Titre :

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

Auteur(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]
Laboratoire d'Informatique de Paris-Nord [LIPN]
Institut de Systématique, Evolution, Biodiversité [ISYEB]
Elati, Mohamed [Auteur]
Programme d'Épigénomique
Génomique métabolique [UMR 8030]
Hétérogénéité, Plasticité et Résistance aux Thérapies des Cancers = Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 [CANTHER]
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]
The Alan Turing Institute
National Institute of Advanced Industrial Science and Technology [AIST]

Titre de la revue :

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

Pagination :

18142-18147

Éditeur :

National Academy of Sciences

Date de publication :

2019

ISSN :

0027-8424

Mot(s)-clé(s) en anglais :

machine learning
diauxic shift
artificial intelligence

Discipline(s) HAL :

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

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Adaptive Automated Scientific Laboratory

Collections :

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

Source :

Harvested from HAL