Department of Biomedical Engineering, University of Virginia, Health System, Charlottesville, VA 22908, USA.
Biotechnol J. 2010 Jul;5(7):660-70. doi: 10.1002/biot.201000129.
Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Network analysis can direct microbial development efforts towards successful strategies and enable quantitative fine-tuning of the network for optimal product yields while maintaining the robustness of the production microbe. Metabolic modeling yields insights into microbial function, guides experiments by generating testable hypotheses, and enables the refinement of knowledge on the specific organism. While the application of such analytical approaches to algal systems is limited to date, metabolic network analysis can improve understanding of algal metabolic systems and play an important role in expediting the adoption of new biofuel technologies.
藻类燃料来源以碳中和的方式承诺提供无与伦比的产量，最大限度地减少农业和燃料作物之间的资源竞争。在藻类生物燃料被接受为替代化石燃料战略的一部分之前，必须解决许多挑战。一个重大挑战是，藻类燃料生产的成本必须具有竞争力，才能与现有燃料替代品竞争。藻类生物燃料生产为微调微生物代谢机制以获得最佳生物质成分和所需燃料分子的混合物提供了机会。基于基因组规模模型的藻类代谢设计有望通过指导复杂、相互关联的代谢网络中的代谢物的利用来实现这两个目标，从而优化目标化合物的生产。网络分析可以指导微生物的开发工作朝着成功的策略发展，并能够对网络进行定量微调以获得最佳的产品产量，同时保持生产微生物的稳健性。代谢建模深入了解微生物功能，通过生成可测试的假设来指导实验，并能够细化对特定生物体的了解。尽管此类分析方法在藻类系统中的应用目前受到限制，但代谢网络分析可以提高对藻类代谢系统的理解，并在加速采用新的生物燃料技术方面发挥重要作用。
