Research Online

Logo

Goldsmiths - University of London

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

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 and King, Ross 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, 116(36), pp. 18142-18147. ISSN 0027-8424 [Article]

[img] Text
revised_manuscript.pdf
Permissions: Administrator Access Only

Download (2MB)
[img]
Preview
Text
18142.full.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview

Abstract or Description

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.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1073/pnas.1900548116

Keywords:

artificial intelligence, machine learning, diauxic shift

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
3 September 2019Published
16 August 2019Published Online
23 July 2019Accepted

Item ID:

27135

Date Deposited:

14 Oct 2019 10:08

Last Modified:

16 Oct 2019 13:26

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/27135

View statistics for this item...

Edit Record Edit Record (login required)