Increasing evenness and stability in synthetic microbial consortia
Bibliographic record
Abstract
Abstract Construction of successful synthetic microbial consortia will harbour a new era in the field of agriculture, bioremediation, and human health. Engineering communities is a complex, multi-dimensional problem with several considerations ranging from the choice of consortia members and spatial factors to genetic circuit performances. There has been a growing number of computational strategies to aid in synthetic microbial consortia design, but a framework to optimize communities for two essential properties, evenness and stability, is missing. We investigated how the structure of different social interactions (cooperation, competition, and predation) in quorum-sensing based circuits impacts robustness of synthetic microbial communities and specifically affected evenness and stability. Our proposed work predicts engineering targets and computes their operating ranges to maximize the probability of synthetic microbial consortia to have high evenness and high stability. Our exhaustive pipeline for rapid and thorough analysis of large and complex parametric spaces further allowed us to dissect the relationship between evenness and stability for different social interactions. Our results showed that in cooperation, the speed at which species stabilizes is unrelated to evenness, however the region of stability increases with evenness. The opposite effect was noted for competition, where evenness and stable regions are negatively correlated. In both competition and predation, the system takes significantly longer to stabilize following a perturbation in uneven microbial conditions. We believe our study takes us one step closer to resolving the pivotal debate of evenness-stability relationship in ecology and has contributed to computational design of synthetic microbial communities by optimizing for previously unaddressed properties allowing for more accurate and streamlined ecological engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 | 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 teacher head, 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".