Computational and Experimental Systems Biology of Cyanobacterial Metabolism

蓝藻，有时被称为蓝绿藻，可以利用太阳能将二氧化碳转化为生物燃料和生物产品。因此，它们为燃料、材料和其他化学品的可持续生产提供了潜力。以具有成本效益的方式实现这一潜力将需要对蓝藻的代谢（化学反应）有深入的了解，该项目将应用最新的计算和实验技术来研究蓝藻代谢。这项研究的目标是产生一个全面的计算模型，描述蓝藻如何利用碳和光或化学能在光明或黑暗的环境。这种方法将导致控制代谢物合成中的碳流的预测模型，这是提高蓝藻生产有价值生物产品能力的关键工具。该项目包括推进本科生和研究生科学教育的举措，包括一个关注科学领域代表性不足群体的夏季本科生研究项目；本项目采用先进的计算和实验技术，编写新的本科和研究生教材；以及为高中教师和学生举办的研讨会。系统生物学的方法将结合，以提高对水生光养生物代谢和生长的理解。实验方法（在许多生长条件下进行的综合代谢物测量，包括13C动力学通量谱）和计算方法（全细胞水平代谢的数学动力学通量模型）将用于量化和预测蓝藻聚藻球菌（PCC 7002）的三种基本生长生理：光自养生长、氧化呼吸和自发酵。这种综合方法将导致代谢通量的预测控制，包括碳分支和细胞内能量平衡。该项目将开发和应用适合实验测量的全细胞动力学模型，计算模型将通过对基因敲除效果的预测和实验测试以及对已确定目标的上下调节来验证。该项目将专门寻找在光自养生长过程中增加糖原积累和在自发酵过程中增加氢产量的基因操作。这些知识将广泛适用于了解蓝藻代谢和提高许多生物产品的产量，而不仅仅是生物燃料。该奖项由分子和细胞生物科学部的系统和合成生物学项目以及化学、生物工程、环境和运输系统部的生物医学工程项目共同资助。

Cyanobacteria, sometimes called blue-green algae, can harness solar energy to convert carbon dioxide into biofuels and bioproducts. Thus they provide a potential for sustainable production of fuels, materials, and other chemicals. Realizing this potential in a cost-effective manner will require a deep understanding of the metabolism (chemical reactions) of cyanobacteria and this project will apply the latest computational and experimental techniques to study cyanobacterial metabolism. The goal of this research is to produce a comprehensive computational model that describes how cyanobacteria utilize carbon and light or chemical energy in light or dark environments. This approach will lead to predictive models for the control of carbon flow in the synthesis of metabolites, a key tool for improving the ability of cyanobacteria to produce valuable bioproducts. The project includes initiatives to advance undergraduate and graduate science education, including a summer undergraduate research program focusing on underrepresented groups in science; new undergraduate and graduate teaching materials based on the advanced computational and experimental techniques used in the project; and a workshop for high-school teachers and students. Systems biology approaches will be combined to advance understanding of metabolism and growth in aquatic phototrophs. Experimental approaches (comprehensive metabolite measurements made over many growth conditions and including 13C kinetic flux profiling) and computational approaches (mathematical kinetic flux model of metabolism at the whole-cell level) will be applied to quantify and predict three basic growth physiologies in the cyanobacterium Synechococcus sp. PCC 7002: photoautotrophic growth, oxidative respiration, and auto-fermentation. This integrated approach will lead to predictive control of metabolic fluxes, including carbon branching and intracellular energy balance. The project will develop and apply a whole-cell kinetic model fitted to the experimental measurements and the computational model will be validated by prediction and experimental testing of the effects of genetic knockouts and up or down regulation of identified targets. The project will search specifically for genetic manipulations that increase glycogen accumulation during photoautotrophic growth and hydrogen production during auto-fermentation. This knowledge will be widely applicable to understanding cyanobacterial metabolism and to improving yields of many bioproducts, not just biofuels.This award is funded jointly by the Systems and Synthetic Biology Program in the Division of Molecular and Cellular Biosciences and the Biomedical Engineering Program in the Division of Chemical, Bioengineering, Environmental and Transport Systems.