Evidence map›Paper›PMID 32183690›Full record

ArticleBMC bioinformatics2020

Revealing hidden information in osteoblast's mechanotransduction through analysis of time patterns of critical events.

Gianluca Ascolani, Timothy M Skerry, Damien Lacroix, Enrico Dall'Ara, Aban Shuaib

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.4field-weighted citation impact, top 43% of its field
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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Multiscale modeling in disease.Current opinion in systems biology · 2021
    Article
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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

5 authors at 2 institutions in 1 country.

Gianluca AscolaniDepartment of Oncology and Metabolism, University of Sheffield, Sheffield, UK.
Timothy M SkerryDepartment of Oncology and Metabolism, University of Sheffield, Sheffield, UK.
Damien LacroixInsigneo Institute of In Silico Medicine, University of Sheffield, Sheffield, UK.
Enrico Dall'AraDepartment of Oncology and Metabolism, University of Sheffield, Sheffield, UK.
Aban ShuaibDepartment of Oncology and Metabolism, University of Sheffield, Sheffield, UK. aban.shuaib@sheffield.ac.uk.ORCID http://orcid.org/0000-0002-6137-8205
Insigneo · GBUniversity of Sheffield · GB

Funding

Engineering and Physical Sciences Research Council (GB) EP/K03877X/1
6 · The paper itself

Abstract

backgroundMechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix by mechanical load and intracellular activity. By controlling the balance between the intracellular and extracellular domains, mechanotransduction determines the optimum functionality of skeletal dynamics. Failure of this relationship was suggested to contribute to bone-related diseases such as osteoporosis.

resultsA hybrid mechanical and agent-based model (Mech-ABM), simulating mechanotransduction in a single osteoblast under external mechanical perturbations, was utilised to simulate and examine modulation of the activation dynamics of molecules within mechanotransduction on the cellular response to mechanical stimulation. The number of molecules and their fluctuations have been analysed in terms of recurrences of critical events. A numerical approach has been developed to invert subordination processes and to extract the direction processes from the molecular signals in order to derive the distribution of recurring events. These predict that there are large fluctuations enclosing information hidden in the noise which is beyond the dynamic variations of molecular baselines. Moreover, studying the system under different mechanical load regimes and altered dynamics of feedback loops, illustrate that the waiting time distributions of each molecule are a signature of the system's state.

conclusionsThe behaviours of the molecular waiting times change with the changing of mechanical load regimes and altered dynamics of feedback loops, presenting the same variation of patterns for similar interacting molecules and identifying specific alterations for key molecules in mechanotransduction. This methodology could be used to provide a new tool to identify potent molecular candidates to modulate mechanotransduction, hence accelerate drug discovery towards therapeutic targets for bone mass upregulation.

Indexed as

Mechanotransduction, CellularExtracellular MatrixHumansOsteoblastsProteinsUp-RegulationProteinsDirecting processFluctuationsMolecular networkOsteoblast mechanotransductionSubordination theoryWaiting time distribution

Identifiers

PMID32183690
PMCPMC7079370
OpenAlexW3012424556

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.