Meet Daniel Petras

Daniel investigates the roles played by small molecules, including metabolites and xenobiotics, in microbial interactions. His group develops mass spectrometry methods capable of capturing a broad range of molecules in complex environmental systems, then combines them with complementary approaches to add functional information at scale.

“The interdisciplinary aspect of our research is what excites me the most,” he says. Alongside faster, more sensitive mass spectrometry, Daniel’s group uses computational methods, in-house engineered instruments, biological models, and environmental systems. “While my background is in analytical chemistry and natural product biochemistry, I have the opportunity to learn more and more about microbiology, environmental science, engineering, and data science.”

Daniel predicts that generative AI will “turn our lives and science upside down,” becoming embedded throughout analytical science – from instrument development and experimental design to reporting results and contextualizing findings. That potential comes with risks. “I am afraid that it will be harder for students – and most of us – to build up fundamental knowledge and not outsource the most basic understanding and skills to large language models,” he says. Responsible use of AI in the training of future scientists will therefore be a defining challenge.

A research internship with Pieter Dorrestein at the University of California, San Diego, proved to be the biggest turning point in Daniel’s academic career. It led to a postdoctoral position in Dorrestein’s group and established the collaborative network that continues to support much of his research.

His instinct for building instruments has earlier roots. “Working in my dad’s woodshop when I was a kid made me find joy in building and fixing things,” he says, “which I think my research benefits greatly from.”