Evidence map›Paper›PMID 25874228›Full record

ArticleBioMed research international2015

Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework.

Gael Pérez-Rodríguez, Martín Pérez-Pérez, Daniel Glez-Peña, Florentino Fdez-Riverola, Nuno F Azevedo, Anália Lourenço

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2015. 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
0.3field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Rules of Engagement: A Guide to Developing Agent-Based Models.Methods in molecular biology (Clifton, N.J.) · 2022
    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 at 2 institutions in 2 countries.

Gael Pérez-RodríguezEscuela Superior de Ingeniería Informática (ESEI), Edificio Politécnico, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain.
Martín Pérez-PérezEscuela Superior de Ingeniería Informática (ESEI), Edificio Politécnico, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain.
Daniel Glez-PeñaEscuela Superior de Ingeniería Informática (ESEI), Edificio Politécnico, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain.
Florentino Fdez-RiverolaEscuela Superior de Ingeniería Informática (ESEI), Edificio Politécnico, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain.ORCID 0000-0002-3943-8013
Nuno F AzevedoLEPABE, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rúa Dr. Roberto Frias, 4200-465 Porto, Portugal.
Anália LourençoEscuela Superior de Ingeniería Informática (ESEI), Edificio Politécnico, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain ; Centre of Biological Engineering (CEB), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
Universidade de Vigo · ESUniversidade do Porto · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Agent-based modelling is being used to represent biological systems with increasing frequency and success. This paper presents the implementation of a new tool for biomolecular reaction modelling in the open source Multiagent Simulator of Neighborhoods framework. The rationale behind this new tool is the necessity to describe interactions at the molecular level to be able to grasp emergent and meaningful biological behaviour. We are particularly interested in characterising and quantifying the various effects that facilitate biocatalysis. Enzymes may display high specificity for their substrates and this information is crucial to the engineering and optimisation of bioprocesses. Simulation results demonstrate that molecule distributions, reaction rate parameters, and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of realistic cell environments. While higher percentage of collisions with occurrence of reaction increases the affinity of the enzyme to the substrate, a faster reaction (i.e., turnover number) leads to a smaller number of time steps. Slower diffusion rates and molecular crowding (physical hurdles) decrease the collision rate of reactants, hence reducing the reaction rate, as expected. Also, the random distribution of molecules affects the results significantly.

Indexed as

Models, Biological

Identifiers

PMID25874228
PMCPMC4385633
OpenAlexW2033663126

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.