Artificial neural network‐genetic algorithm (ANN‐GA) based medium optimization for the production of human interferon gamma (hIFN‐γ) in <i>Kluyveromyces lactis</i> cell factory
Bibliographic record
Abstract
Abstract In the current investigation, we have adapted response surface methodology (RSM) and artificial neural network‐genetic algorithm (ANN‐GA) based optimization to develop a defined medium for maximizing human interferon gamma production from recombinant Kluyveromyces lactis (K. lactis). In the initial screening studies, sorbitol and glycine emerged as a carbon and nitrogen source respectively having higher influence on hIFN‐γ production. Substrate inhibition studies were performed by varying the initial substrate concentration, and we found maximum hIFN‐γ concentration at 50 g L−1 of sorbitol. Inhibition kinetics studies were carried out using 3 and 4‐parametric models. Among the estimated models, the Moser model was observed as the best fitted model followed by the Luong model with R2 values of 0.882 and 0.75, respectively. The model acceptability test was carried out using the extra sum of squares F‐test and Akaike information criterion (AIC). The Plackett‐Burman multifactorial design identified sorbitol, glycine, Na2HPO4, and MgSO4.7H2O as the parameters significantly influencing the hIFN‐γ production. Further, the Box‐Behnken design (BBD) followed by the artificial neural network coupled with genetic algorithm (ANN‐GA) was employed for the precise optimization of medium components. With ANN‐GA a maximum hIFN‐γ yield of 2.1 ±0.3 mg L−1 in shake flask level and 3.5 ±0.1 mg L−1 in reactor level was achieved. The findings of this study serve as a model for a process development strategy (bench scale to reactor scale) to achieve a high productivity of the desired protein from a microbial cell factory.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".