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

将基因与分子匹配进行功能分析 (MAGic-MOLFUN)

• 批准号: EP/X03142X/1
• 负责人: Katherine Duncan
• 金额: $ 33.8万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX03142X%2F1
• 关键词: MAtching; Genes; MOLecules; FUNctional; Analysis

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.

MAGIC-MOLFUN博士网络将培养转化天然产品研究的下一代专家。他们将在湿实验室和计算技能的结合中接受培训，将基因组挖掘和代谢组学与尖端途径发现和工程方法相结合。对这些技能组合的需求迅速增长，但在目前的综合培训计划中很少传授这些技能和资格。这些多学科技能和资格将在实现计划的科学目标的同时获得，即了解和开发用于跨行业应用的微生物纳米粒子的复杂生物合成和生产，如医药、食品、农业或生物技术。具体地说，博士生将在三个方面工作：(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(Ii)使用化学信息学方法将NPs的代谢组学数据与生产者的基因组数据联系起来的补充，这将极大地改进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)汇聚为实验应用，发现并表征具有良好生物活性的新型NPs(例如，抗生素、前/益生菌、农用化学品、生物色素)。与科学培训计划相辅相成的是全面的可转移技能培训，这将为区议会今天在工业和学术界成功职业生涯的需求做好准备。在发展网络中获得的技能将使发展中国家不仅能够从事天然产品研究，而且还能够在生物技术的许多其他数据密集型领域开展工作。