Group Study MAE 298
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This 5 page Class Notes was uploaded by Consuelo Herman DDS on Tuesday September 8, 2015. The Class Notes belongs to MAE 298 at University of California - Davis taught by Staff in Fall. Since its upload, it has received 21 views. For similar materials see /class/187472/mae-298-university-of-california-davis in Engineering Mechanical & Aero at University of California - Davis.
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Date Created: 09/08/15
Examples of Biological Networks n y M mm M10 in c What are appropriate network descriptions 4 t J m mama um mmquot s mm if as m 12000 random solutions correct spatial structure Most parameters can vary 10000 fold causing no changes W W m 39 Mutation selection balance n 2 ma Anianor pnstsnor posmcn mm How to integrate with evolutionary genetics 1 Data mining N 2 Wings k A V rmtminw a 4 a a 10 12 2a Number of proteinprotein interactions FIG l7Pmteindivergcncetnuntlxzrofsynonymuuwubsumtionspcr synonymouacodonJNp n negatively momma p n 10 P z 001 F Wm39 a mm or quot tigmllnni P 705 mm ALDll Immin reported in Brimplum 2 Analysis of natural variation Drosophila Yeast phenotypes ethanol tolerance growth rate fermentation rate sulfur dioxide multose sucrose aroma I 39 30 natural yeast genotypes in 4 reps with Agilent chips Factor Analysis for Expression Data Factor is a linear combination Measurements load on of measurements 7 l l factors In every genotype the value of the factor can be calculated and correlated with the trait value 9 genes with high loads 500 genotypes 9 30 Agilent chips 9 4 per genotype Exploratory Factor Analysis Does variation in TF expression level account for variation in expression oftargets ADR1 Transcription Factor 9 AaFiADRrAr rz CHAA CRZl CTHl DALE DALSO FAFl FHL1 FKHl FKHZ FZF1 GAT T3 3AM GlSI K2 STBS zTETZ STF l SUT39I SWI4 TBF T054 T058 TYE7 TYE7 UGA3WAR1 XBPl YAPl YAF l YAP Factors 6 how much variance is explained Correlations ADR1 X Factor 1 0581 P00008 ADR1 X Factor 2 0656 Plt00001 ADR1 X Factor non significant o ADR1 Transcription Factor 9 Which of the regulated genes are real Loading Gene Factor 1 Factor 2 ABF 17 7 AFTZ 4 54 CHA4 5 1 CRZ l 7 54 FZF1 50 10 GAT2 54 64 GAT3 46 1 GAT4 65 21 May we model in the same framework not a single connection but all of the network Confirmatory Factor Analysis Structural Equation 31 Exogenous variables 12 Endogenous parameters V4 I P4P353 V5 53 V3 E3 VB Xz Adrenaline V2 E2 W H IEVEI V7 p33p73 szgg Wine 3912 Hang lg p32 consumption aver PIUZ Vii 21 x ti
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