Empirical models of the proliferative response of cytokine‐dependent hematopoietic cell lines
Bibliographic record
Abstract
There is an expanding need for predictive mathematical models to accelerate the optimization of cell therapy culture processes. Here we demonstrate the ability of simple mathematical models to describe quantitatively the cytokine growth-rate dependence of two human hematopoietic cell lines, TF-1 and MO7e. These cells are immortal but depend on either interleukin-3 (IL-3) or granulocyte-macrophage colony stimulating factor (GM-CSF) for their continued survival and maximal proliferation. They are also responsive to interleukin-6 (IL-6) and exhibit saturation kinetics when these cytokines are limiting. A Monod-type relationship consistently failed to fit measured cytokine dose-proliferation response curves while a Hill-type relationship showed a good fit. Cytokine interactions were first modeled by modifying the Hill-function to include an interaction parameter, gamma. This model did not indicate either synergistic or even additive effects between IL-3 and GM-CSF. Based on the reported competition between IL-3 and GM-CSF for their common receptor (beta(c)) subunit, a competitive model was also developed. This model had no new parameters beyond those obtained from single cytokine cultures and provided improved prediction of the growth rates for both cell lines exposed to combinations of IL-3 and GM-CSF over a wide range of concentrations. As expected, the competitive model failed to fit the data for IL-6 in combination with either IL-3 or GM-CSF, since IL-6 signaling does not involve the beta(c) chain of the IL-3/GM-CSF receptors. Interestingly, the cell-specific rates of GM-CSF uptake and cell proliferation were found to be uncoupled processes. Taken together, these results illustrate the utility of appropriately designed empirical models to describe the proliferative responses of hematopoietic cells to cytokine stimulation.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".