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

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 cur...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2019-09, Vol.116 (36), p.18142-18147
Main 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.
Format: Article
Language:English
Subjects:
Aging
Artificial Intelligence
Automation
Baking yeast
Bioinformatics
Biological Sciences
Biology
Computational Biology
Computer programs
Computer Science
Design of experiments
Experiments
Gene Expression Regulation, Fungal
Immune system
Laboratories
Life Sciences
Microbiology and Parasitology
Mycology
Performance prediction
Physical Sciences
Robotics
Saccharomyces cerevisiae
Saccharomyces cerevisiae - genetics
Saccharomyces cerevisiae - metabolism
Semantic web
Software
Software development tools
Systems Biology
Yeasts
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 (Saccharomyces cerevisiae) 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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1900548116