Logical transformation of genome-scale metabolic models for gene level applications and analysis.
BACKGROUND
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 gene-reaction 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.


METHODS
Source code implemented in MATLAB, RAVEN toolbox and COBRA toolbox, is public available at https://sourceforge.net/projects/logictransformationofmodel.
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China, Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden and.
Cheng
C
Zhang
Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden and.
Boyang
B
Ji
Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden and.
Adil
A
Mardinoglu
Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden and.
Jens
J
Nielsen
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China, Shanghai Collaborative Innovation Center for Biomanufacturing Technology (SCICBT), Shanghai 200237, China.
Qiang
Q
Hua
D006528:Carcinoma, Hepatocellular / Q000378:metabolism; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D029968:Escherichia coli Proteins / Q000378:metabolism*; D016680:Genome, Bacterial; D016681:Genome, Fungal; D015894:Genome, Human; D006801:Humans; D008113:Liver Neoplasms / Q000378:metabolism*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D011381:Programming Languages; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D049490:Systems Biology / Q000379:methods*
D029968:Escherichia coli Proteins; D029701:Saccharomyces cerevisiae Proteins