Evidence map›Paper›PMID 27919524›Full record

ReviewCytokine2017

Demystifying the cytokine network: Mathematical models point the way.

Penelope A Morel, Robin E C Lee, James R Faeder

Abstract readReview
In one paragraph

Review in Cytokine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

25 citing papers in PubMed, 48 citations in OpenAlex.

  1. Article
  2. Review
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  9. Review
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  14. Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy.Frontiers in cell and developmental biology · 2021
    Review
  15. 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

3 authors at 1 institution in 1 country.

Penelope A MorelDepartment of Immunology, University of Pittsburgh, Pittsburgh, USA. Electronic address: morel@pitt.edu.
Robin E C LeeDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA.
James R FaederDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA.
University of Pittsburgh · US

Funding

Regulation of Cardiolin Byosynthesis in Epithelial InjuryP01HL114453 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MALLAMPALLI, RAMA K · 2014 to 2023
$21.3M
TrainingP41GM103712 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FAEDER, JAMES · 2012 to 2021
$16.3M
Equipment Supplement for 5R35GM119462: Deciphering dynamic signals in control of cell fate decisionsR35GM119462 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LEE, ROBIN E. C. · 2016 to 2025
$4.2M
WESTPA: A high-performance framework for simulating at the frontiers of biologyR01GM115805 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LILLIAN T CHONG · 2015 to 2026
$4.0M
Negative Control of IL-17R Signaling: Implications for Fungal ImmunityR01AI107825 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI GAFFEN, SARAH L · 2014 to 2018
$1.9M
NHLBI NIH HHS P01 HL114453NIAID NIH HHS R01 AI107825NIGMS NIH HHS P41 GM103712NIGMS NIH HHS R01 GM115805NIGMS NIH HHS R35 GM119462
6 · The paper itself

Abstract

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

Indexed as

Models, BiologicalAnimalsCytokinesHumansMiceCytokinesCD4 T cell differentiationCytokine networksMathematical modelingSingle-cell analysis

Identifiers

PMID27919524
PMCPMC5457394
OpenAlexW2557303151

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.