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

Autor: Ross D. King, Jacek Grzebyta, Martin Carpenter, Jan Ramon, Henry Soldano, Céline Rouveirol, Guillaume Santini, Anthony Coutant, Katherine Roper, Larisa N. Soldatova, Daniel Trejo-Banos, Dominique Bouthinon, Mohamed Elati
Přispěvatelé: Laboratoire d'Informatique de Paris-Nord (LIPN), Université Paris 13 (UP13)-Institut Galilée-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS), University of Manchester [Manchester], Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Brunel University London [Uxbridge], Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Programme d'Épigénomique, Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Machine Learning in Information Networks (MAGNET), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of London [London], The Alan Turing Institute, National Institute of Advanced Industrial Science and Technology (AIST), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
0301 basic medicine
Computer science
Systems biology
Distributed computing
0206 medical engineering
Cloud computing
Saccharomyces cerevisiae
02 engineering and technology
Reuse
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
03 medical and health sciences
Software
Gene Expression Regulation
Fungal
Semantic Web
[SDV.MP.MYC]Life Sciences [q-bio]/Microbiology and Parasitology/Mycology
Multidisciplinary
business.industry
Systems Biology
Computational Biology
diauxic shift
Robotics
Biological Sciences
artificial intelligence
Biophysics and Computational Biology
ComputingMethodologies_PATTERNRECOGNITION
030104 developmental biology
Laboratory robotics
machine learning
Physical Sciences
Laboratory automation
Robot
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
business
020602 bioinformatics
Zdroj: Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America, 2019, 116 (36), pp.18142-18147. ⟨10.1073/pnas.1900548116⟩
Proceedings of the National Academy of Sciences of the United States of America, National Academy of Sciences, 2019, 116 (36), pp.18142-18147. ⟨10.1073/pnas.1900548116⟩
Coutant, A, Roper, K, Trejo-Banos, D, Bouthinon, D, Carpenter, M, Grzebyta, J, Santini, G, Soldano, H, Ramon, J, Elati, M, Rouveirol, C, Soldatova, L N & King, R D 2019, ' Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast ', Proceedings of the National Academy of Sciences . https://doi.org/10.1073/pnas.1900548116
ISSN: 0027-8424
1091-6490
DOI: 10.1073/pnas.1900548116⟩
Popis: Significance Systems biology involves the development of large computational models of biological systems. The radical improvement of systems biology models will necessarily involve the automation of model improvement cycles. We present here a general approach to automating systems biology model improvement. Humans are eukaryotic organisms, and the yeast Saccharomyces cerevisiae is widely used in biology as a “model” for eukaryotic cells. The yeast diauxic shift is the most studied cellular transformation. We combined multiple software tools with integrated laboratory robotics to execute three semiautomated cycles of diauxic shift model improvement. All the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for execution. The resulting improved model is relevant to understanding cancer, the immune system, and aging.
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.
Databáze: OpenAIRE
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