dulbecco r. (1986) a turning point in cancer research: sequencing the human genome. science...
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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.
Top Pharmacogenomic tests
Imatinib Cancer BCR-ABLIrinotecan Cancer UGT1A15Fluorouracil Cancer DPYD-TYMSTamoxifen Cancer CYP2D6Long-QT Cardiac FamilionMercaptopurine Cancer TPMTClozapine Anti-psychotic HLA-DQB1Abacavir HIV-AIDS HLA B5701 & 1502Clopidogrel Anti-Clot CYP2C19Warfarin Anti-Clot CYP2C9 & VKCoR
Top Predictable & Actionable Adult Onset Variants
Genes Disorders TreatmentsHFE Hemochromatosis Blood letting LQT1-12 Cardiac arrhythmia Beta-blockersMLH1/SH2,6 Colorectal cancer Polyp removal PMS1-2,APC "ProthrombinII Pulmonary embolism Warfarin FV-Leiden Deep Vein Thrombosis "MTHFR Pregnancy complication MonitoringBRCA1-2 Breast cancer MasectomyG6PDH Acute haemolysis Avoid sulfonamides
antimalarials, aspirin
http://www.theuniversityhospital.com/adultgenetics/
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
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).
Human metabolic NetworkDuarte 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
Reaction Stoichiometry
A
2C
B
RC
RB
RA
x1
x2
12
11
111
C
B
ACBA RRRxx 21
Where do the Stochiometric
matrices (& kinetic parameters) come
from?
EMP RBC, E.coli KEGG, Ecocyc
Where do the Stochiometric
matrices (& kinetic parameters) come
from?
ijijtransusedegsyni bvSVVVV
dt
dX )()(
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 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
Vsyn Vdeg
Vtrans
Vuse
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.
0 bvSX
dt
d
• 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 AnalysisbvS
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
iii v
Flux Balance Example
A
2C
B
RC
RB
RA
x1
x2
Flux Balances:A: RA – x1 – x2 = 0B: x1 – RB = 0C: 2 x2 – RC = 0
Supply/load constraints:RA = 3RB = 1
Equations:A: x1+x2 = 3B: x1 = 1C: 2 x2 – RC = 0
0
1
3
12
1
11
2
1
CR
x
x
C
B
ACRxx 21
bvS
FBA Example
bSv
bvS1
4
2
1
0
1
3
122
011
010
2
1
2
1
C
C
R
x
x
R
x
x
bSv 1
A
2C
B
4
1
3 1
2
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?
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
FluxB
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
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)
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
biomassMd growthv
allMM
ii
iii
i
Xv
v
v
0
bvS
Steady-state flux optima
A BRA
x1
x2
RB
D
C
Feasible fluxdistributions
x1
x2
Max Z=3 at (x2=1, x1=0)
RC
RD
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
Z = 3RD + RC
(But what if we really wanted to select for a fixed ratio of 3:1?)
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
0 5 10 15 20 25 30 35 40 4510
-6
10-4
10-2
100
102
ACCOA
COA
ATP
FAD
GLY
NADH
LEU
SUCCOA
metabolites
coef
f. in
gro
wth
rea
ctio
nBiomass Composition
Flux ratios at each branch point yields optimal polymer composition for replication
x,y are two of the 100s of flux dimensions
Minimization of Metabolic Adjustment
(MoMA)
Flux Data
0 50 100 150 2000
20
40
60
80
100
120
140
160
180
200
1
2
3
456
78
9
10
11121314
15
16
17 18
-50 0 50 100 150 200 250-50
0
50
100
150
200
250
1
2
3456
78
910
11121314
1516
17
18
Experimental Fluxes
Pre
dic
ted
Flu
xes
-50 0 50 100 150 200 250-50
0
50
100
150
200
250
1
2
3
456
78
910
111213
14
15
16
1718
pyk (LP)
WT (LP)
Experimental Fluxes
Pre
dic
ted
Flu
xes
Experimental Fluxes
Pre
dic
ted
Flu
xes
pyk (QP)
=0.91p=8e-8
=-0.06p=6e-1
=0.56P=7e-3
C009-limited
Competitive growth data: reproducibility
Correlation between two selection experiments
Badarinarayana, et al. Nature Biotech.19: 1060
Essential 142 80 62Reduced growth 46 24 22
Non essential 299 119 180 p = 4∙10-3
Essential 162 96 66Reduced growth 44 19 25
Non essential 281 108 173 p = 10-5
MOMA
FBA
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
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.
.
.
Co-evolution of mutual biosensors/biosynthesissequenced 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
Reconstructing evolved strains
Prioritizing by odds ratio, actionability, FP consequences