Department of Computer Science, Tufts University, Medford, MA 02155, USA.
Metab Eng. 2011 Jul;13(4):435-44. doi: 10.1016/j.ymben.2011.01.006. Epub 2011 Feb 1.
Expression of novel synthesis pathways in host organisms amenable to genetic manipulations has emerged as an attractive metabolic engineering strategy to overproduce natural products, biofuels, biopolymers and other commercially useful metabolites. We present a pathway construction algorithm for identifying viable synthesis pathways compatible with balanced cell growth. Rather than exhaustive exploration, we investigate probabilistic selection of reactions to construct the pathways. Three different selection schemes are investigated for the selection of reactions: high metabolite connectivity, low connectivity and uniformly random. For all case studies, which involved a diverse set of target metabolites, the uniformly random selection scheme resulted in the highest average maximum yield. When compared to an exhaustive search enumerating all possible reaction routes, our probabilistic algorithm returned nearly identical distributions of yields, while requiring far less computing time (minutes vs. years). The pathways identified by our algorithm have previously been confirmed in the literature as viable, high-yield synthesis routes. Prospectively, our algorithm could facilitate the design of novel, non-native synthesis routes by efficiently exploring the diversity of biochemical transformations in nature.
在可进行遗传操作的宿主生物中表达新的合成途径，已成为一种有吸引力的代谢工程策略，可以用于大量生产天然产物、生物燃料、生物聚合物和其他商业有用的代谢物。我们提出了一种途径构建算法，用于识别与细胞平衡生长兼容的可行合成途径。我们不是进行详尽的探索，而是研究反应的概率选择来构建途径。我们研究了三种不同的反应选择方案：高代谢物连接性、低连接性和均匀随机选择。对于所有涉及不同目标代谢物的案例研究，均匀随机选择方案导致了最高的平均最大产率。与枚举所有可能反应途径的穷举搜索相比，我们的概率算法返回了几乎相同的产率分布，而所需的计算时间要少得多（分钟与数年）。我们算法识别的途径在文献中已被证实是可行的、高产率的合成途径。从前景来看，我们的算法可以通过有效地探索自然界中生物化学转化的多样性，促进新型非天然合成途径的设计。
