ATCC's Podcast: Behind the Biology

In this episode of Behind the Biology, David Yarmosh and Dr. Sanjeev Thohan explore how failure can become a powerful source of scientific knowledge. Sanjeev discusses the importance of separating the person from the experiment, examining what went wrong, and sharing unsuccessful results so others can learn from them. The conversation then turns to artificial intelligence and its potential to accelerate drug discovery by identifying patterns researchers might otherwise miss. Sanjeev also offers a grounded view of AI’s limitations, emphasizing that these tools are only as strong as the information they learn from—and that human creativity, experience, and judgment will remain essential to meaningful scientific progress.

What happens after the science works—but the path forward is still uncertain? In this episode, Dr. Sanjeev Thohan walks David Yarmosh through pivotal moments in malaria research, antiviral development, and biodefense that shaped his perspective on translating promising results into real-world impact. He discusses why strong data alone is not enough, highlighting the role of regulatory strategy, cross-functional teamwork, funding, and manufacturing in determining whether a program can advance. The episode also examines the leadership judgment required to know when to push ahead, when to pivot, and when to hand a promising effort to the team best positioned to carry it forward.

In this episode of Behind the Biology, David Yarmosh talks with Dr. Sanjeev Thohan about the curiosity, mentorship, and career experiences that shaped his approach to science and leadership. Sanjeev reflects on the value of asking both the obvious and overlooked questions, recognizing patterns across complex problems, and developing the situational awareness needed to move scientific programs forward. The conversation also explores the realities of startup leadership, including how difficult decisions, lessons from failure, and a willingness to challenge assumptions can help teams uncover opportunities others may miss.

In this episode, Dr. Ryan Wick explores the technologies and ideas that are shaping the future of microbial genomics. Dr. Wick discusses how advances in machine learning have dramatically improved the accuracy of next-generation sequencing, helping scientists translate complex electrical signals into reliable DNA sequences. He explains the differences between traditional computational approaches and modern AI-driven methods, highlighting how improvements in algorithms and biochemistry have worked together to advance long-read sequencing. Looking ahead, Dr. Wick shares his perspective on emerging opportunities in metagenomics, eukaryotic genome assembly, and the continued evolution of tools designed to make genomic analysis more accurate and accessible.

In this episode, Dr. Ryan Wick discusses the path that led him into bioinformatics and the technologies that have shaped his work in bacterial genomics. Dr. Wick shares how an early interest in biology evolved through careers in healthcare software and science education into a passion for programming and computational biology. He explains how that journey led to the development of a tool that helps scientists visualize genome assembly graphs, and later a hybrid assembler that combines short- and long-read sequencing data to create complete bacterial genome assemblies. Dr. Wick also explores how advances in next-generation sequencing, machine learning, and bioinformatics algorithms have improved the accuracy and accessibility of genome assembly.