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A synthetic multi-cellular network of coupled self-sustained oscillators
Author(s) -
Miguel Fernández-Niño,
Daniel Giraldo,
Judith Lucia GomezPorras,
Ingo Drèyer,
Andrés Fernando González Barrios,
Catalina Arévalo-Ferro
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0180155
Subject(s) - modular design , synthetic biology , cluster analysis , synchronization (alternating current) , computer science , multicellular organism , biological system , synchronization networks , biological network , cellular network , population , systems biology , synthetic data , distributed computing , artificial intelligence , biology , bioinformatics , computer network , channel (broadcasting) , biochemistry , demography , sociology , gene , operating system
Engineering artificial networks from modular components is a major challenge in synthetic biology. In the past years, single units, such as switches and oscillators, were successfully constructed and implemented. The effective integration of these parts into functional artificial self-regulated networks is currently on the verge of breakthrough. Here, we describe the design of a modular higher-order synthetic genetic network assembled from two independent self-sustained synthetic units: repressilators coupled via a modified quorum-sensing circuit. The isolated communication circuit and the network of coupled oscillators were analysed in mathematical modelling and experimental approaches. We monitored clustering of cells in groups of various sizes. Within each cluster of cells, cells oscillate synchronously, whereas the theoretical modelling predicts complete synchronization of the whole cellular population to be obtained approximately after 30 days. Our data suggest that self-regulated synchronization in biological systems can occur through an intermediate, long term clustering phase. The proposed artificial multicellular network provides a system framework for exploring how a given network generates a specific behaviour.

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