Research Focus Areas
Combining theory, computation, and biology to expand next-generation RNA technologies
RNA Structure Prediction
Developing and applying advanced algorithms to capture RNA structures, folding dynamics, and kinetic pathways with high accuracy.
Rational RNA Design
Building AI-driven design systems that enable sequence generation with target structures and programmable kinetic folding routes.
RNA Therapeutics
Building next-generation RNA-based therapies that combine robust stability, exceptional specificity to meet diverse medical challenges.
Recent Publications
Our research contributions to the scientific community
Identification of conserved RNA regulatory switches in living cells using RNA secondary structure ensemble mapping and covariation analysis
Ivana Borovská, Chundan Zhang, Sarah-Luisa J. Dülk, Edoardo Morandi, Marta F. S. Cardoso, Billal M. Bourkia, Daphne A. L. van den Homberg, Michael T. Wolfinger, Willem A. Velema, Danny Incarnato
Nature Biotechnology. (2025)
doi:10.1038/s41587-025-02739-0 | PDF | Journal article
This paper reports the discovery of conserved RNA regulatory switches in living cells by integrating RNA secondary structure ensemble mapping with covariation analysis, revealing structural elements that control gene expression at the RNA level.
From structure to function: Computational insights into Musashi-RNA complexes in the context of viral pathogenesis
Nitchakan Darai, Leonhard Sidl, Thanyada Rungrotmongkol, Peter Wolschann, Michael T. Wolfinger
Science Asia 51S(1) 2025s013:1-10 (2025)
doi:10.2306/scienceasia1513-1874.2025.s013 | PDF | Review article
This paper reviews computational and structural insights into Musashi–RNA complexes, emphasizing how Musashi proteins interact with viral RNAs to modulate replication and pathogenesis, and exploring implications for antiviral strategies and synthetic biology.
KinPFN: Bayesian approximation of RNA folding kinetics using prior-data fitted networks
Dominik Scheuer, Frederic Runge, Jörg K.H. Franke, Michael T. Wolfinger, Christoph Flamm, Frank Hutter
The Thirteenth International Conference on Learning Representations (ICLR'25) (2025)
doi:10.5281/zenodo.15233965 | PDF | Conference article
This paper presents KinPFN, a deep-learning method based on prior-data fitted networks that approximates RNA folding-time distributions from only a few simulated examples, enabling orders-of-magnitude faster and accurate modeling of RNA folding kinetics and related biological processes.
Bayesian approximation of RNA folding times
Dominik Scheuer, Frederic Runge, Jörg K.H. Franke, Michael T. Wolfinger, Christoph Flamm, Frank Hutter
ICLR 2025 Workshop on AI for Nucleic Acids (2025)
doi:10.5281/zenodo.15228717 | PDF | Conference article
This paper highlights the methodological foundations of KinPFN by detailing its synthetic prior design and in-context learning strategy, and demonstrates how these innovations enable rapid, accurate approximation of RNA folding-time distributions as a lightweight extension to existing kinetic simulators.
Pan-flavivirus analysis reveals sfRNA-independent, 3’UTR-biased siRNA production from an insect-specific flavivirus
Benoit Besson, Gijs J. Overheul, Michael T. Wolfinger, Sandra Junglen, Ronald P. van Rij
Journal of Virology e01215-24 (2024)
doi:10.1128/jvi.01215-24 | Preprint PDF | Journal article
This paper shows that mosquito-specific flaviviruses, such as Kamiti River virus, exploit their unusually long RNA tail to drive a distinct small-RNA immune reaction in mosquitoes, pointing to a novel way these viruses interact with insect hosts.
Xinyang flavivirus, from Haemaphysalis flava ticks in Henan province, China, defines a basal, likely tick-only flavivirus clade
Lan-Lan Wang, Qia Cheng, Natalee D. Newton, Michael T. Wolfinger, Mahali S. Morgan, Andrii Slonchak, Alexander A. Khromykh, Tian-Yin Cheng, Rhys H. Parry
Journal of General Virology 105(5) (2024)
doi:10.1099/jgv.0.001991 | PDF | Journal article
This paper describes the discovery of Xinyang flavivirus, a new tick-only virus from China that represents a previously unknown branch of the flavivirus family, highlighting how unexplored viral diversity in ticks can reveal new evolutionary paths and potential influences on tick-borne disease cycles.