Lysate-based cell-free systems have become a major platform to study gene expression, but batch-to-
batch variation makes protein production difficult to predict. During this webinar, you will learn about an active learning approach to explore a combinatorial space of approximately 4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity.
Also, discover a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality.
Join molecular physiologist and microbial bioengineer, Olivier Borkowski from Institut Pasteur and Inria, who is working on model bacteria and yeast. His scientific interest, from his PhD to current work, focuses on the relationship between cell physiology and gene expression. During his PhD, he worked on B. subtilis to understand the impact of growth rate on protein production using a systems-oriented approach. Then, during his postdoc, he focused on the impact on protein production on the growth rate. He has developed a method to predict the cost of heterologous protein production using cell-free systems (in vitro expression) and models of the translation process. In parallel, he used synthetic biology approaches to build:
- Genetic circuits sensing the changes in cell physiology in different growth phases to control protein production in E. coli using genetic logic gates
- Autonomous "feedback control" circuits to sense and reduce the weight of resource competition between the host and synthetic circuits
Register today and learn more about cell-free expression systems and lab automation - two major technologies which are spreading in biological laboratories. Such technologies increase experimental scale for cellular and molecular biology.
If you are a laboratory manager/director, post-doc, and/or you have experience managing lab techs, monitoring project progress, evaluating and bringing online new technologies, and are involved in process improvement, you should attend and learn:
- Cell-free systems methods
- How to use machine learning in molecular biology
- The experimental scale achievable with state-of-the-art robots
- The importance of chassis understanding for synthetic biology
Presenters
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