Unravelling the constrained cell growth in engineered living materials
Bibliographic record
Abstract
Abstract Engineered living materials (ELMs) leverage the integrative advantages of materials science and synthetic biology for advanced functionalities. Predicting and controlling cellular behavior is essential for designing and building ELMs, requiring fundamental understanding of the growth dynamics of encapsulated cells. Here, we interrogate the interference of constrained growth on the engineered functionalities and cellular physiology of cyanobacteria and unveil the dynamic interaction between cell growth and spatial confinements within photosynthetic ELMs. We observed that engineered cyanobacteria within ELMs exhibited compromised performances in growth, uptake of non-natural substrate, and synthesis of customized products, while ELMs could protect encapsulated cells from external stresses. Besides commonly accepted external influences, we identified abnormally high levels of reactive oxygen species and impaired oxygenic photosynthesis inside the cells encapsulated in ELMs. Finally, we illustrated the dynamics of cell growth within the confined spaces enveloped by the material matrices, forming clustered cell aggregates and compressed growth bubbles until the spatial limits. Our study provides a fundamental yet often overlooked connection between cellular behavior and spatial confinements, consolidating the foundation for advanced ELM innovations.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".