Séminaire virtuel: vendredi 8 janvier 2021
Thomas E. Gorochowski (University of Bristol, UK) et Olivier Borkowski (Inria and Institut Pasteur)

13h00 - 13h30 – Thomas E. Gorochowski (University of Bristol, UK)

Using diverse sequencing technologies to accelerate genetic circuit design

Synthetic genetic circuits are composed of many interconnected parts that must function together in concert to implement desired biological computations. A major challenge when developing new circuits is that genetic parts often display unexpected changes in their performance when used in new ways. Such changes can arise due to contextual effects or unintended interactions with the host cell. In this talk, I will demonstrate how we have been using a variety of sequencing technologies to tackle problem. First, I will show how RNA-sequencing can be used to measure the function of every transcriptional part making up large genetic circuits. This enables us to better understand why some designs fail and helps pinpoint the root cause. Then, I will present some recent work where we combined RNA-sequencing with ribosome profiling and RNA spike-in standards to enable the first large-scale characterization of transcriptional and translational parts in absolute units. Finally, I will discuss some new work that uses long-read nanopore sequencing to enable the characterization of thousands of genetic parts simultaneously to better understand their design constraints. Taken together, the methods presented provide a means for a more complete and quantitative view of the inner workings of genetic circuits and improves our understanding of the rules governing the effective construction of larger and more complex biological systems.

13h30 - 14h00 – Olivier Borkowski (Inria and Institut Pasteur)

A large-scale exploration of cell-free compositions to maximize protein production using active learning

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. We described an active learning approach to explore a combinatorial space of ~4,000,000 cell-free compositions, maximizing protein production and identifying critical parameters involved in cell-free productivity. We also provided a one-step-method to achieve high quality predictions for protein production using minimal experimental effort regardless of the lysate quality. Eventually, we challenged our method with a collection of E. coli cell-free systems using various homemade cell lysates and lysates supplemented with antibiotics to alter the efficiency of transcription and translation processes.
Joint work with Mathilde Koch, Agnès Zettor, Amir Pandi, Angelo Cardoso Batista, Paul Soudier, and Jean-Loup Faulon at Génomique Métabolique, Genoscope, and Micalis Institute, INRAE, France

Dernière modification le 08/01/2021