Evidence map›Paper›PMID 31748916›Full record

ReviewCellular and molecular life sciences : CMLS2020

Multi-tissue to whole plant metabolic modelling.

Rahul Shaw, C Y Maurice Cheung

Abstract readReview
In one paragraph

Review in Cellular and molecular life sciences : CMLS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Exploring synergies between plant metabolic modelling and machine learning.Computational and structural biotechnology journal · 2022
    Review
  11. Environment-coupled models of leaf metabolism.Biochemical Society transactions · 2021
    Review
  12. Systems biology: current status and challenges.Cellular and molecular life sciences : CMLS · 2020
    Review
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.

Rahul ShawDepartment of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, Singapore.
C Y Maurice CheungYale-NUS College, Singapore, Singapore. maurice.cheung@yale-nus.edu.sg.ORCID http://orcid.org/0000-0002-5531-7895

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-scale metabolic models have been successfully applied to study the metabolism of multiple plant species in the past decade. While most existing genome-scale modelling studies have focussed on studying the metabolic behaviour of individual plant metabolic systems, there is an increasing focus on combining models of multiple tissues or organs to produce multi-tissue models that allow the investigation of metabolic interactions between tissues and organs. Multi-tissue metabolic models were constructed for multiple plants including Arabidopsis, barley, soybean and Setaria. These models were applied to study various aspects of plant physiology including the division of labour between organs, source and sink tissue relationship, growth of different tissues and organs and charge and proton balancing. In this review, we outline the process of constructing multi-tissue genome-scale metabolic models, discuss the strengths and challenges in using multi-tissue models, review the current status of plant multi-tissue and whole plant metabolic models and explore the approaches for integrating genome-scale metabolic models into multi-scale plant models.

Indexed as

Genome, PlantHumansMetabolic Networks and PathwaysModels, BiologicalPlantsConstraint-based modelGenome-scale metabolic modelModel integrationMulti-tissue modelPlant metabolic modellingWhole plant model

Identifiers

PMID31748916
PMCPMC11104929

What OpenQuestion holds

Textmetadata
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.