Algae have great potential to serve as platform strains for engineering efforts, but tools for these species lag behind traditional model systems like E. coli and yeast. Metabolic models are one such tool that would enhance our ability to rationally engineer algae, but current automated algorithms to build models do not perform well for algal genomes. We present a novel software pipeline, Rapid Annotation of Photosynthetic Systems (RAPS), which leverages manual curation efforts of published models to create high quality first draft metabolic networks for new species. We compared models produced by our pipeline to published models and found that these models are able to capture more genes than the published models and are more predictive of experimentally determined growth rate. We used RAPS to automate the creation of 8 first draft metabolic models of new species to enable the further study of metabolism in these species.

藻类具有巨大的潜力可作为工程研究的平台菌株，但这些物种的工具落后于传统模型系统（如大肠杆菌）和酵母。代谢模型就是一种工具，可以增强我们对藻类进行合理工程设计的能力，但是目前用于构建模型的自动化算法对于藻类基因组的效果并不理想。我们提出了一种新颖的软件管道，即光合系统快速注释（RAPS），它利用已发布模型的人工管理成果为新物种创建高质量的初稿代谢网络。我们将管道产生的模型与已发布的模型进行了比较，发现这些模型比已发布的模型能够捕获更多的基因，并且对实验确定的增长率具有更高的预测性。我们使用RAPS来自动创建新物种的8个代谢模型的初稿，从而能够进一步研究这些物种的代谢。