Logical transformation of genome-scale metabolic models for gene level applications and analysis
Artikel i vetenskaplig tidskrift, 2015

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.

Författare

C. Zhang

Chalmers, Biologi och bioteknik, Systembiologi

Boyang Ji

Chalmers, Biologi och bioteknik, Systembiologi

Adil Mardinoglu

Chalmers, Biologi och bioteknik, Systembiologi

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

Q. Hua

Shanghai Collaborative Innovation Center for Biomanufacturing Technology

East China University of Science and Technology

Bioinformatics

1367-4803 (ISSN) 1367-4811 (eISSN)

Vol. 31 2324-2331

Infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

Styrkeområden

Livsvetenskaper och teknik

Ämneskategorier

Bioinformatik och systembiologi

DOI

10.1093/bioinformatics/btv134

PubMed

25735769