Engineering metabolic time-sharing in a clonal Escherichia coli population
Bibliographic record
Abstract
The “division of labour” strategy is common among microbial communities, as dividing burdensome tasks between members of a community alleviates the strain placed on individual cells. Exploiting this phenomenon in heterogeneous microbial co-cultures for industrial synthesis of valuable compounds is limited by inefficiencies in nutrient exchange and conflicting growth requirements. Here, we demonstrate a synthetic gene circuit which enables cells of an isogenic Escherichia coli population to carry out “metabolic time-sharing” by shifting between alternate metabolic states via temporal changes in gene expression. Further, we review techniques for monitoring such dynamic processes at the single-cell level, and discuss their current applications in bacterial studies. To validate that our circuit may be used to induce cooperative behaviours in microbial populations, we adapted this circuit to engineer cells that oscillate between distinct amino acid auxotrophy phenotypes, driven by the periodic silencing of key biosynthetic genes. Culturing a clonal time-sharing population with unsynchronized oscillators permits reciprocal amino acid cross-feeding, ultimately ensuring population viability. Through comparative growth experiments, we found that the fitness of our time-sharing population was comparable to that of a heterogeneous co-culture composed of E. coli auxotrophs similarly capable of cross-feeding amino acids. Although future studies would be needed to confirm this, our preliminary results suggest that metabolic time-sharing may be a viable alternative to synthetic heterogeneous co-cultures. As it may enable an entire complex biosynthetic pathway to be engineered into a single host with reduced metabolic burden, the metabolic time-sharing strategy demonstrated here could potentially be implemented for microbial bioproduction, among other widespread applications.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".