Display options
Cite
Guthke R, Gerber S, Conrad T, et al. Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens. Front Microbiol. 2016;7:570doi: 10.3389/fmicb.2016.00570.
Guthke, R., Gerber, S., Conrad, T., Vlaic, S., Durmuş, S., Çakır, T., Sevilgen, F. E., Shelest, E., & Linde, J. (2016). Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens. Frontiers in microbiology, 7570. https://doi.org/10.3389/fmicb.2016.00570
Guthke, Reinhard, et al. "Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens." Frontiers in microbiology vol. 7 (2016): 570. doi: https://doi.org/10.3389/fmicb.2016.00570
Guthke R, Gerber S, Conrad T, Vlaic S, Durmuş S, Çakır T, Sevilgen FE, Shelest E, Linde J. Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens. Front Microbiol. 2016 Apr 22;7:570. doi: 10.3389/fmicb.2016.00570. eCollection 2016. PMID: 27148247; PMCID: PMC4840211.
Copy Download .nbib Format: NLM
Share it on

Front Microbiol. 2016 Apr 22;7:570. doi: 10.3389/fmicb.2016.00570. eCollection 2016.

Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens.

Frontiers in microbiology

Reinhard Guthke, Silvia Gerber, Theresia Conrad, Sebastian Vlaic, Saliha Durmuş, Tunahan Çakır, F E Sevilgen, Ekaterina Shelest, Jörg Linde

Affiliations

  1. Research Group Systems Biology and Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knoell Institute Jena, Germany.
  2. Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University Kocaeli, Turkey.
  3. Department of Computer Engineering, Gebze Technical University Kocaeli, Turkey.

PMID: 27148247 PMCID: PMC4840211 DOI: 10.3389/fmicb.2016.00570

Abstract

In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility to predict central (hub) genes and thereby indicate potential biomarkers and drug targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene regulatory interactions, which have to be validated experimentally. Due to the lack of sufficient quantity and quality of both experimental data and prior knowledge about regulator-target gene relations, the genome-wide modeling still remains problematic for fungal pathogens. While a first genome-wide GRN model has already been published for Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated in the present article. Based on this evaluation, opinions are drawn on future directions of GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is the experimental evidence, both used for inferring the networks (omics 'first-hand' data as well as literature data used as prior knowledge) and for validation and evaluation of the inferred network models.

Keywords: Aspergillus fumigatus; Candida albicans; genome-wide modeling; reverse engineering; text mining; transcription factor

References

  1. Nucleic Acids Res. 2014 Jan;42(Database issue):D705-10 - PubMed
  2. Nucleic Acids Res. 2014 Jan;42(Database issue):D717-25 - PubMed
  3. Nucleic Acids Res. 2003 Jan 1;31(1):248-50 - PubMed
  4. BMC Syst Biol. 2013 Jan 02;7:1 - PubMed
  5. Nucleic Acids Res. 2015 Jan;43(Database issue):D470-8 - PubMed
  6. Int J Med Microbiol. 2011 Jun;301(5):453-9 - PubMed
  7. Biosystems. 2009 Apr;96(1):86-103 - PubMed
  8. Nucleic Acids Res. 2012 Jan;40(Database issue):D700-5 - PubMed
  9. Front Microbiol. 2012 Mar 12;3:85 - PubMed
  10. Nucleic Acids Res. 2015 Jan;43(Database issue):D447-52 - PubMed
  11. Front Microbiol. 2015 Aug 04;6:764 - PubMed
  12. Nature. 2005 Dec 22;438(7071):1151-6 - PubMed
  13. BMC Syst Biol. 2015 Apr 24;9:19 - PubMed
  14. Bioinformatics. 2007 Jul 1;23(13):i41-8 - PubMed
  15. Nat Rev Genet. 2014 Feb;15(2):107-20 - PubMed
  16. Nat Rev Microbiol. 2013 Jan;11(1):21-32 - PubMed
  17. BMC Syst Biol. 2010 Nov 04;4:148 - PubMed
  18. J Mol Biol. 2006 Jun 30;360(1):213-27 - PubMed
  19. Crit Rev Microbiol. 2010;36(1):1-53 - PubMed
  20. Trends Microbiol. 2011 Oct;19(10):509-15 - PubMed
  21. Cell Microbiol. 2016 Jul;18(7):889-904 - PubMed
  22. PLoS One. 2015 Sep 10;10 (9):e0136932 - PubMed
  23. Front Microbiol. 2015 Feb 06;6:65 - PubMed
  24. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D436-41 - PubMed
  25. Bioinformatics. 2015 Feb 1;31(3):445-6 - PubMed
  26. EXCLI J. 2015 Mar 02;14:346-78 - PubMed
  27. BMC Syst Biol. 2012 Jan 19;6:6 - PubMed
  28. PLoS Genet. 2005 Sep;1(3):e39 - PubMed
  29. PLoS One. 2014 Mar 20;9(3):e92734 - PubMed
  30. Front Microbiol. 2015 Jun 30;6:625 - PubMed
  31. Science. 2012 May 11;336(6082):647 - PubMed
  32. IEEE Signal Process Mag. 2009 Jan 1;26(1):76-97 - PubMed
  33. Front Microbiol. 2015 May 05;6:411 - PubMed
  34. Bioinformatics. 2005 Apr 15;21(8):1626-34 - PubMed
  35. Nucleic Acids Res. 2013 Jan;41(Database issue):D991-5 - PubMed
  36. Sci Transl Med. 2012 Dec 19;4(165):165rv13 - PubMed
  37. Front Microbiol. 2015 Apr 09;6:235 - PubMed
  38. Eukaryot Cell. 2011 Jan;10(1):34-42 - PubMed
  39. Brief Funct Genomics. 2016 Feb 8;:null - PubMed
  40. Methods Mol Biol. 2016;1356:3-15 - PubMed
  41. Front Microbiol. 2012 Feb 16;3:51 - PubMed
  42. Front Microbiol. 2012 Apr 02;3:108 - PubMed
  43. Nat Biotechnol. 2005 May;23(5):554-5 - PubMed
  44. Brief Bioinform. 2008 Jul;9(4):326-32 - PubMed
  45. Drug Discov Today. 2006 Mar;11(5-6):220-7 - PubMed

Publication Types