Evidence map›Paper›PMID 24441143›Full record

ReviewCurrent opinion in biotechnology2014

System level dynamics of post-translational modifications.

Aaron S Gajadhar, Forest M White

Abstract readReview
In one paragraph

Review in Current opinion in biotechnology, 2014. 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
–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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Novel Strategies to Address the Challenges in Top-Down Proteomics.Journal of the American Society for Mass Spectrometry · 2021
    Review
  4. Decoding the PTM-switchboard of Notch.Biochimica et biophysica acta. Molecular cell research · 2019
    Review
  5. Utilizing Ion Mobility to Identify Isobaric Post-Translational Modifications: Resolving Acrolein and Propionyl Lysine Adducts by TIMS Mass Spectrometry.International journal for ion mobility spectrometry : official publication of the International Society for Ion Mobility Spectrometry · 2018
    Article
  6. Article
  7. Review
  8. 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.

Aaron S GajadharDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Forest M WhiteDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Electronic address: fwhite@mit.edu.

Funding

Tumor Cell Networks CenterU54CA112967 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI LAUFFENBURGER, DOUGLAS A · 2004 to 2015
$26.2M
TOXICOLOGY CORE UNITP30ES002109 · NIEHS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI LEVINE, STUART S · 1985 to 2020
$25.6M
Vanderbilt Proteome Characterization CenterU24CA159988 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI LIEBLER, DANIEL C · 2011 to 2016
$13.9M
NCI NIH HHS U24 CA159988NCI NIH HHS U54 CA112967NIEHS NIH HHS P30 ES002109
6 · The paper itself

Abstract

Attempts to characterize cellular behaviors with static, univariate measurements cannot fully capture biological complexity and lead to an inadequate interpretation of cellular processes. Significant biological insight can be gleaned by considering the contribution of dynamic protein post-translational modifications (PTMs) utilizing systems-level quantitative analysis. High-resolution mass spectrometry coupled with computational modeling of dynamic signal-response relationships is a powerful tool to reveal PTM-mediated regulatory networks. Recent advances using this approach have defined network kinetics of growth factor signaling pathways, identified systems level responses to cytotoxic perturbations, elucidated kinase-substrate relationships, and unraveled the dynamics of PTM cross-talk. Innovations in multiplex measurement capacity, PTM annotation accuracy, and computational integration of datasets promise enhanced resolution of dynamic PTM networks and further insight into biological intricacies.

Indexed as

Protein Processing, Post-TranslationalAnimalsHumansMass SpectrometryPhosphorylationProteinsSignal TransductionProteins

Identifiers

PMID24441143
PMCPMC4097989

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.