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Industry interviews
Nautilus Biotechnology
February 26, 2026
Professor Olga Vitek has a deep understanding of statistics, machine learning, and computational biology. She puts her know-how to work to develop computational tools enabling high-quality proteomic analysis and systems biology approaches. She hopes to apply these tools to the quantitative analysis of large-scale mass spectrometry-based investigations and thereby advance our understanding of organismal function. In this episode, Olga and Parag discuss:
• Why statistics is important for experimental design
• How statistics and AI can help researchers understand biology
• Gaps keeping us from using AI and statistics to their maximum potential in biology
Find this episode on YouTube , Apple Podcasts , and Spotify .
Chapters
00:00 – 01:26 – Intro 01:27 – 04:26 – Why did Olga decide to apply statistics to biology and proteomics in particular? 04:27 – 06:13 – Factors leading to the adoption of statistics in proteomics 06:14 – 10:06 – Why do we need statistics for experimental design? 10:07 – 14:58 – How does statistics deal with observational experiments? 14:59 – 19:04 – Statistical principles Olga wishes more researchers were aware of 19:05 – 27:21 – How do we balance the use of AI models with the need for rigor and interpretability in our analyses? 27:22 – 36:11 – Combining data from multiple sources using tools that reason on the language of biological molecules 36:12 – 43:34 – In Olga’s dream future, how will researchers be using AI, statistics, and machine learning? 43:35 – 45:25 – What gaps are keeping us from achieving Olga’s dream? 45:25 – End – Outro
Resources
Statistical methods for studies of biomolecular systems website Olga’s personal lab website.
Beyond protein lists: AI-assisted interpretation of proteomic investigations in the context of evolving scientific knowledge Gyori and Vitek, 2024 discuss how AI can be used to interpret proteomics data and its biological meaning.
A Bayesian Active Learning Experimental Design for Inferring Signaling Networks Ness et al., 2018 show how statistical methods can guide the selection of experiments that optimally enhance understanding.
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