Evidence map›Paper›PMID 26496494›Full record

ArticlePloS one2015

Complementing ODE-Based System Analysis Using Boolean Networks Derived from an Euler-Like Transformation.

Claudia Stötzel, Susanna Röblitz, Heike Siebert

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Boolean modelling in plant biology.Quantitative plant biology · 2022
    Review
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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.

Claudia StötzelMathematics for Life and Materials Sciences, Zuse Institute Berlin, Berlin, Germany.
Susanna RöblitzMathematics for Life and Materials Sciences, Zuse Institute Berlin, Berlin, Germany; Dep. of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.
Heike SiebertDep. of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs) into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations.

Indexed as

Models, StatisticalSystems AnalysisAnimalsCattleCorpus LuteumDinoprostoneEstrous CycleFemaleFollicle Stimulating HormoneGonadotropin-Releasing HormoneLuteinizing HormoneOvarian FollicleProstaglandins FDinoprostoneFollicle Stimulating HormoneGonadotropin-Releasing HormoneLuteinizing HormoneProstaglandins F

Identifiers

PMID26496494
PMCPMC4619740

What OpenQuestion holds

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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.