In this episode of the Computomics podcast, Pernille Ollendorff Hede, CEO and co-founder of Healthycrop, discusses the company's innovative approach to fungal-resistant crops. Healthycrop boosts the production of Nepenthesin, a naturally occurring enzyme in plants, to degrade fungal enzymes, preventing fungal growth without the need for fungicides. Using cis-genesis and gene editing techniques, Healthycrop enhances the plant's natural defense. While these techniques are classified as genetically modified (GM) in Europe, they are considered non-GM in the U.S. Pernille shares the company's mission to revolutionize agriculture by offering sustainable solutions and hopes for regulatory changes in Europe to facilitate broader adoption.

More:

• Healthycrop - Homepage
• Pernille Ollendorff Hede on LinkedIn

In this episode of the Computomics podcast, bioinformatics analyst Michelle Hagen describes how her work bridges biology and data science. As a data analyst in bioinformatics and plant breeding, Michelle transforms raw genetic and environmental data into insights that guide breeding decisions. She particularly enjoys the teamwork, problem-solving, and creativity involved in her job.

Michelle works on Computomics’ Climate-Smart Breeding technology xSeedScore and the Metagenomics Analysis Platform MORPHEUS. Her main tasks involve performing data quality control, running bioinformatics pipelines, and creating interactive dashboards for customers. Her work supports the development of climate-resilient, high-yield crops, and it helps breeders make data-driven breeding faster and more precise.

Glendon Ascough, Research Director at Starke Ayres, discusses the evolution of the company from a historic seed merchant to Africa’s largest independent vegetable breeding company. He shares his personal journey into plant science, driven by a fascination with genetics, and highlights the company’s focus on breeding disease-resistant vegetable varieties tailored to local and international markets. He talks about the collaboration with Computomics, using machine learning to predict sweet corn hybrid performance. Glendon emphasizes the importance of high-quality, diverse phenotypic data and the shift in breeder mindset required for data-driven approaches.