Clonal evolution of stem and differentiated cells can be predicted by integrating cell‐intrinsic and ‐extrinsic parameters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Stem cells and their derivatives represent a renewable source of cells for therapeutic applications. However, the inability to quantitatively integrate and exploit the effects of multiple parameters on the fate of stem cells limits their use in clinical applications. To address this, we developed a computational model that combines probabilistic, individual-cell and deterministic cell-population parameters to simultaneously calculate the specific effects of exogenous and endogenous factors on the overall population-dynamics behaviour. The model tracks the progeny trajectory of individual cells over several generations as a threshold function of ligand-receptor signalling interactions. Simulations in silico were validated against an Oct 4-promoter-driven green-fluorescent-protein-expressing murine embryonic stem cell line, and used to understand the effects of key parameters on the clonal evolution of stem versus differentiated cells in this system. Our approach demonstrated the ability to distinguish between individual-cell and population-averaged parameters with respect to their effects on governing dynamic behaviour. Moreover, we could discriminate between digital versus graded regulation of the Oct 4 transcription factor in accounting for experimental observations. Finally, we showed that our approach could be generalized to other stem-cell systems, in particular the previously characterized intestinal crypt system, in elucidating relative contributions of stem and progenitor cells to population output. On the basis of all these results, we believe that our iterative experimental and computational approach has been found to be useful for the study of various stem-cell systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 it