Model-guided systems re-engineering of Chlamydomonas reinhardtii

模型引导的莱茵衣藻系统再造

• 批准号: 1606206
• 负责人: Nitin Baliga
• 金额: $ 29.87万
• 依托单位: Institute for Systems Biology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1606206&HistoricalAwards=false
• 关键词: Model; guided; systems; re; engineering

PI Name: Nitin BaligaProposal Number: 1606206Microscopic algae are a promising future platform for the sustainable production of biofuels. These organisms use sunlight, atmospheric carbon dioxide, and nutrients such as nitrogen and phosphorous dissolved in liquid medium to make lipids which can be processed into liquid transportation fuel. Most algal biofuel processes require two stages. In the first stage, the algae consume nutrients and grow. In the second stage, the lipids used to make biofuel accumulate within the biomass, but only when all the nutrients are consumed so that the biomass does not grow any more. In order to improve the economic viability of algae-based biofuels, it is necessary to develop strains of algae that can generate lipids for biofuel while producing more biomass that sustains the process. The goal of this project is re-program the gene networks in a model strain of algae named Chlamydomonas reinhardtii to enhance both biofuel and biomass production at the same time. The research will attempt to make the systems biology platform more generic that it can be extended to other organisms. The educational activities associated with the project will develop high school curricular materials for renewable green biotechnology topics.A critical challenge with algal biofuel production is that nutrient starvation is required to induce lipid accumulation. The proposed research will develop a systems biology strategy to predictably manipulate regulatory and metabolic networks using the model photosynthetic green microalga Chlamydomonas reinhardtii in an effort to significantly enhance lipid accumulation without growth arrest. A predictive Environment and Gene Regulatory Influence Network (EGRIN) model will be developed to rationally identify gene targets for systems level re-engineering. The EGRIN model will be built upon a compendium of transcriptomes from cultures of C. reinhardtii grown in a diverse set of nutritional and environmental conditions. Accuracy of the EGRIN model will be improved by incorporating experimentally mapped open chromatin structure. Further, the EGRIN model will be integrated with a metabolic network model to identify gene targets for engineering. Finally, model-guided genome engineering of algae using CRISPR/Cas9 technology will be used to enhance biomass or lipid production. The proposed research will generate a fundamental, model-guided strategy for predictably manipulating regulatory and metabolic networks in algae through a generalized approach which can be customized to perform similar strain engineering objectives other organisms.

PI名称：Nitin Baligaproposal编号：1606206Microscopic藻类是可持续生产生物燃料的有前途的未来平台。 这些生物使用阳光，大气二氧化碳以及在液体培养基中溶解的氮和磷等营养素，以制成可以加工成液体运输燃料的脂质。 大多数藻类生物燃料过程都需要两个阶段。 在第一阶段，藻类消耗营养并生长。 在第二阶段，脂质用于使生物燃料积聚在生物质中，但只有当所有营养素被消耗以使生物质不再生长时。 为了改善基于藻类的生物燃料的经济生存能力，有必要开发藻类的菌株，以生成生物燃料的脂质，同时产生更多维持该过程的生物量。该项目的目的是重新编程，该基因网络在名为Chlamydomonas renhardtii的藻类模型菌株中，以同时增强生物燃料和生物量的生产。 该研究将试图使系统生物学平台更通用，以扩展到其他生物体。 与该项目相关的教育活动将开发高中课程材料，用于可再生绿色生物技术主题。藻类生物燃料生产的关键挑战是，需要营养饥饿来诱导脂质积累。 拟议的研究将制定一种系统生物学策略，可以使用模型光合绿色的微alga衣原体reinhardtii来可预测地操纵调节和代谢网络，以显着增强脂质的积累而无需增长。 将开发一个预测环境和基因调节影响网络（EGRIN）模型，以合理地识别系统级重新设计的基因靶标。 Egrin模型将建立在从多种营养和环境条件下生长的氏念珠菌培养物的转录组汇编。 通过合并实验映射的开放染色质结构，将提高艾格林模型的精度。此外，Egrin模型将与代谢网络模型集成在一起，以识别工程基因目标。 最后，使用CRISPR/CAS9技术的藻类模型引导的基因组工程将用于增强生物质或脂质生产。 拟议的研究将产生一种基本的，模型指导的策略，以通过广义方法可以预测地操纵藻类的调节和代谢网络，该方法可以定制以执行类似的应变工程目标其他生物体。