Rubén Garrido-Oter

Rubén Garrido-Oter serves as a Group Leader in the Department of Plant Microbe Interactions at the Max Planck Institute for Plant Breeding Research in Cologne, Germany. His research program focuses on understanding the evolutionary and ecological principles governing complex microbial communities associated with photosynthetic organisms.

His research interests center on microbial ecology and host-microbe interactions, particularly examining how microbiota establish, maintain stability, and evolve within plant hosts. Using reductionist experimental approaches and computational tools with tractable microbial culture collections, his lab develops mechanistic understanding of microbe-microbe and host-microbe interactions. His work spans evolutionary biology, ecological dynamics, and computational analysis of microbial communities.

Analysis of his recent publications reveals a strong focus on plant root microbiomes, host specificity, and microbial community assembly. His work frequently employs model systems like Arabidopsis thaliana and Lotus japonicus, examining how microbial communities differ between wild and domesticated plants. His research bridges fundamental microbial ecology with potential applications in sustainable agriculture.

• European Research Council (ERC) Funding

Dr. Garrido-Oter's research is supported by significant funding from the European Research Council (ERC), the German Research Foundation (DFG), and the Max Planck Society. He is actively recruiting PhD and postdoctoral scientists for his research group. His lab is part of the Cluster of Excellence in Plant Sciences (CEPLAS), contributing to collaborative research on plant-microbe interactions.

His laboratory employs both experimental and computational approaches to study host-associated microbial communities, developing reductionist systems based on large-scale microbial culture collections. The group contributes to theoretical plant biology and data science through development of tools like Rbec for analyzing amplicon sequencing data from synthetic microbial communities.