Evidence map›Paper›PMID 20842363›Full record

ReviewSeminars in immunopathology2010

Integrative systems biology and networks in autophagy.

Aylwin C Y Ng

Abstract readReview
PubMed Publisher
In one paragraph

Review in Seminars in immunopathology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. 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.

Aylwin C Y NgCenter for Computational and Integrative Biology and Gastrointestinal Unit, Massachusetts General Hospital, Harvard Medical School, Simches Research Center 7th Floor, 185 Cambridge Street, Boston, MA 02114, USA. ang@ccib.mgh.harvard.edu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing recognition that autophagy has important roles in many biological pathways, physiological systems, and infection and disease states necessitates a multidimensional perspective and systems-wide understanding of how autophagy is triggered or modulated by diverse stimuli. To delineate the nonlinearity and combinatorial complexity of biological networks and signaling pathways impinging on autophagy requires an integrative framework that brings together diverse information from genome-scale data (genomics, transcriptomics, proteomics, interactomics, functional RNAi screens) to dynamic time-series analyses and biochemical assays across a variety of biological and clinical contexts. We outline recent applications of genome-wide approaches to studying autophagy and highlight how some of these could be integrated to derive sub-networks that are more functionally focused. Viewed from a network perspective, the extensive interconnectivity between pathway systems converging on autophagy provides the essential foundation from which to systematically elucidate the regulatory nuances and crosstalks that orchestrate autophagic processes in different pathophysiological contexts.

Indexed as

Models, ImmunologicalAnimalsAutophagyGenome, HumanGenome-Wide Association StudyHumansSignal TransductionSystems Biology

Identifiers

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.