XTMS: pathway design in an eXTended metabolic space.
XTMS: pathway design in an eXTended metabolic space.
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DOI：
10.1093/nar/gku362
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发表时间：
2014-07
影响因子：
14.9
通讯作者：
Faulon JL
中科院分区：
文献类型：
--
作者：
Carbonell P;Parutto P;Herisson J;Pandit SB;Faulon JL
随着代谢工程和合成生物学朝着更可持续地利用生物资源的目标发展，增加工业生物体中可生产的增值化学品的数量的需求变得更加迫切。然而，探索通过在底盘生物体中的异源基因表达进行工程改造的途径的巨大可能性是复杂的，并且无法手动实现。在这里，我们提出了XTMS，一个基于网络的途径分析平台，可在http://xtms.issb.genopole.fr，它提供了一套完整的途径，可以导入到底盘生物体，如大肠杆菌通过应用程序的扩展代谢空间建模框架。XTMS方法包括确定一组生化转化，其可以潜在地在体内处理，如通过分子特征建模，用于推导代谢反应的反应规则的特定编码系统以及所有相应底物和产物的计数。最有前途的路线中描述的代谢物交换，最大允许的途径产量，毒性和酶的效率。通过回答这些关键设计点，XTMS不仅为代谢工程的合理化铺平了道路，而且为非天然代谢物和新型酶促转化开辟了新的加工可能性。
As metabolic engineering and synthetic biology progress toward reaching the goal of a more sustainable use of biological resources, the need of increasing the number of value-added chemicals that can be produced in industrial organisms becomes more imperative. Exploring, however, the vast possibility of pathways amenable to engineering through heterologous genes expression in a chassis organism is complex and unattainable manually. Here, we present XTMS, a web-based pathway analysis platform available at http://xtms.issb.genopole.fr, which provides full access to the set of pathways that can be imported into a chassis organism such as Escherichia coli through the application of an Extended Metabolic Space modeling framework. The XTMS approach consists on determining the set of biochemical transformations that can potentially be processed in vivo as modeled by molecular signatures, a specific coding system for derivation of reaction rules for metabolic reactions and enumeration of all the corresponding substrates and products. Most promising routes are described in terms of metabolite exchange, maximum allowable pathway yield, toxicity and enzyme efficiency. By answering such critical design points, XTMS not only paves the road toward the rationalization of metabolic engineering, but also opens new processing possibilities for non-natural metabolites and novel enzymatic transformations.
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影响因子：
14.9
作者：
McClymont K;Soyer OS
通讯作者：
Soyer OS
影响因子：
--
作者：
Cho A;Yun H;Park JH;Lee SY;Park S
通讯作者：
Park S
影响因子：
14.9
作者：
Kanehisa M;Goto S;Sato Y;Furumichi M;Tanabe M
通讯作者：
Tanabe M
影响因子：
14.9
作者：
Hastings J;de Matos P;Dekker A;Ennis M;Harsha B;Kale N;Muthukrishnan V;Owen G;Turner S;Williams M;Steinbeck C
通讯作者：
Steinbeck C
DOI：
10.1126/science.1191652
发表时间：
2010-10-01
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Ajikumar PK;Xiao WH;Tyo KE;Wang Y;Simeon F;Leonard E;Mucha O;Phon TH;Pfeifer B;Stephanopoulos G
通讯作者：
Stephanopoulos G