MAtching Genes with MOLecules for FUNctional Analysis (MAGic-MOLFUN)

MAGIC-MOLFUN博士网络（DN）将培养下一代天然产品研究转型专家。他们将接受湿实验室和计算技能相结合的教育，将基因组挖掘和代谢组学与尖端的途径发现和工程方法相结合。这些技能组合的需求正在快速增长，但目前的综合培训课程很少教授这些技能。这些多学科技能和资格将在实现该计划的科学目标的同时获得，即理解和开发用于跨部门应用的微生物NP的复杂生物合成和生产，如医学，食品，农业或生物技术。具体而言，博士生将在三个领域工作：（i）开发新的计算工具和算法，以提高基因组数据中编码NP生物合成的生物合成基因簇的识别和预测质量。这种以基因组为中心的方法得到了以下补充：（ii）使用化学信息学方法将NP的代谢组学数据与生产者的基因组数据联系起来，这将大大改善化合物的发现和去复制过程。这两种以数据为中心的方法最终将（iii）收敛到实验应用中，发现和表征具有有前途的生物活性的新型NP（例如，抗生素、益生菌、农用化学品、生物色素）。科学的培训计划是由一个全面的可转移的技能培训，将装备在工业和学术界的成功职业生涯的今天的需求的DC补充。在DN中获得的技能将使DCs不仅能够在天然产品研究中工作，而且还能够在生物技术的许多其他数据密集型领域工作。

The MAGIC-MOLFUN doctoral network (DN) will train the next generation of specialists for transforming natural products research. They will be educated in a combination of wet-lab and computational skills to integrate genome mining and metabolomics with cutting-edge pathway discovery and engineering approaches. There is a fast-growing demand for these combination of skills, but these are rarely taught in current integrated training programs.These multidisciplinary skills and qualifications will be acquired while achieving the scientific goals of the program, namely understanding and developing the complex biosynthesis and production of microbial NPs for cross-sector applications such as medicine, food, agriculture, or biotechnology. Specifically, the Doctoral Candidates (DCs) will work in three areas: (i) develop novel computational tools and algorithms to improve the identification and prediction quality of biosynthetic gene clusters encoding NP biosynthesis in genomic data. This genome-centred approach is complemented by (ii) the use cheminformatics approaches to link metabolomics data of NPs with the genomic data of the producers, which will greatly improve the compound discovery and dereplication process. These two data-centric approaches will finally (iii) converge into experimental applications that discover and characterize novel NPs with promising bioactivities (e.g., antibiotics, pre-/probiotics, agrichemicals, bio-pigments). The scientific training program is complemented by a comprehensive transferable skill training that will equip the DCs for todays' demands of a successful career in industry and academia. The skills obtained in the DN will enable the DCs to work not only in natural product research but also many other data-intensive areas of biotechnology.