Evidence map›Paper›PMID 26430156›Full record

ArticleGenome research2015

Half dozen of one, six billion of the other: What can small- and large-scale molecular systems biology learn from one another?

Ian A Mellis, Arjun Raj

Abstract read
In one paragraph

Article in Genome research, 2015. 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. Article
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  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.

Ian A MellisPerelman School of Medicine, Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6021, USA;
Arjun RajDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6321, USA.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007170 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI BRASS, LAWRENCE F · 1985 to 2022
$54.0M
A Comprehensive Spatial Picture of Transcription in the NucleusDP2OD008514 · OD · UNIVERSITY OF PENNSYLVANIA · PI RAJ, ARJUN · 2011 to 2011
$2.4M
Validation and development of single nucleotide variant RNA FISH in single cells in culture and tissueR33EB019767 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI RAJ, ARJUN · 2014 to 2016
$1.1M
NIBIB NIH HHS R33 EB019767NIGMS NIH HHS MSTP T32 GM-07170NIGMS NIH HHS T32 GM007170NIH HHS 1DP2OD008514NIH HHS DP2 OD008514
6 · The paper itself

Abstract

Small-scale molecular systems biology, by which we mean the understanding of a how a few parts work together to control a particular biological process, is predicated on the assumption that cellular regulation is arranged in a circuit-like structure. Results from the omics revolution have upset this vision to varying degrees by revealing a high degree of interconnectivity, making it difficult to develop a simple, circuit-like understanding of regulatory processes. We here outline the limitations of the small-scale systems biology approach with examples from research into genetic algorithms, genetics, transcriptional network analysis, and genomics. We also discuss the difficulties associated with deriving true understanding from the analysis of large data sets and propose that the development of new, intelligent, computational tools may point to a way forward. Throughout, we intentionally oversimplify and talk about things in which we have little expertise, and it is likely that many of our arguments are wrong on one level or another. We do believe, however, that developing a true understanding via molecular systems biology will require a fundamental rethinking of our approach, and our goal is to provoke thought along these lines.

Indexed as

Molecular BiologySystems BiologyAlgorithmsComputational BiologyGenetic ResearchGenomicsHumansModels, Biological

Identifiers

PMID26430156
PMCPMC4579331

What OpenQuestion holds

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LicenceCC BY-NC
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.