Fundamental trade-off between speed of switching and robustness of genetic switches limits dynamic control of metabolism
Bibliographic record
Abstract
1 Abstract Bi-stable gene regulatory motifs are found in a wide variety of natural gene regulatory networks and effect transcriptional switching between stable phenotypic states in cells. In synthetic gene regulatory circuits, these architectures can be leveraged to dynamically switch between distinct metabolic states for metabolic engineering and therapeutic applications. However, the lack of modularity and predictability of these motifs in varying environments has limited widespread application, especially since the factors that affect switching characteristics are still unclear. In this work, we use a mathematical model along with a newly developed dynamical modeling and continuity analysis framework to analyze the dynamics and robustness of bi-stable switches over a range of biologically relevant model parameter values. We identify a hitherto undiscovered fundamental trade-off between the robustness of the motif - the parameter ranges over which it retains bi-stable function, and the speed at which it effects a phenotypic change. Further, using E. coli as a model host, we constructed a large library (100) of transcriptional switches that show a wide range of switching speeds, to experimentally demonstrate the presence of this trade-off. The presence of this trade-off has significant implications on the design of transcriptional switches for diverse applications and explains the circuit architecture of naturally occurring transcriptional switches as well. Additionally, we anticipate that our diverse library of experimentally validated robust bi-stable switches will be valuable to effect phenotypic changes with differing switching speed requirements for metabolic engineering applications. 2 Significance statement The ability to switch genes on and off in response to spatio-temporal stimuli is critical to the survival of all organisms. At the cellular level, such switching is effected by regulatory motifs such as the bi-stable genetic toggle switch. Bi-stable motifs also serve as a primitive mode of cellular memory - “remembering” the last environment to which the cells have been exposed. In this work, we uncover a fundamental trade-off between the robust functioning of these switches and the speed at which they can effect a change in the gene expression landscape. These findings have a broad impact on the design and use of such synthetic gene regulatory devices across several fields such as industrial biotechnology, healthcare, etc.
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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".