Sinitcyn Lab | Utrecht University

Computational Biomolecular Mass Spectrometry Group

Bridging Machine Learning, Computer and Life Sciences. We fuse algorithm design, large-scale data analysis, and frontier proteomics to decode molecular systems.

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Our Mission

Modern biology generates vast amounts of data, yet interpreting it remains a bottleneck. We believe that domain knowledge is crucial for making sense of this data. Our group addresses the unique challenges of biological data - noise, sparsity, and complexity - by fusing recent advances in machine learning with practical applications.

We are a bridge between AI Technology for Life (AIT4Life) and Biomolecular Mass Spectrometry and Proteomics (BioMS), connecting expertise across the Information and Computing Sciences and Pharmaceutical Sciences departments.

We aim to develop explainable models that generalize well, helping us better understand and predict stochastic biological processes at the molecular level.

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Research Areas

Research Area Illustration
  • Computational Mass Spectrometry: Developing statistical tools for shotgun proteomics and Data-Independent Acquisition (DIA).
  • Predictive Modeling: forecasting peptide behavior (digestion, fragmentation) to sequence amino acids and study the entire proteome.
  • Trustworthy AI: Accurately estimating false discovery rates and defining limits of applicability for AI models.
  • Generative Approaches: Using protein language models to generate realistic decoy peptides, and validating them with separability tests and MS-based benchmarks.

Community & Collaboration

We are dedicated to building a computational biology community at Utrecht Science Park that connects project ideas, students, and partners from academia and industry. We currently partner with clinicians, instrument vendors, and computer scientists to bridge distinct fields.

Interested in pushing the boundaries of proteomics with us? Get in touch.

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