Sybil Herrera Foessel

I work at the intersection of genetics, plant pathology, breeding, and bioinformatics to understand how crops respond to disease, environmental stress, and a changing climate. My current research at SLU Plant Breeding, in Therése Bengtsson’s team, focuses on data-driven approaches to support crop adaptation and resilience. As part of the NorBalFoodSec project, we are building a pan-Nordic database that links results from multilocational variety trials with weather data to better understand yield variation across environments.

Before joining SLU, I worked with spatial transcriptomics at KTH SciLifeLab, where I developed probe-design workflows and analyzed spatial gene expression patterns to study how cells and tissues respond to biological signals. Earlier in my career, I spent many years at CIMMYT in Mexico as a wheat resistance breeder, where my work focused on durable rust resistance, identification of novel resistance genes, and development of molecular markers within international collaborations. I particularly enjoy building international and multidisciplinary research initiatives.

I have studied plant pathology, agricultural sciences, and bioinformatics. I enjoy connecting biological questions with computational and statistical methods, and I see meaningful data integration as an important part of developing more resilient and sustainable crops. I am inspired by projects that connect field-based research with high-technology methods, translating approaches from medical research into agriculture to support future food production.

My research focuses on how plants respond to biotic and abiotic stresses, with particular interest in plant–pathogen interactions, environmental drivers of crop performance, and resilience under climate change. I work with large-scale genomic, transcriptomic, and environmental datasets, applying bioinformatic and statistical modeling approaches to extract biological insight.

Part of my work involves developing integrative and interactive data platforms that can support breeding decisions and improve the use of data in crop improvement.