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Record W4406701839 · doi:10.1186/s12934-025-02643-x

Physiology-informed use of Cupriavidus necator in biomanufacturing: a review of advances and challenges

2025· review· en· W4406701839 on OpenAlexafffund
Christian Euler

Bibliographic record

VenueMicrobial Cell Factories · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsBiomanufacturingCupriavidus necatorBiochemical engineeringBiotechnologyBioplasticSynthetic biologyAutotrophBiologyComputational biologyEngineeringEcologyBacteria

Abstract

fetched live from OpenAlex

Biomanufacturing offers a potentially sustainable alternative to deriving chemicals from fossil fuels. However, traditional biomanufacturing, which uses sugars as feedstocks, competes with food production and yields unfavourable land use changes, so more sustainable options are necessary. Cupriavidus necator is a chemolithoautotrophic bacterium capable of consuming carbon dioxide and hydrogen as sole carbon and energy sources, or formate as the source of both. This autotrophic metabolism potentially makes chemical production using C. necator sustainable and attractive for biomanufacturing. Additionally, C. necator natively fixes carbon in the form of poly-3-hydroxybutyrate, which can be processed to make biodegradable plastic. Recent progress in development of modelling and synthetic biology tools have made C. necator much more usable as a biomanufacturing chassis. However, these tools and applications are often limited by a lack of consideration for the unique physiology and metabolic features of C. necator. As such, further work is required to better understand the intricate mechanisms that allow it to prioritise generalization over specialization. In this review, progress toward physiology-informed engineering of C. necator across several dimensions is critically discussed, and recommendations for moving toward a physiological approach are presented. Arguments for metabolic specialization, more focus on autotrophic fermentation, C. necator-specific synthetic biology tools, and modelling that goes beyond constraints are presented based on analysis of existing literature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.281
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2025
Admission routes2
Has abstractyes

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