Evidence map›Paper›PMID 39008245›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2024

Iron Homeostatic Regulation in Saccharomyces cerevisiae: Introduction to a Computational Modeling Method.

Paul A Lindahl

Abstract readReview
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

1 author.

Paul A LindahlDepartments of Chemistry and of Biochemistry and Biophysics, Texas A&M University, College Station, TX, USA. Lindahl@chem.tamu.edu.

Funding

Iron Trafficking and Regulation in Biological SystemsR35GM127021 · NIGMS · TEXAS A&M UNIVERSITY · PI PAUL A. LINDAHL · 2018 to 2026
$3.4M
National Science Foundation MCB-1817389NIGMS NIH HHS R35 GM127021
6 · The paper itself

Abstract

Over the past 30 years, much has been learned regarding iron homeostatic regulation in budding yeast, S. cerevisiae, including the identity of many of the proteins and molecular-level regulatory mechanisms involved. Most advances have involved inferring such mechanisms based on the analysis of iron-dysregulation phenotypes arising in various genetic mutant strains. Still lacking is a cellular- or system-level understanding of iron homeostasis. These experimental advances are summarized in this review, and a method for developing cellular-level regulatory mechanisms in yeast is presented. The method employs the results of Mössbauer spectroscopy of whole cells and organelles, iron quantification of the same, and ordinary differential equation-based mathematical models. Current models are simplistic when compared to the complexity of iron homeostasis in real cells, yet they hold promise as a useful, perhaps even required, complement to the popular genetics-based approach. The fundamental problem in comprehending cellular regulatory mechanisms is that, given the complexities involved, different molecular-level mechanisms can often give rise to virtually indistinguishable cellular phenotypes. Mathematical models cannot eliminate this problem, but they can minimize it.

Indexed as

HomeostasisIronSaccharomyces cerevisiaeComputer SimulationModels, BiologicalSaccharomyces cerevisiae ProteinsSpectroscopy, MossbauerIronSaccharomyces cerevisiae ProteinsBiochemical reaction networksCellular regulatory mechanismsCytosolHemesIron–sulfur clustersMitochondriaMössbauer spectroscopyOrdinary differential equationsSoft or continuous Heaviside logistical functionsVacuoles

Identifiers

PMID39008245
PMCPMC11514128

What OpenQuestion holds

Textmetadata
LicenceTDM
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.