Developing the strains and processes at the Lab.

Problem description

Design-Build-Test-Learn (DBTL) cycles have been widely applied to solve engineering tasks. “Self-driving laboratories” are a next level of DBTL that allow to optimize certain properties of a system of interest in an iterative and fully automated manner.

Once an initial design has been made in-silico, it can be implemented and tested on an automated robotics platform. The thereby generated data are fed to an AI/ML framework that predicts the best design of experiments for the next iteration. A “Selflearning laboratory system” requires well-defined and well-managed workflows, seamless data and information flows between the individual phases, programmatic access to equipment and advanced data analytics across scales and types.

References

Next-Generation Experimentation with Self-Driving Laboratories

Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik
Autonomous experimentation systems for materials development: A community perspective

Eric Stach, Brian De Cost, A. Gilad Kusne, Jason Hattrick-Simpers, Keith A.Brown, Kristofer G.Reyes, Joshua Schrier, Simon Billinge, Tonio Buonassisi, Ian Foster, Carla P. Gomes, John M.Gregoire, Apurva Mehta, Joseph Montoya, Elsa Olivetti, Chiwoo Park
WEBINAR: From Screening to Production: a Holistic Approach of High-throughput Modelbased Bioprocess Development

Peter Neubauer, Mariano Nicolas Cruz Bournazou

Summary

From Screening to Production: a Holistic Approach of High-throughput Modelbased Bioprocess Development. A significantly more efficient development of bioprocesses can only be achieved through a higher degree of automation and digitalisation.
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WEBINAR: Atinary SDLabs: Revolutionize R&D with Machine Learning

Loïc Roch

Summary

Atinary Technologies is a Swiss-American deeptech startup that develops market-leading ML algorithms and technology tools to revolutionize R&D and innovation of advanced materials. In his talk, Dr. Roch will describe how Atinary SDLabs accelerates R&D by orders of magnitude by putting Machine Learning (ML) at the heart of the innovation process.
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Goal

To develop autonomous systems that:

evaluate and develop ‘’self-learning” experimental design algorithms;
combine data-driven approaches with mechanistic modeling;
optimize experimental DBTL algorithms;
apply the developed methods to DBTL use cases in biotech (i) method, (ii) strain and/or (iii) process development.