Evidence map›Paper›PMID 25170839›Full record

ArticlePloS one2014

Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses.

Debasish Mondal, Edward Dougherty, Abhishek Mukhopadhyay, Adria Carbo, Guang Yao, Jianhua Xing

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Identifying robust hysteresis in networks.PLoS computational biology · 2018
    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

6 authors.

Debasish MondalDepartment of Biological Sciences, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
Edward DoughertyDepartment of Genetics, Bioinformatics and Computational Biology Ph. D program, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
Abhishek MukhopadhyayDepartment of Physics, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America; Department of Computer Science, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
Adria CarboDepartment of Genetics, Bioinformatics and Computational Biology Ph. D program, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America; Nutritional Immunology and Molecular Medicine Laboratory and Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
Guang YaoDepartment of Molecular & Cellular Biology, University of Arizona, Tucson, Arizona, United States of America.
Jianhua XingDepartment of Biological Sciences, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America; Department of Physics, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America; Beijing Computational Science Research Center, Beijing, China.

Funding

Mechanisms of Immune Modulation by Abscisic AcidR01AT004308 · NCCIH · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI BASSAGANYA-RIERA, JOSEP · 2007 to 2010
$1.2M
NCCIH NIH HHS 5R01AT004308NCCIH NIH HHS R01 AT004308NIAID NIH HHS HHSN272201000056CPHS HHS HHSN272201000056
6 · The paper itself

Abstract

A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.

Indexed as

Gene Regulatory NetworksAlgorithmsAnimalsBioengineeringCell ProliferationComputational BiologyE2F Transcription FactorsHumansModels, BiologicalModels, StatisticalReproducibility of ResultsRetinoblastoma ProteinSignal TransductionSystems BiologyE2F Transcription FactorsRetinoblastoma Protein

Identifiers

PMID25170839
PMCPMC4149494

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.