文献类型：期刊文献

英文题名：Logical transformation of genome-scale metabolic models for gene level applications and analysis

作者：Zhang, Cheng[1,2];Ji, Boyang[2];Mardinoglu, Adil[2];Nielsen, Jens[2];Hua, Qiang[1,3]

机构：[1]E China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[2]Chalmers, Dept Biol & Biol Engn, SE-41296 Gothenburg, Sweden;[3]SCICBT, Shanghai 200237, Peoples R China

年份：2015

卷号：31

期号：14

起止页码：2324

外文期刊名：BIOINFORMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000358173500012)】；

基金：Knut and Alice Wallenberg Foundation, the Bill and Melinda Gates Foundation and the European Research Council (247013); National Basic Research Program of China (973 Program) (2012CB721101); China Scholarship Council.

语种：英文

摘要：Motivation: In recent years, genome-scale metabolic models (GEMs) have played important roles in areas like systems biology and bioinformatics. However, because of the complexity of genereaction associations, GEMs often have limitations in gene level analysis and related applications. Hence, the existing methods were mainly focused on applications and analysis of reactions and metabolites. Results: Here, we propose a framework named logic transformation of model (LTM) that is able to simplify the gene-reaction associations and enables integration with other developed methods for gene level applications. We show that the transformed GEMs have increased reaction and metabolite number as well as degree of freedom in flux balance analysis, but the gene-reaction associations and the main features of flux distributions remain constant. In addition, we develop two methods, OptGeneKnock and FastGeneSL by combining LTM with previously developed reaction-based methods. We show that the FastGeneSL outperforms exhaustive search. Finally, we demonstrate the use of the developed methods in two different case studies. We could design fast genetic intervention strategies for targeted overproduction of biochemicals and identify double and triple synthetic lethal gene sets for inhibition of hepatocellular carcinoma tumor growth through the use of OptGeneKnock and FastGeneSL, respectively.