Evidence map›Paper›PMID 38234412›Full record

ArticleSynthetic and systems biotechnology2024

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design.

Debiao Wu, Feng Xu, Yaying Xu, Mingzhi Huang, Zhimin Li, Ju Chu

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

Article in Synthetic and systems biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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 1 institution in 1 country.

Debiao WuState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
Feng XuState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
Yaying XuState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
Mingzhi HuangState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
Zhimin LiState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
Ju ChuState Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
East China University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in

Indexed as

Biosystems designFlux balance analysisMachine learningMetabolic modelingSaccharomyces cerevisiaeSuccinate dehydrogenase

Identifiers

PMID38234412
PMCPMC10793177
OpenAlexW4390414705

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.