Engineered Networks of Synthetic and Natural Proteins To Control Cell Migration
Bibliographic record
Abstract
Mammalian cells reprogrammed with engineered transgenes have the potential to be useful therapeutic platforms because they can support large genetic networks, can be taken from a host or patient, and perform useful functions such as migration and secretion. Successful engineering of mammalian cells will require the development of modules that can perform well-defined, reliable functions, such as directed cell migration toward a chemical or physical signal. One inherently modular cellular pathway is the Ca(2+) signaling pathway: protein modules that mobilize and respond to Ca(2+) are combined across cell types to create complexity. We have designed a chimera of Rac1, a GTPase that controls cell morphology and migration, and calmodulin (CaM), a Ca(2+)-responsive protein, to control cell migration. The Rac1-CaM chimera (named RACer) controlled lamellipodia growth in response to Ca(2+). RACer was combined with LOVS1K (a previously engineered light-sensitive Ca(2+)-mobilizing module) and cytokine receptors to create protein networks where blue light and growth factors regulated cell morphology and, thereby, cell migration. To show the generalizability of our design, we created a Cdc42-CaM chimera that controls filopodia growth in response to Ca(2+). The insights that have been gained into Ca(2+) signaling and cell migration will allow future work to combine engineered protein systems to enable reprogrammed cell sensing of relevant therapeutic targets in vivo.
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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 | 0.000 | 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 | 0.001 |
| 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".