Speaking of Mol Bio Thermo Fisher Scientific

The safety of our food supply, and the people that work to ensure it, are often taken for granted but critically important. Join us to learn about the unsung heroes, both people and molecular methods, that deliver food heath and safety.

What do CROs do and how important is molecular biology to them in supporting their customer’s projects? Join us to meet Saboor Hekmaty, Director of Laboratory Operations at Avrok Biosciences, who answers these questions, and more.

We kick off Season 2 with a great conversation featuring an entrepreneur using high-throughput transcriptomics and AI to innovate drug discovery. Join us to learn how miniaturization and automation are generating astoundingly large transcriptomics data sets and how generative AI methods are being applied to link the structure and activity of small molecules with RNA fingerprints.

Sneak a peek at what’s to come in Season 2 of the series. Steve dishes on what changes to expect, as well as what constants persist as the series evolves. He also teases on topics to be covered in upcoming episodes, which drop soon.

In this episode of Speaking of Mol Bio, we speak with Dr. Mandovi Chatterjee about single cell analysis. Dr. Chatterjee is the Director of Single Cell Core at Harvard University Medical School. She has spent her career at the exploring and mastering a wide variety of single cell methods . In conversation with Dr. Chatterjee, our hosts dive into the more technical aspects of this ever-evolving field. We also learn an interesting analogy to better understand the difference between bulk and single cell RNA sequencing.

In this episode, Gabriel and Steve share a conversation with Dr. Ben Sun about multi-omics and the role AI and machine learning are playing in this space for biomarker discovery and disease prediction. Ben shares his insights from being both a clinician and research scientist, and the conversation delves into some really interesting aspects of using AI, including the consideration of the carbon footprint needed to train an algorithm.