Optimal control for the metabolic activity of microbial cells through the metabolic flux parameterization
Bibliographic record
Abstract
Abstract During microbial fermentation, the metabolic fluxes reflect the growth and reproduction rate of microbial cells. By adjusting the metabolic fluxes, the desired production target can be achieved. It is highly necessary to ensure that the fermentation process meets the production constraints because the metabolic fluxes are regulated by various enzymatic reactions. However, previous studies cannot guarantee that the production constraints are satisfied over the course of the process. In this paper, constraints are designed at the microscopic flux level for the E. coli cell fermentation process. A control variable parameterization method, which discretizes the control variables while the state variables remain continuous, is used to optimize the E. coli cell growth activity, where the time domain is divided into several subintervals. In each subinterval, the metabolic fluxes are approximated by a series of parameters to be optimized. Then, the E. coli metabolic activity is transformed into a dynamic optimization problem, and the optimal trajectories of metabolic fluxes are obtained by solving the problem. Finally, the simulation results of the E. coli fermentation process verify the effectiveness of the method.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".