Evidence map›Paper›PMID 42509354›Full record

ReviewAntonie van Leeuwenhoek2026

Metabolic engineering strategies: utilizing different microbial strains and advanced technologies for the synthesis of high-valued terpenoids.

Aakash Kamalesan, K K Kumar, Bharathi Nathan, Renukadevi Perumal, Senthil Natesan, Vellaikumar Sampathrajan

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In one paragraph

Review in Antonie van Leeuwenhoek, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Aakash KamalesanDepartment of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.
K K KumarDepartment of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.
Bharathi NathanDepartment of Plant Molecular Biology & Bioinformatics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.
Renukadevi PerumalDepartment of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.
Senthil NatesanDepartment of Plant Molecular Biology & Bioinformatics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India.
Vellaikumar SampathrajanDepartment of Plant Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, 641003, India. svk73@tnau.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Terpenoids represent the largest and most structurally diverse class of secondary metabolites, with extensive applications in the pharmaceutical, nutraceutical, cosmetic, agricultural, fragrance, and biofuel industries. The growing demand for these compounds has resulted in extensive exploitation of plant-derived terpenoids, raising concerns regarding resource availability and sustainability. Consequently, microbial production has emerged as a promising alternative because of the high genetic tractability, rapid growth, and ease of metabolic engineering offered by microbial hosts. Various metabolic engineering strategies, including heterologous gene insertion, targeted gene deletion, and redirection of carbon flux from primary metabolism toward terpenoid biosynthesis, have been employed to enhance terpenoid production. The selection of an appropriate microbial host is a critical determinant of production efficiency, as it influences metabolite yield, cultivation feasibility, genetic manipulability, scalability, environmental sustainability, and economic viability. Genetically engineered microorganisms have therefore become well-established platforms for the production of diverse classes of terpenoids. Although substantial progress has been made in reconstructing and expressing terpenoid biosynthetic pathways in microbial hosts, further strain optimization requires systematic integration of computational approaches. In this context, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for metabolic engineering by enabling pathway prediction, metabolic flux optimization, enzyme engineering, and identification of bottlenecks throughout terpenoid biosynthesis. Coupled with advances in genomics, systems biology, and synthetic biology, these technologies are accelerating the development of robust microbial cell factories for the sustainable, large-scale production of terpenoids through industrial bioprocesses.

Indexed as

BacteriaMetabolic EngineeringTerpenesArtificial IntelligenceBiosynthetic PathwaysMachine LearningTerpenesArtificial intelligenceBiosynthetic pathwayMicrobial strainsTerpenoids

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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.