Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast.

Clicks: 225
ID: 39238
2019
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Ranked #176 of 292 articles by views in Proceedings of the National Academy of Sciences of the United States of America

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Abstract
One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast () diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles.
Reference Key
coutant2019closedloopproceedings Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Coutant, Anthony;Roper, Katherine;Trejo-Banos, Daniel;Bouthinon, Dominique;Carpenter, Martin;Grzebyta, Jacek;Santini, Guillaume;Soldano, Henry;Elati, Mohamed;Ramon, Jan;Rouveirol, Celine;Soldatova, Larisa N;King, Ross D;
Journal Proceedings of the National Academy of Sciences of the United States of America
Year 2019
DOI
10.1073/pnas.1900548116
URL
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