SystemsR35GM127021 · NIGMS · TEXAS A&M UNIVERSITY · PI PAUL A. LINDAHL · 2018 to 2026
$3.4M
NIGMS NIH HHS R35 GM127021
6 · The paper itself

Abstract

backgroundIron plays crucial roles in the metabolism of eukaryotic cells. Much iron is trafficked into mitochondria where it is used for iron-sulfur cluster assembly and heme biosynthesis. A yeast strain in which Mrs3/4, the high-affinity iron importers on the mitochondrial inner membrane, are deleted exhibits a slow-growth phenotype when grown under iron-deficient conditions. However, these cells grow at WT rates under iron-sufficient conditions. The object of this study was to develop a mathematical model that could explain this recovery on the molecular level.

resultsA multi-tiered strategy was used to solve an ordinary-differential-equations-based mathematical model of iron import, trafficking, and regulation in growing Saccharomyces cerevisiae cells. At the simplest level of modeling, all iron in the cell was presumed to be a single species and the cell was considered to be a single homogeneous volume. Optimized parameters associated with the rate of iron import and the rate of dilution due to cell growth were determined. At the next level of complexity, the cell was divided into three regions, including cytosol, mitochondria, and vacuoles, each of which was presumed to contain a single form of iron. Optimized parameters associated with import into these regions were determined. At the final level of complexity, nine components were assumed within the same three cellular regions. Parameters obtained at simpler levels of complexity were used to help solve the more complex versions of the model; this was advantageous because the data used for solving the simpler model variants were more reliable and complete relative to those required for the more complex variants. The optimized full-complexity model simulated the observed phenotype of WT and Mrs3/4ΔΔ cells with acceptable fidelity, and the model exhibited some predictive power.

conclusionsThe developed model highlights the importance of an Fe

Indexed as

Models, BiologicalMutationBiological TransportCation Transport ProteinsIronKineticsMitochondriaMitochondrial ProteinsOxygenPhenotypeSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsCation Transport ProteinsIronMitochondrial ProteinsMRS3 protein, S cerevisiaeMRS4 protein, S cerevisiaeOxygenSaccharomyces cerevisiae ProteinsCytosolIron-sulfur clustersKineticsMitochondriaNanoparticlesOrdinary differential equationsOxygenVacuoles

Identifiers

PMID30791941
PMCPMC6385441
OpenAlexW2923938753

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.