Abstract:
Our understanding of plant gene function largely relies on data from the model plant Arabidopsis thaliana that is used to annotate genes from other species by genomic approaches. However, genomic approaches have limitations, as they often cannot accurately predict gene function for most genes. In this talk, I talk about how transcriptomic data can make accurate gene function predictions and produce insights not attainable by genomic approaches, such as predicting how plants behave in complex environments. I discuss how the accumulating gene functional data and large language models, such as GPT, can be used to produce robust, kingdom-wide inferences about various aspects of plant biology.

Bio:
Assoc. Prof. Marek Mutwil is a computational biologist with a Ph.D. from the University of Potsdam in 2011, Germany, and a Master's (2007) and Bachelor's (2005) degree in Biochemistry from the University of Copenhagen, Denmark. He led the Regulatory Networks group at the Max Planck Institute of Molecular Plant Physiology, Germany (2012-2016). He has been serving as an Assistant Professor (2017-2022) and Associate Professor (2022-) at the Nanyang Technological University, School of Biological Sciences, Singapore.
His research is a fusion of experimental and computational methods, targeting plant evolution, specialized metabolism, and stress acclimation