Dynamic Co-Clustering and Self-Sorting in Interactive Protocell Populations
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION(2025)
Univ Bristol
Abstract
The design and implementation of collective actions in model protocell communities is an on-going challenge in synthetic protobiology. Herein, we covalently graft alginate or chitosan onto the outer surface of semipermeable enzyme-containing silica colloidosomes to produce hairy catalytic protocells with pH-switchable membrane surface charge. Binary populations of the enzymatically active protocells exhibit self-initiated stimulus-responsive changes in spatial organization such that the mixed community undergoes alternative modes of electrostatically induced self-sorting and reversible co-clustering. We demonstrate that co-clustering, but not self-sorting, mitigates signal attenuation in a binary community of enzyme-containing sender and receiver protocells due to increased proximity effects. The level of signal attenuation is correlated with a time-dependent pH-mediated switch in the spatial organization of the sender and receiver populations. Our results pave the way towards the development of programmable networks of adaptive life-like objects and could have implications for the development of interactive cytomimetic materials and agent-based robotics.
MoreTranslated text
Key words
hairy protocells,polymer coated colloidosomes,interprotocellular communication,homotypic and heterotypic interactions,biocatalysis
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined