The Mini Colon Model: a benchtop multi-bioreactor system to investigate the gut microbiome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
fermentation systems allow for the investigation of gut microbial communities with precise control of various physiological parameters while decoupling confounding factors from the human host. Current systems, such as the SHIME and Robogut, are large in footprint, lack multiplexing, and have low experimental throughput. Alternatives which address these shortcomings, such as the Mini Bioreactor Array system, are often reliant on expensive specialized equipment, which hinders wide replication across labs. Here, we present the Mini Colon Model (MiCoMo), a low-cost, benchtop multi-bioreactor system that simulates the human colon environment with physiologically relevant conditions. The device consists of triplicate bioreactors working independently of an anaerobic chamber and equipped with automated pH, temperature, and fluidic control. We conducted 14-d experiments and found that MiCoMo was able to support a stable complex microbiota community with a Shannon Index of 3.17 ± 0.65, from individual fecal samples after only 3-5 d of inoculation. MiCoMo also retained inter-sample microbial differences by developing closely related communities unique to each donor, while maintaining both minimal variations between replicate reactors (average Bray-Curtis similarity 0.72 ± 0.13) andday-to-day variations (average Bray-Curtis similarity 0.81±0.10) after this short stabilization period. Together, these results establish MiCoMo as an accessible system for studying gut microbial communities with high throughput and multiplexing capabilities.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it