Dr. José M Jiménez-Gómez joins CBGP to lead the new Adaptive Genetics and Genomics Lab

Dr. José M. Jiménez-Gómez has recently established the Adaptive Genetics and Genomics Lab in the Computational Systems Biology and Genomics (CsBGP) programme at the CBGP (UPM-INIA/CSIC). He relocated to CBGP (UPM-INIA/CSIC) his group from the Institut Jean-Pierre Bourgin in Versailles (IJPB-INRAe), France. His research uses a combination of genomics, genetics and molecular biology to study the genetic basis of plant evolution, with a special focus in crop domestication.


Since their appearance on earth, plants have evolved mechanisms to thrive in every possible ecosystem, including agricultural settings. Understanding the molecular basis of how plants adapt to these environments is one of the mayor quests in modern biology, essential for the development of novel crop varieties adapted to novel environmental conditions and the goal of the Adaptive Genomics and Genetics group led by Dr. José M Jiménez-Gómez. His group combines expertise in bioinformatics and genomics with classic genetics and modern molecular biology techniques in two model systems: Arabidopsis thaliana and tomato. Some of the recent developments in his group include the finding that domestication of tomato delayed its circadian rhythms, the characterization of natural alleles existing for one of the mayor determinants of flowering time in Arabidopsis, or the identification of a gene controlling photoperiod sensitivity in tomato that can be edited to accelerate production in this crop.

Dr. José M Jiménez-Gómez finished his PhD in 2005, working in quantitative genetics of flowering time in tomato in the National Centre of Biotechnology (CNB-CSIC), Madrid, Spain, under the supervision of Dr. Jose Miguel Martínez-Zapater. He then moved for a postdoctoral stage to the lab of Dr. Julin Maloof at the University of California Davis (USA), where he developed skills in bioinformatics and genomics applied to the study of natural variation in Arabidopsis thaliana. In 2010, he started the Adaptive Genomics and Genetics group in the Max Planck Institute for Plant Breeding Research, in Germany, where we focused in the application of genomics and bioinformatics to the study of plant evolution and domestication. In 2013 his lab moved to the Institut Jean-Pierre Bourgin in Versailles (IJPB-INRAe), France, where he continued to work on these topics.

In December 2021, he has joined the CBGP (UPM-INIA/CSIC) as Group Leader, after obtaining a permanent position as Scientist at INIA/CSIC. In the coming years, he will aim at advancing our understanding of what are the genes and features involved in plant domestication, that is needed to improve the adaptation of crops to the new climate and environmental conditions.

About the CBGP: The mission of the CBGP (UPM-INIA/CSIC) is to carry out fundamental and strategic research in plant science and in microorganisms interacting with plants. The research is focused on understanding important biological processes such as plant development, the interaction of plants with the environment and the mechanisms of plant nutrition. Additionally, CBGP (UPM-INIA/CSIC) is interested in developing and using computational biology tools to achieve its goals. The acquired knowledge is used to tackle major problems of agriculture and forestry, and to develop novel technological solutions. The Center has also an educational role and is a reference center for training scientists and Master's and Bachelor-level students in plant biotechnology and genomics. The CBGP (UPM-INIA/CSIC) has been recognized by the Spanish Research Agency (AEI) as Centre of Excellence Severo Ochoa, the highest institutional recognition of scientific research excellence in Spain.

About CsBGP: The CBGP (UPM-INIA/CSIC) Computational Systems Biology and Genomics (CsBGP) programme is designed to develop new research and analysis tools within the area of computational biology and genomics through the application of biological systems level approaches, and the analysis and modeling of large volumes of biological data.