Developing the strains and processes at the Lab.
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.
Next-Generation Experimentation with Self-Driving Laboratories
Autonomous experimentation systems for materials development: A community perspective
WEBINAR: From Screening to Production: a Holistic Approach of High-throughput Modelbased Bioprocess DevelopmentSummary
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.Read more
WEBINAR: Atinary SDLabs: Revolutionize R&D with Machine LearningSummary
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.Read more
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.