Evidence map›Paper›PMID 40510533›Full record

ReviewSynthetic and systems biotechnology2025

From reactants to products: computational methods for biosynthetic pathway design.

Shaozhen Ding, Dongliang Liu, Yu Tian, Dachuan Zhang, HuaDong Xing, Junni Chen, Zhiguo Liu, Qian-Nan Hu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Synthetic and systems biotechnology, 2025. 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
–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

1 citing paper in PubMed.

  1. EngineeringFrontiers in microbiology · 2026
    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

8 authors.

Shaozhen DingDepartment of Chemical and Biomolecular Engineering, School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430023, China.
Dongliang LiuDepartment of Chemical and Biomolecular Engineering, School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430023, China.
Yu TianDepartment of Chemical and Biomolecular Engineering, School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430023, China.
Dachuan ZhangDepartment of Food Science and Technology, National University of Singapore, 117542, Singapore.
HuaDong XingEmpa - Swiss Federal Laboratories for Materials Science and Technology, Technology and Society Laboratory, 9014, St. Gallen, Switzerland.
Junni ChenWuhan LifeSynther Science and Technology Co. Limited, Wuhan, 430070, China.
Zhiguo LiuDepartment of Chemical and Biomolecular Engineering, School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430023, China.
Qian-Nan HuSchool of Artificial Intelligence, Wuhan University, Wuhan, 430072, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the main goals in synthetic biology is to produce value-added compounds from available precursors using enzymatic approaches. The construction of biosynthetic pathways for synthesizing target molecules plays a crucial role in this process. However, it is challenging and time-consuming for researchers to design efficient pathways manually. In recent decades, pathway design has advanced through data- and algorithm-driven approaches. In this article, we review key computational tools involved in biosynthetic pathway design, covering: 1) Biological Big-Data including compounds, reactions/pathways and enzymes. 2) Retrosynthesis methods leveraging multi-dimensional biosynthesis data to predict potential pathways for target compounds synthesis. 3) Enzyme engineering relying on data mining to identify/de novo design enzymes with desired functions. Integrating these three key components can significantly enhance the efficiency and accuracy of biosynthetic pathway design in synthetic biology.

Indexed as

Biological big-dataEnzyme engineeringMethods of retrosynthetic analysis

Identifiers

PMID40510533
PMCPMC12159830

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.