Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
Mol Syst Biol. 2011 Aug 2;7:518. doi: 10.1038/msb.2011.52.
Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology.
代谢网络重建包含了生物体代谢和基因组注释的现有知识，为组学数据分析和表型预测提供了一个平台。模式藻衣藻被用于研究从光合作用到趋光性的各种生物过程。由于国际上开发藻类生物燃料的努力，人们对该物种的兴趣最近有所增加。我们整合了生物和光学数据，为这种藻类重建了一个基因组规模的代谢网络，并设计了一种新颖的光模型方法，能够针对给定的光源进行定量生长预测，解决波长和光子通量问题。我们通过模拟和生成新的实验生长数据来实验验证网络中包含的转录本，并验证模型功能的生理学有效性，从而为网络内容和预测应用提供了高度信心。该网络提供了对藻类代谢的深入了解和遗传工程的潜力，以及高效光源设计，这是研究光驱动代谢和定量系统生物学的开创性资源。
