Dulbecco R. (1986) A turning point in cancer research: sequencing the human genome. Science 231:1055-6

1. Dulbecco R. (1986) A turning point in cancer research: sequencing the human genome. Science 231:1055-6 Trastuzumab[Herceptin], Imatinib[Gleevec] : Normal, sensitive, & resistant alleles 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. Wang Z, et al. 2004 Science 304:1164. Mutational analysis of the tyrosine phosphatome in colorectal cancers.

2. Top Pharmacogenomic tests Imatinib Cancer BCR-ABL Irinotecan Cancer UGT1A1 5Fluorouracil Cancer DPYD-TYMS Tamoxifen Cancer CYP2D6 Long-QT Cardiac Familion Mercaptopurine Cancer TPMT Clozapine Anti-psychotic HLA-DQB1 Abacavir HIV-AIDS HLA B5701 & 1502 Clopidogrel Anti-Clot CYP2C19 Warfarin Anti-Clot CYP2C9 & 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 RB x1 B RA A RC x2 2C

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

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

10. Dynamic mass balances on each metabolite Vtrans Vdeg Vsyn Vuse 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 vj is the jth reaction rate, b is the transport rate vector, Sij is the “Stoichiometric matrix” = moles of metabolite i produced in reaction j

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. Flux-Balance Analysis • 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).

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 Flux Balances: A: RA – x1 – x2 = 0 B: x1 – RB = 0 C: 2 x2 – RC = 0 Supply/load constraints: RA = 3 RB = 1 RB x1 B RA A RC x2 2C Equations: A: x1+x2 = 3 B: x1 = 1 C: 2 x2 – RC = 0

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

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 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 FluxC FluxB FluxA

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. Steady-state flux optima RC Flux Balance Constraints: RA< 1 molecule/sec (external) RA = RB(because no net increase) x1 + x2< 1 (mass conservation) x1 >0 (positive rates) x2 > 0 C x1 RB RA A B x2 D RD x2 Max Z=3 at (x2=1, x1=0) Feasible flux distributions Z = 3RD + RC (But what if we really wanted to select for a fixed ratio of 3:1?) x1

20. 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