KWS and Metabolomic Discoveries sign research agreement

KWS Saat AG and Metabolomic Discoveries GmbH have signed a three year metabolomics research agreement. Aim is to study and improve KWS crops on the biochemical level. Metabolomics can help to understand the underlying mechanisms of plant characteristics.

“Metabolomics is a very exciting technology for plant breeding,” says Nicolas Schauer, CEO of Metabolomic Discoveries. “Important traits, such as yield, stress tolerance, resistances or seed quality are influenced or regulated by metabolites. Here, our metabolomics and pattern recognition platform will lead to better strategies for crop breeding and help in identifying biomarker for trait prediction.”

Jens Lein of KWS: “We are pleased with the collaboration with Metabolomic Discoveries. Our key concern is to develop plant varieties that deliver high and stable yields. At the same time we see growing demands on crops with better cultivation characteristics. Metabolomics adds valuable input to our research and breeding activities.”

KWS is one of the world's leading plant breeding companies. More than 4,800 employees in 70 countries generated net sales of € 1.178 billion in fiscal 2013/2014. For more than 150 years KWS has operated as an independent company with a tradition of family ownership. Its main areas of work lie in the breeding of varieties and the production and sale of seed for corn, sugarbeet, cereals, potatoes, oil­seed rape and sunflower. The company invests 13 percent of its annual net sales in research and development. KWS uses state­of­the­art plant breeding methods and technologies to continuously improve variety yields and resistances to diseases, pests and abiotic stress.
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Metabolomic Discoveries is a leading research and analytical service company. The company provides global metabolite analysis ­ metabolomics ­ in biological systems. The world­leading advanced machine learning pattern recognition platform provides deep insights into metabolism and allows to develop prediction models leading to the identification of biomarkers.
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