An automated high-throughput lighting system for screening photosynthetic microorganisms in plate-based formats
Bibliographic record
Abstract
The capacity of photosynthetic microorganisms to fix carbon dioxide into biomass positions them as promising cell factories for sustainable biomanufacturing. However, limitations in screening throughput hinder the identification of enzymes, strains, and growth conditions needed to realize this potential. Here we present a microplate-based high-throughput cultivation system that can be integrated into existing automation infrastructure and supports growth of both prokaryotic and eukaryotic photosynthetic microorganisms. We validate this system by optimizing BG-11 medium compositions for Synechococcus elongatus UTEX 2973, Chlamydomonas reinhardtii UTEX 90 and Nostoc hatei CUBC1040, resulting in growth rates increases of 38.4% to 61.6%. We also identify small molecules that influence growth rates in Synechococcus elongatus UTEX 2973, including candidate compounds for growth rate increase and dozens that prevent growth. The sensitivity, throughput, and extensibility of this system support screening, strain isolation, and growth optimization needed for the development of photosynthetic microbial cell factories. A high-throughput cultivation system enables users to leverage existing infrastructure and expands the range of applications in functional screening, strain isolation, and growth optimization for the development of photosynthetic microbial cell factories.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".