title: Modeling Gene Networks in Saccharomyces cerevisiae Based on Gene Expression Profiles.
abstract: Detailed and innovative analysis of gene regulatory network structures may reveal novel insights to biological mechanisms. Here we study how gene regulatory network in Saccharomyces cerevisiae can differ under aerobic and anaerobic conditions. To achieve this, we discretized the gene expression profiles and calculated the self-entropy of down- and upregulation of gene expression as well as joint entropy. Based on these quantities the uncertainty coefficient was calculated for each gene triplet, following which, separate gene logic networks were constructed for the aerobic and anaerobic conditions. Four structural parameters such as average degree, average clustering coefficient, average shortest path, and average betweenness were used to compare the structure of the corresponding aerobic and anaerobic logic networks. Five genes were identified to be putative key components of the two energy metabolisms. Furthermore, community analysis using the Newman fast algorithm revealed two significant communities for the aerobic but only one for the anaerobic network. David Gene Functional Classification suggests that, under aerobic conditions, one such community reflects the cell cycle and cell replication, while the other one is linked to the mitochondrial respiratory chain function.
authors.affiliation: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong 266590, China.
authors.forename: Yulin
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China.
authors.forename: Kebo
authors.initials: K
authors.lastname: Lv
authors.affiliation: College of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, China.
authors.forename: Shudong
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.
authors.forename: Jionglong
authors.initials: J
authors.lastname: Su
authors.affiliation: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong 266590, China.
authors.forename: Dazhi
authors.initials: D
authors.lastname: Meng
mesh_terms: D000332:Aerobiosis / Q000235:genetics; D000465:Algorithms; D000693:Anaerobiosis / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D004734:Energy Metabolism / Q000235:genetics; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D016015:Logistic Models; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism
references.citation: J Biol. 2005;4(2):6
references.pmid: 15982408
references.citation: Biochem Soc Trans. 2003 Dec;31(Pt 6):1516-8
references.pmid: 14641102
references.citation: Lett Appl Microbiol. 2009 Jul;49(1):14-9
references.pmid: 19413773
references.citation: Nature. 2005 Jun 9;435(7043):814-8
references.pmid: 15944704
references.citation: PLoS Comput Biol. 2007 Apr 27;3(4):e43
references.pmid: 17465672
references.citation: BMC Syst Biol. 2009;3:115
references.pmid: 20003439
references.citation: Nat Genet. 2007 Mar;39(3):415-21
references.pmid: 17277776
references.citation: BMC Syst Biol. 2012;6:113
references.pmid: 22929591
references.citation: Nat Genet. 2007 May;39(5):683-7
references.pmid: 17417638
references.citation: Bioinformatics. 2002 Feb;18(2):261-74
references.pmid: 11847074
references.citation: FEBS J. 2005 Oct;272(20):5110-8
references.pmid: 16218945
references.citation: EMBO J. 1995 Aug 15;14(16):4031-43
references.pmid: 7664742
references.citation: ScientificWorldJournal. 2014;2014:968341
references.pmid: 24587766
references.citation: Proc Natl Acad Sci U S A. 2002 Jun 11;99(12):7821-6
references.pmid: 12060727
references.citation: Brief Bioinform. 2003 Sep;4(3):228-35
references.pmid: 14582517
references.citation: J Comput Biol. 2000;7(3-4):601-20
references.pmid: 11108481
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Jun;69(6 Pt 2):066133
references.pmid: 15244693
references.citation: Yeast. 2005 Dec;22(16):1257-68
references.pmid: 16358322
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Feb;69(2 Pt 2):026113
references.pmid: 14995526
references.citation: J R Soc Interface. 2008 Aug 6;5 Suppl 1:S85-94
references.pmid: 18508746
references.citation: Bioinformatics. 2006 Oct 15;22(20):2523-31
references.pmid: 16844710
references.citation: Mol Microbiol. 2002 Sep;45(5):1433-41
references.pmid: 12207708
references.citation: Appl Environ Microbiol. 2005 Nov;71(11):6465-72
references.pmid: 16269670
references.citation: IEEE Trans Biomed Circuits Syst. 2014 Feb;8(1):42-53
references.pmid: 24681918
references.citation: Bioinformatics. 2005 Sep 1;21 Suppl 2:ii206-12
references.pmid: 16204105
references.citation: PLoS One. 2007;2(10):e988
references.pmid: 17912365
references.citation: Bioinformatics. 2002 Oct;18(10):1319-31
references.pmid: 12376376
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Jul;64(1 Pt 2):016132
references.pmid: 11461356
references.citation: J Biol Chem. 1994 Oct 21;269(42):26144-51
references.pmid: 7929327
references.citation: Mol Microbiol. 1997 Nov;26(3):481-91
references.pmid: 9402019
references.citation: Genome Biol. 2007;8(9):R183
references.pmid: 17784955