Comparative analysis of gene expression on mRNA and protein level during development of <b><i>Streptomyces</i></b> cultures by using singular value decomposition
Bibliographic record
Abstract
This paper describes a comparative systems level analysis of the developmental proteome and transcriptome in the model antibiotic-producing eubacterium Streptomyces coelicolor, cultured on different media. The analysis formulates expression as the superposition of effects of regulatory networks and biological processes which can be identified using singular value decomposition (SVD) of a data matrix formed by time series measurements of expression of individual genes throughout the cell cycle of the bacterium. SVD produces linearly orthogonal factors, each of which can represent an independent system behavior defined by a linear combination of the genes/proteins highly correlated with the corresponding factor. By using SVD of the developmental time series of gene expression, as measured by both protein and RNA levels, we show that on the highest level of control (representing the basic kinetic behavior of the population), the results are identical, regardless of the type of experiment or cultivation method. The results show that this approach is capable of identifying basic regulatory processes independent of the environment in which the organism lives. It also shows that these processes are manifested equally on protein and RNA levels. Biological interpretation of the correlation of the genes and proteins with significant eigenprofiles (representing the highest level kinetic behavior of protein and/or RNA synthesis) revealed their association with metabolic processes, stress responses, starvation, and secondary metabolite production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".