Synthetic memory circuits for tracking human cell fate
Bibliographic record
Abstract
A variety of biological phenomena, from disease progression to stem cell differentiation, are typified by a prolonged cellular response to a transient environmental cue. While biologically relevant, heterogeneity in these long-term responses is difficult to assess at the population level, necessitating the development of biological tools to track cell fate within subpopulations. Here we present a novel synthetic biology approach for identifying and tracking mammalian cell subpopulations. We constructed three genomically integrated circuits that use bistable autoregulatory transcriptional feedback to retain memory of exposure to brief stimuli. These "memory devices" are used to isolate and track the progeny of cells that responded differentially to doxycycline, hypoxia, or DNA-damaging agents. Following hypoxic or ultraviolet radiation exposure, strongly responding cells activate the memory device and exhibit changes in gene expression, growth rates, and viability for multiple generations after the initial stimulus. Taken together, these results indicate that a heritable memory of hypoxia and DNA damage exists in subpopulations that differ in long-term cell behavior.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".