Multi-Objective Optimization of Biomass and Riboflavin Production Using Dynamic Flux Balance Analysis: A Study of Methylocystis Hirsuta
Bibliographic record
Abstract
In this study, we investigate the discrepancies between the dynamic flux balance analysis model and experimental data regarding the growth of the microorganism Methylocystis hirsuta.While the model accurately predicts substrate uptakes, it tends to overestimate biomass production, resulting in significant deviations from observed growth outcomes.This comparison highlights the metabolic potential of this microorganism to produce other metabolites, particularly riboflavin during its growth phase, which may explain these discrepancies.The dynamic flux balance analysis model depends on genome-scale metabolic models, highlighting the need for careful selection of objective functions for calculating reaction fluxes and defining metabolic pathways.By focusing on maximizing both biomass and riboflavin production, we advocate for a multi-objective optimization approach.To address this, we employed Pareto analysis to assess the trade-offs between these two objectives, providing valuable insights into the optimal conditions for enhancing both biomass yield and riboflavin synthesis.Our findings emphasize the importance of developing refined modelling techniques that closely align with experimental results, ultimately aiding in the metabolic engineering studies and the design of more effective bioprocesses for microbial production systems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".