Logical transformation of genome-scale metabolic models for gene level applications and analysis
Journal article, 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.

Author

C. Zhang

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Boyang Ji

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Adil Mardinoglu

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

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 14 2324-2331

Industrial Systems Biology of Yeast and A. oryzae (INSYSBIO)

European Commission (FP7), 2010-01-01 -- 2014-12-31.

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Areas of Advance

Life Science Engineering

Subject Categories

Bioinformatics and Systems Biology

DOI

10.1093/bioinformatics/btv134

PubMed

25735769

More information

Latest update

9/6/2018 2