Bibliographic record
Abstract
Living cells integrate information from their external environments and demonstrate a wide range of sophisticated behaviors. Most of the rapid responses exhibited by cells are mediated by circuits composed of interconnected signal transduction proteins. What mechanisms allow these proteins and circuits to respond precisely in space and time? Moreover, how do signaling networks evolve, producing new relationships between signals and responses? We chose to address these questions using a synthetic biology approach by engineering signaling proteins with novel input-output relationships.Many signaling proteins are composed of both catalytic domains and interaction domains, which are physically and functionally modular: the domains can be separated and function in different contexts. This modular structure has led to the hypothesis that new input-output relationships could be generated by recombining catalytic domains with alternative interaction domains. We first tested this hypothesis by engineering variants of the actin regulatory protein N-WASP (neuronal Wiskott-Aldrich syndrome proteins). These variants demonstrated a diverse array of gating behaviors in response to non-physiological inputs.We then tested this approach in a cellular context by engineering synthetic Dbl family guanine nucleotide exchange factors (GEFs), which activate Rho family GTPases, the master regulators of the actin cytoskeleton. Microinjection of these GEFs linked specific morphological responses to normally unrelated signaling pathways. In addition, two synthetic GEFs could be linked in series to form a linear cascade, which demonstrated amplification and increased ultrasensitivity when compared to the direct single-GEF circuits.These results demonstrate the evolutionary plasticity of modular signaling proteins, and suggest that it may be possible to manipulate cellular responses by engineering synthetic signaling networks. This ability will be critical for engineering cells with diverse therapeutic and biotechnological applications. \n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".