Title :

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

Author(s) :

Coutant, Anthony [Auteur]
Laboratoire d'Informatique de Paris-Nord [LIPN]
Roper, Katherine [Auteur]
University of Manchester [Manchester]
Trejo-Banos, Daniel [Auteur]
Génomique métabolique [UMR 8030]
Bouthinon, Dominique [Auteur]
Laboratoire d'Informatique de Paris-Nord [LIPN]
Carpenter, Martin [Auteur]
University of Manchester [Manchester]
Grzebyta, Jacek [Auteur]
Brunel University London [Uxbridge]
Santini, Guillaume [Auteur]
Laboratoire d'Informatique de Paris-Nord [LIPN]
Soldano, Henry [Auteur]
Institut de Systématique, Evolution, Biodiversité [ISYEB]
Laboratoire d'Informatique de Paris-Nord [LIPN]
Elati, Mohamed [Auteur]
Hétérogénéité, Plasticité et Résistance aux Thérapies des Cancers = Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 [CANTHER]
Génomique métabolique [UMR 8030]
Programme d'Épigénomique
Ramon, Jan [Auteur]
Machine Learning in Information Networks [MAGNET]
Rouveirol, Céline [Auteur]
Laboratoire d'Informatique de Paris-Nord [LIPN]
Soldatova, Larisa [Auteur]
University of London [London]
King, Ross [Auteur]
National Institute of Advanced Industrial Science and Technology [AIST]
The Alan Turing Institute

Journal title :

Proceedings of the National Academy of Sciences of the United States of America

Pages :

18142-18147

Publisher :

National Academy of Sciences

Publication date :

2019

ISSN :

0027-8424

English keyword(s) :

machine learning
diauxic shift
artificial intelligence

HAL domain(s) :

Informatique [cs]/Bio-informatique [q-bio.QM]
Informatique [cs]/Intelligence artificielle [cs.AI]
Sciences du Vivant [q-bio]/Microbiologie et Parasitologie/Mycologie

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

Adaptive Automated Scientific Laboratory

Collections :

• Cancer Heterogeneity, Plasticity and Resistance to Therapies (CANTHER) - UMR 9020 - UMR 1277

Source :

Harvested from HAL