Dr. Jędrzej Jakub Szymański

In our lab, we examine mechanistic relationships between genetic information and crop quality traits. Utilizing deep learning on extensive sequence and omics datasets, we identify and functionally characterize genetic sequence features that affect gene expression and translate to macro-phenotypes. Our strategy integrates multi-modal data, including genomic, transcriptomic, metabolomic, and phenomic information. Our main research topics are:
AI meets gene regulation. We use deep learning to decipher gene regulatory sequences and protein-DNA interactions, trained on diverse multi-species data. Our accurate models reconstruct gene networks, assess genetic variation impacts, aid GWAS candidate identification, and guide gene editing for expression modulation.
Systems Genetics. We integrate multi-omic data to pinpoint interactions among genetic variation, gene expression, metabolites, and crop traits. Our approach enhances GWAS gene-phenotype identification and tracks molecular events during development and stress responses. We collaborate widely, aiding in understanding genetic associations and advancing targeted breeding.
Gamification of Plant Life. We view plant life as a survival game, creating molecular networks to simulate their growth and adaptive strategies. Developing science-based games, we immerse students and researchers into this world, allowing direct interaction with our models. Our aim: merge science and education, enriching understanding of plant systems biology.