Harnessing metabolites from plant cell tissue and organ culture for sustainable biotechnology
Bibliographic record
Abstract
Abstract The convergence of plant cell, tissue and organ culture (PCTOC) with metabolomics and computational tools represents a transformative platform for sustainable biotechnology. PCTOC enables the controlled, sterile, and scalable production of high-value secondary metabolites, independent of environmental and geographical constraints. However, the metabolic complexity of plant systems and the variability in culture conditions have historically posed challenges in optimizing metabolite yields. Metabolomics, by providing a comprehensive snapshot of small cellular-molecule composition, allows for the monitoring, analysis, and manipulation of biosynthetic pathways in vitro. By guiding experimental designs through response surface methodology and leveraging computational prediction via artificial intelligence, metabolomics enables data-driven optimization of culture parameters, enabling a shift from empirical to rational design strategies. This review presents a holistic framework for harnessing metabolites from PCTOC systems, highlighting the advances in bioreactor technologies, analytical platforms (LC-MS/MS, GC-MS/MS, NMR), and computational analytics that collectively enhance metabolite production. Here we critically examine case studies of commercially important phytochemicals produced via callus, suspension, adventitious root, and hairy root cultures, with emphasis on elicitation strategies, metabolic engineering, and flux analysis. Moreover, the application of PCTOC-metabolomics platforms extends beyond bioproduction to plant conservation and biodiversity, where chemotaxonomic profiling supports ex situ preservation of threatened species. Despite its promise, this integrated approach faces technical and translational challenges, including limited spectral libraries, scalability barriers, and regulatory constraints. Future directions emphasize the development of automated bioprocess systems, multi-omics integration, AI-guided synthetic biology, and sustainable biomanufacturing aligned with circular economy principles. Ultimately, the integration of PCTOC and metabolomics, powered by computational innovation, offers a resilient, reproducible, and eco-conscious strategy for plant-based bioproduction, conservation, and therapeutic discovery in the post-genomic era.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".