Mahadevan Radhakrishnan, von Kamp Axel, Klamt Steffen
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, M5S3E5, Canada, Institute of Biomaterials and Biomedical Engineering, Toronto, ON, M5S 3G9, Canada and.
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, D-39106, Germany.
Bioinformatics. 2015 Sep 1;31(17):2844-51. doi: 10.1093/bioinformatics/btv217. Epub 2015 Apr 25.
Stoichiometric and constraint-based methods of computational strain design have become an important tool for rational metabolic engineering. One of those relies on the concept of constrained minimal cut sets (cMCSs). However, as most other techniques, cMCSs may consider only reaction (or gene) knockouts to achieve a desired phenotype.
We generalize the cMCSs approach to constrained regulatory MCSs (cRegMCSs), where up/downregulation of reaction rates can be combined along with reaction deletions. We show that flux up/downregulations can virtually be treated as cuts allowing their direct integration into the algorithmic framework of cMCSs. Because of vastly enlarged search spaces in genome-scale networks, we developed strategies to (optionally) preselect suitable candidates for flux regulation and novel algorithmic techniques to further enhance efficiency and speed of cMCSs calculation. We illustrate the cRegMCSs approach by a simple example network and apply it then by identifying strain designs for ethanol production in a genome-scale metabolic model of Escherichia coli. The results clearly show that cRegMCSs combining reaction deletions and flux regulations provide a much larger number of suitable strain designs, many of which are significantly smaller relative to cMCSs involving only knockouts. Furthermore, with cRegMCSs, one may also enable the fine tuning of desired behaviours in a narrower range. The new cRegMCSs approach may thus accelerate the implementation of model-based strain designs for the bio-based production of fuels and chemicals.
MATLAB code and the examples can be downloaded at http://www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html.
krishna.mahadevan@utoronto.ca or klamt@mpi-magdeburg.mpg.de
Supplementary data are available at Bioinformatics online.
化学计量学和基于约束的计算菌株设计方法已成为合理代谢工程的重要工具。其中一种方法依赖于约束最小割集（cMCSs）的概念。然而，与大多数其他技术一样，cMCSs可能仅考虑反应（或基因）敲除以实现所需表型。
我们将cMCSs方法推广到约束调节最小割集（cRegMCSs），其中反应速率的上调/下调可以与反应缺失相结合。我们表明，通量上调/下调实际上可以被视为割集，从而允许它们直接集成到cMCSs的算法框架中。由于基因组规模网络中的搜索空间大幅扩大，我们开发了策略来（可选地）预先选择适合通量调节的候选对象，并开发了新颖的算法技术以进一步提高cMCSs计算的效率和速度。我们通过一个简单的示例网络说明了cRegMCSs方法，然后将其应用于在大肠杆菌的基因组规模代谢模型中确定乙醇生产的菌株设计。结果清楚地表明，结合反应缺失和通量调节的cRegMCSs提供了更多合适的菌株设计，其中许多相对于仅涉及敲除的cMCSs要小得多。此外，使用cRegMCSs，还可以在更窄的范围内对所需行为进行微调。因此，新的cRegMCSs方法可能会加速基于模型的菌株设计在生物基燃料和化学品生产中的实施。
MATLAB代码和示例可从http://www.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html下载。
krishna.mahadevan@utoronto.ca或klamt@mpi-magdeburg.mpg.de
补充数据可在《生物信息学》在线获取。
