1. 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.

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

3. Top Predictable & Actionable Adult Onset Variants Genes Disorders Treatments HFE Hemochromatosis Blood letting LQT1-12 Cardiac arrhythmia Beta-blockers MLH1/SH2,6 Colorectal cancer Polyp removal PMS1-2,APC " ProthrombinII Pulmonary embolism Warfarin FV-Leiden Deep Vein Thrombosis " MTHFR Pregnancy complication Monitoring BRCA1-2 Breast cancer Masectomy G6PDH Acute haemolysis Avoid sulfonamides antimalarials, aspirin http://www.theuniversityhospital.com/adultgenetics/

4. 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

5. 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).

6. Human metabolic Network Duarte et al. reconstruction of the human metabolic network based on genomic and bibliomic data. PNAS 2007 104:1777-82. 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. Mo ML, Palsson BØ. Understanding human metabolic physiology: a genome- to-systems approach. Trends Biotechnol. 2009 Jan;27(1):37-44. Jamshidi N, Palsson BØ. Systems biology of SNPs. Mol Syst Biol. 2006;2:38. Mo ML, Jamshidi N, Palsson BØ. A genome-scale, constraint-based approach to systems biology of human metabolism. Mol Biosyst. 2007 Sep;3(9):598-603

7. Reaction Stoichiometry A 2C B RCRC RBRB RARA x1x1 x2x2

8. Where do the Stochiometric matrices (& kinetic parameters) come from?

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

10. Dynamic mass balances on each metabolite Time derivatives of metabolite concentrations are linear combination of the reaction rates. The reaction rates are non- linear functions of the metabolite concentrations (typically from in vitro kinetics). Where v j is the jth reaction rate, b is the transport rate vector, S ij is the “Stoichiometric matrix” = moles of metabolite i produced in reaction j V syn V deg V trans V use

11. Flux-Balance Analysis Make simplifications based on the properties of the system. –Time constants for metabolic reactions are very fast (sec - min) compared to cell growth and culture fermentations (hrs) –There is not a net accumulation of metabolites in the cell over time. One may thus consider the steady-state approximation.

12. Removes the metabolite concentrations as a variable in the equation. Time is also not present in the equation. We are left with a simple matrix equation that contains: –Stoichiometry: known –Uptake rates, secretion rates, and requirements: known –Metabolic fluxes: Can be solved for! In the ODE cases before we already had fluxes (rate equations, but lacked C(t). Flux-Balance Analysis

13. Additional Constraints –Fluxes >= 0 (reversible = forward - reverse) –The flux level through certain reactions is known –Specific measurement – typically for uptake rxns –maximal values –uptake limitations due to diffusion constraints –maximal internal flux

14. Flux Balance Example A 2C B RCRC RBRB RARA x1x1 x2x2 Flux Balances: A: R A – x 1 – x 2 = 0 B: x 1 – R B = 0 C: 2 x 2 – R C = 0 Supply/load constraints: R A = 3 R B = 1 Equations: A: x 1 +x 2 = 3 B: x 1 = 1 C: 2 x 2 – R C = 0

15. FBA Example A 2C B 4 1 3 1 2

16. FBA Often, enough measurements of the metabolic fluxes cannot be made so that the remaining metabolic fluxes can be calculated. Now we have an underdetermined system –more fluxes to determine than mass balance constraints on the system –what can we do?

17. Incomplete Set of Metabolic Constraints Identify a specific point within the feasible set under any given condition Linear programming - Determine the optimal utilization of the metabolic network, subject to the physicochemical constraints, to maximize the growth of the cell Flux A Flux B Flux C Assumption: The cell has found the optimal solution by adjusting the system specific constraints (enzyme kinetics and gene regulation) through evolution and natural selection. Find the optimal solution by linear programming

18. Under-Determined System All real metabolic systems fall into this category, so far. Systems are moved into the other categories by measurement of fluxes and additional assumptions. Infinite feasible flux distributions, however, they fall into a solution space defined by the convex polyhedral cone. The actual flux distribution is determined by the cell's regulatory mechanisms. It absence of kinetic information, we can estimate the metabolic flux distribution by postulating objective functions(Z) that underlie the cell’s behavior. Within this framework, one can address questions related to the capabilities of metabolic networks to perform functions while constrained by stoichiometry, limited thermodynamic information (reversibility), and physicochemical constraints (ie. uptake rates)

19. FBA - Linear Program For growth, define a growth flux where a linear combination of monomer (M) fluxes reflects the known ratios (d) of the monomers in the final cell polymers. A linear programming finds a solution to the equations below, while minimizing an objective function (Z). Typically Z= growth (or production of a key compound). i reactions

20. 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?)

21. 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

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

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

24. Minimization of Metabolic Adjustment (MoMA)

25. Flux Data

26. 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

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

28. 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

29. 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.

30. .

31. .

32. 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

33. Reconstructing evolved strains

34. Prioritizing by odds ratio, actionability, FP consequences