Model-guided systems re-engineering of Chlamydomonas reinhardtii

PI名称：Nitin Bligia Proposal编号：1606206显微藻类是可持续生产生物燃料的未来平台。这些生物利用阳光、大气中的二氧化碳以及溶解在液体介质中的氮和磷等营养物质来制造脂类，这些脂类可以加工成液体运输燃料。大多数藻类生物燃料过程需要两个阶段。在第一阶段，藻类消耗营养物质并生长。在第二阶段，用于制造生物燃料的脂肪在生物质中积累，但只有在所有营养物质被消耗后，生物质才不会再生长。为了提高以藻类为基础的生物燃料的经济可行性，有必要培育能够产生用于生物燃料的脂类的藻类菌株，同时生产更多维持这一过程的生物质。该项目的目标是对一种名为Chlamydomonas rehardtii的藻类模式菌株的基因网络进行重新编程，以同时提高生物燃料和生物量的产量。这项研究将试图使系统生物学平台更加通用，从而可以扩展到其他生物体。与该项目相关的教育活动将为可再生的绿色生物技术主题开发高中课程材料。藻类生物燃料生产的一个关键挑战是需要营养饥饿来诱导脂肪积累。拟议的研究将开发一种系统生物学策略，利用光合作用绿色微藻衣藻衣藻的模型，可预测地操纵调节和代谢网络，以努力在不停止生长的情况下显著促进脂质积累。一个预测环境和基因调控影响网络(EGrin)模型将被开发，以合理地识别系统级重组的基因靶点。EGrin模型将建立在一套不同的营养和环境条件下生长的莱茵梭菌培养物的转录本概要上。通过结合实验绘制的开放染色质结构，EGrin模型的准确性将得到提高。此外，EGrin模型将与代谢网络模型相结合，以确定用于工程的基因靶点。最后，将使用CRISPR/Cas9技术对藻类进行模型引导的基因组工程，以提高生物量或脂肪产量。拟议的研究将产生一个基本的、模型指导的策略，通过一个通用的方法来预测地操纵藻类中的调节和代谢网络，该方法可以定制以执行类似的菌株工程目标，其他生物体。

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.