Genomics, Computing, Economics

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Genomics, Computing, Economics
10 AM Tue 20-Feb
Harvard Biophysics 101  (MIT-OCW Health Sciences
Technology 508) http//openwetware.org/wiki/Har
vardBiophysics_101/2007
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Class outline
(1) Topic priorities for homework since last
class (2) Quantitative exercises
psycho-statistics, combinatorials,
exponential/logistic, bits, association
multi-hypotheses, FBA (3) Project level
presentation discussion Personalized
Medicine Energy Metabolism (4) Discuss
communication/presentation tools (5) Topic
priorities for homework for next class
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Steady-state flux optima
RC
Flux Balance Constraints RA lt 1 molecule/sec
(external) RA RB (because no net
increase) x1 x2 lt 1 (mass conservation) x1 gt0
(positive rates) x2 gt 0
C
x1
RB
RA
A
B
x2
D
RD
x2
Max Z3 at (x21, x10)
Feasible flux distributions
Z 3RD RC (But what if we really
wanted to select for a fixed ratio of 31?)
x1
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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

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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

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in silico cells
E. coli H. influenzae H. pylori Genes
695 362 268 Reactions 720 488
444 Metabolites 436 343
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/
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Where do the Stochiometric matrices ( kinetic
parameters) come from?
EMP RBC, E.coli KEGG, Ecocyc
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Biomass Composition
ATP
GLY
LEU
coeff. in growth reaction
ACCOA
NADH
FAD
SUCCOA
COA
metabolites
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Flux ratios at each branch point yields optimal
polymer composition for replication
x,y are two of the 100s of flux dimensions
10
Minimization of Metabolic Adjustment (MoMA)
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Flux Data
12
C009-limited
200
WT (LP)
180
7
8
160
140
9
120
10
Predicted Fluxes
100
r0.91 p8e-8
11
13
14
12
3
1
80
60
40
16
20
2
5
6
4
15
17
18
0
0
50
100
150
200
Experimental Fluxes
250
250
Dpyk (LP)
Dpyk (QP)
200
200
18
7
r0.56 P7e-3
8
150
r-0.06 p6e-1
150
7
8
2
Predicted Fluxes
Predicted Fluxes
10
100
9
13
100
9
11
12
3
1
14
10
11
13
14
12
3
50
50
5
6
4
16
16
2
15
5
6
0
15
17
0
17
18
4
1
-50
-50
-50
0
50
100
150
200
250
-50
0
50
100
150
200
250
Experimental Fluxes
Experimental Fluxes
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Competitive growth data reproducibility
Correlation between two selection experiments
Badarinarayana, et al. Nature Biotech.19 1060
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Competitive growth data
On minimal media
negative small

selection effect
C 2 p-values 4x10-3 1x10-5
LP QP
Novel redundancies
Position effects
Hypothesis next optima are achieved by
regulation of activities.
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Non-optimal evolves to optimal
Ibarra et al. Nature. 2002 Nov
14420(6912)186-9. Escherichia coli K-12
undergoes adaptive evolution to achieve in silico
predicted optimal growth.
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Further 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
6104(6)1777-82. Joyce AR, Palsson BO. Toward
whole cell modeling and simulation comprehensive
functional genomics through the constraint-based
approach. Prog Drug Res. 200764265, 267-309.
Review. Herring, et al. Comparative genome
sequencing of Escherichia coli allows observation
of bacterial evolution on a laboratory timescale.
Nat Genet. 2006 Dec38(12)1406-12. Desai RP,
Nielsen LK, Papoutsakis ET. Stoichiometric
modeling of Clostridium acetobutylicum
fermentations with non-linear constraints. J
Biotechnol. 1999 May 2871(1-3)191-205.