Evidence map›Paper›PMID 32374287›Full record

ArticleJournal of integrative bioinformatics2020

Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli.

Mee K Lee, Mohd Saberi Mohamad, Yee Wen Choon, Kauthar Mohd Daud, Nurul Athirah Nasarudin, Mohd Arfian Ismail, Zuwairie Ibrahim, Suhaimi Napis, Richard O Sinnott

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of integrative bioinformatics, 2020. 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
0.4field-weighted citation impact, top 41% 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, 7 citations in OpenAlex.

  1. 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

9 authors at 5 institutions in 2 countries.

Mee K LeeArtificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia.
Mohd Saberi MohamadInstitute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu Kelantan, Malaysia.
Yee Wen ChoonArtificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia.
Kauthar Mohd DaudArtificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia.
Nurul Athirah NasarudinArtificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia.
Mohd Arfian IsmailSoft Computing and Intelligent System Research Group, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300 Kuantan, Pahang, Malaysia.
Zuwairie IbrahimFaculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia.
Suhaimi NapisFaculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
Richard O SinnottSchool of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3052 Australia.
University of Technology Malaysia · MYUniversiti Malaysia Pahang Al-Sultan Abdullah · MYUniversiti Malaysia Kelantan · MYUniversiti Putra Malaysia · MYUniversity of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.

Indexed as

Escherichia coliModels, BiologicalAlgorithmsMetabolic EngineeringMetabolic Networks and PathwaysSuccinic AcidSuccinic AcidArtificial IntelligenceBioinformaticsMetabolic EngineeringMetaheuristic algorithmsMinimization of Metabolic Adjustment

Identifiers

PMID32374287
PMCPMC7734505
OpenAlexW3022000530

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.