1. But what if we test more than one locus? The future of genetic studies of complex human diseases. RefRef (Note above graphs are active spreadsheets -- just click) GRR = Genotypic relative risk

2. Why multiple genes? alleles? covariance cis & trans Haplotyping Multiple loci models (additive, multiplicative, mean…)

3. SNPs & Covariance in proteins ApoE e4 RRR e3 RCR e2 RCC Ancestral = Thr 61 Arg 112 Genotype frequencies Allelee2e3e4 10.0%e21.7%15.0%1.7%18.3% 75.0%e315.0%55.0%25.0%95.0% 15.0%e41.6%25.0%1.7%28.3% 100.0% 18.3%95.0%28.4%100.0% (61 112 158) Risk ratio: e2/3=0.5 ; e3/3=1; e3/4=2.5 ; e4/4=15

4. Covariance in RNA ref " 1 72

5. Covariance M ij =  fx i x j log 2 [fx i x j /(fx i fx j )] M=0 to 2 bits; x=base type x i x j see Durbin et al p. 266-8. D-stem anticodon TCTC 3’acc

6. Mutual Information ACUUAU M 1,6 =  = fAU log 2 [fAU/(fA*fU)]... CCUUAG x 1 x 6 GCUUGC =4*.25log 2 [.25/(.25*.25)]=2 UCUUGA i =1 j =6 M 1,2 = 4*.25log 2 [.25/(.25*1)]=0 M ij =  fx i x j log 2 [fx i x j /(fx i fx j )] M=0 to 2 bits; x=base type x i x j see Durbin et al p. 266-8. See Shannon entropy, multinomial GrendarGrendar

7. Dulbecco R. (1986) A turning point in cancer research: sequencing the human genome. Science 231:1055-6 Mutations G719S, L858R, Del746ELREA in red. EGFR Mutations in lung cancer: correlation with clinical response to Gefitinib [Iressa] therapy. Paez, … Meyerson (Apr 2004) Science 304: 1497 Lynch … Haber, (Apr 2004) New Engl J Med. 350:2129. Pao.. Mardis,Wilson,Varmus H, PNAS (Aug 2004) 101:13306-11. Trastuzumab[Herceptin], Imatinib[Gleevec] : Normal, sensitive, & resistant alleles Wang Z, et al. 2004 Science 304:1164. Mutational analysis of the tyrosine phosphatome in colorectal cancers.

8. Pharmacogenomic tests AbacavirHIV-AIDSHLA B5701 & 1502 WarfarinAnti-ClotCYP2C9 & VKCoR ImatinibCancerBCR-ABL IrinotecanCancerUGT1A1 5FluorouracilCancerDPYD-TYMS TamoxifenCancerCYP2D6 Long-QTCardiacFamilion MercaptopurineCancerTPMT Clozapine Anti-psychoticHLA-DQB1 ClopidogrelAnti-ClotCYP2C19

9. Nutrigenomics/pharmacogenomics Lactose intolerance: C/T(-13910) lactase persistence/non functions in vitro as a cis element 14kbp upstream enhancing the lactase promoter http://www.genecards.org/cgi-bin/carddisp.pl?gene=LCT

10. Nutrigenomics/pharmacogenomics Thiopurine methyltransferase (TPMT) metabolizes 6- mercaptopurine and azathiopurine, two drugs used in a range of indications, from childhood leukemia to autoimmune diseases CYP450 superfamily: CYP2D6 has over 75 known allelic variations, 30% of people in parts of East Africa have multiple copies of the gene, not be adequately treated with standard doses of drugs, e.g. codeine (activated by CYP2D6).

11. Human metabolic Network (Recon 1) Duarte et al. reconstruction of the human metabolic network based on genomic and bibliomic data. PNAS 2007 104:1777-82. E.coli: 1200 ORFs http://gcrg.ucsd.edu/organisms

12. Steady-state flux optima AB RARA x1x1 x2x2 RBRB D C Feasible flux distributions x1x1 x2x2 Max Z=3 at (x 2 =1, x 1 =0) RCRC RDRD Flux Balance Constraints: R A < 1 molecule/sec (external) R A = R B (because no net increase) x 1 + x 2 < 1 (mass conservation) x 1 >0 (positive rates) x 2 > 0 Z = 3R D + R C (But what if we really wanted to select for a fixed ratio of 3:1?)

13. Applicability of LP & FBA Stoichiometry is well-known Limited thermodynamic information is required –reversibility vs. irreversibility Experimental knowledge can be incorporated in to the problem formulation Linear optimization allows the identification of the reaction pathways used to fulfil the goals of the cell if it is operating in an optimal manner. The relative value of the metabolites can be determined Flux distribution for the production of a commercial metabolite can be identified. Genetic Engineering candidates

14. Precursors to cell growth How to define the growth function. –The biomass composition has been determined for several cells, E. coli and B. subtilis. This can be included in a complete metabolic network –When only the catabolic network is modeled, the biomass composition can be described as the 12 biosynthetic precursors and the energy and redox cofactors

15. in silico cells E. coliH. influenzaeH. pylori Genes 695362 268 Reactions 720488 444 Metabolites 436343 340 (of total genes 4300 1700 1800) Edwards, et al 2002. Genome-scale metabolic model of Helicobacter pylori 26695. J Bacteriol. 184(16):4582-93. Segre, et al, 2002 Analysis of optimality in natural and perturbed metabolic networks. PNAS 99: 15112-7. ( Minimization Of Metabolic Adjustment ) http://arep.med.harvard.edu/moma/

16. EMP RBC, E.coliRBCE.coli KEGG, Ecocyc Where do the Stochiometric matrices (& kinetic parameters) come from?

17. ACCOA COA ATP FAD GLY NADH LEU SUCCOA metabolites coeff. in growth reaction Biomass Composition

18. Flux ratios at each branch point yields optimal polymer composition for replication x,y are two of the 100s of flux dimensions

19. Minimization of Metabolic Adjustment (MoMA)

20. Flux Data

21. 050100150200 0 20 40 60 80 100 120 140 160 180 200 1 2 3 4 56 7 8 9 10 11 12 1314 15 16 1718 -50050100150200250 -50 0 50 100 150 200 250 1 2 3 4 56 7 8 9 10 11 12 1314 15 16 17 18 Experimental Fluxes Predicted Fluxes -50050100150200250 -50 0 50 100 150 200 250 1 2 3 4 56 7 8 9 10 11 12 13 14 15 16 1718  pyk (LP) WT (LP) Experimental Fluxes Predicted Fluxes Experimental Fluxes Predicted Fluxes  pyk (QP)  =0.91 p=8e-8  =-0.06 p=6e-1  =0.56 P=7e-3 C009-limited

22. Competitive growth data: reproducibility Correlation between two selection experiments Badarinarayana, et al. Nature Biotech.19: 1060

23. Competitive growth data  2 p-values 4x10 -3 1x10 -5 Position effects Novel redundancies On minimal media negative small selection effect Hypothesis: next optima are achieved by regulation of activities. LP QP

24. Co-evolution of mutual biosensors/biosynthesis sequenced across time & within each time-point Independent lines of Trp  & Tyr  co-culture 5 OmpF: (pore: large,hydrophilic > small) 42R-> G,L,C, 113 D->V, 117 E->A 2 Promoter: (cis-regulator) -12A->C, -35 C->A 5 Lrp: (trans-regulator) 1b , 9b , 8b , IS2 insert, R->L in DBD. Heterogeneity within each time-point. Reppas, Shendure, Porecca

25. Reconstructing evolved strains

26. Non-optimal evolves to optimal Ibarra et al. Nature. 2002 Nov 14;420(6912):186-9. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

27. Metabolic optimization readings Duarte et al. reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A. 2007 Feb 6;104(6):1777-82. Joyce AR, Palsson BO. Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach. Prog Drug Res. 2007;64:265, 267-309. Review. Herring, et al. Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nat Genet. 2006 38:1406-12. Desai RP, Nielsen LK, Papoutsakis ET. Stoichiometric modeling of Clostridium acetobutylicum fermentations with non-linear constraints. J Biotechnol. 1999 71:191-205.

28. .