Fast-SL: An efficient algorithm to identify synthetic lethal sets in metabolic networks

Bioinformatics

Volumn 31, Issue 20, 2015, Pages 3299-3305

  Pratapa, Aditya ^a   Balachandran, Shankar ^b   Raman, Karthik ^a  

^a Department of Biotechnology   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BACTERIAL GENOME; GENETICS; LETHAL GENE; METABOLISM; SYSTEM ANALYSIS;

ALGORITHMS; GENES, LETHAL; GENOME, BACTERIAL; METABOLIC NETWORKS AND PATHWAYS; PROGRAMMING, LINEAR;

EID: 84947716970     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv352     Document Type: Article

References (33)

1. Acuna, V. et al. (2009). Modes and cuts in metabolic networks: complexity and algorithms. Biosystems, 95, 51-60.
2. Alper, H. et al. (2005). Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng., 7, 155-164.
3. Behre, J. et al. (2008). Structural robustness of metabolic networks with respect to multiple knockouts. J. Theor. Biol., 252, 433-441.
4. Chindelevitch, L. et al. (2014). An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models. Nature Commun., 5, 4893.
5. Deutscher, D. et al. (2006). Multiple knockout analysis of genetic robustness in the yeast metabolic network. Nat. Genet., 38, 993-998.
6. Deutscher, D, et al. (2008). Can single knockouts accurately single out gene functions BMC Syst. Biol., 2, 50.
7. Edwards, J.S., Palsson, B.O. (2000). Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics, 1, 1.
8. Famili, I. et al. (2003). Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc. Natl. Acad. Sci. U.S.A, 100, 13134-13139.
9. Fang, X. et al. (2010). Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis. BMC Syst. Biol., 4, 160.
10. Feist, A.M. et al. (2007). A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol., 3, 121.
11. Gü ell, O. et al. (2014). Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis. PLoS Comput. Biol., 10, e1003637.
12. Harrison, R. et al. (2007). Plasticity of genetic interactions in metabolic networks of yeast. Proc. Natl. Acad. Sci. U.S.A, 104, 2307-2312.
13. Hartman, H.B. et al. (2014). Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation. Microbiology (Reading, England), 160, 1252-1266.
14. Henry, C.S. et al. (2009). Application of high-performance computing to the reconstruction, analysis, and optimization of genome-scale metabolic models. J. Phys.: Conf. Ser., 180, 012025.
15. Jamshidi, N., Palsson, B.O. (2007). Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst. Biol., 1, 26.
16. Kauffman, K.J. et al. (2003). Advances in flux balance analysis. Curr. Opin. Biotechnol., 14, 491-496.
17. Kim, H.U. et al. (2011). Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery. Mol. Syst. Biol., 7, 460.
18. Kuepfer, L. et al. (2005). Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome Res., 12, 1421-1430.
19. Lee, D.S. et al. (2009). Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets. J. Bacteriol., 191, 4015-4024.
20. Navid, A. (2011). Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets. Brief. Funct. Genomics., 10, 354-364.
21. Pinney, J.W. et al. (2007). Metabolic reconstruction and analysis for parasite genomes. Trends Parasitol., 23, 548-554.
22. Plata, G. et al. (2010). Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network. Mol. Syst. Biol., 6, 408.
23. Raghunathan, A. et al. (2009). Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction. BMC Syst. Biol., 3, 38.
24. Schellenberger, J. et al. (2011). Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc., 6, 1290-1307.
25. Sigurdsson, G. et al. (2012). A systems biology approach to drug targets in Pseudomonas aeruginosa biofilm. PLoS One, 7, 1-9.
26. Suthers, P.F. et al. (2009). Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol. Syst. Biol., 5, 301.
27. Thiele, I. et al. (2005). Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single-and double-deletion mutants. J. Bacteriol., 187, 5818-5830.
28. Thiele, I. et al. (2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst. Biol., 5, 8.
29. Varma, A., Palsson, B.O. (1994). Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microb., 60, 3724-3731.
30. von Kamp, A., Klamt, S. (2014). Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Comput. Biol., 10, e1003378.
31. Wodke, J.H. et al. (2013). Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling. Mol. Syst. Biol., 9, 653.
32. Zhang, C. et al. (2015). Logical transformation of genome scale metabolic models for gene level applications and analysis. Bioinformatics, 31, 2324-2331.
33. Zimmermann, G.R. et al. (2007). Multi-target therapeutics: when the whole is greater than the sum of the parts. Drug Discov. Today, 12, 34-42.