Transcript Genomics, Computing, Economics 10 AM Tue 20-Feb Harvard Biophysics 101 (MIT-OCW Health Sciences & Technology 508) http://openwetware.org/wiki/Harvard:Biophysics_101/2007

Genomics, Computing,
Economics
10 AM Tue 20-Feb
Harvard Biophysics 101
(MIT-OCW Health Sciences & Technology 508)
http://openwetware.org/wiki/Harvard:Biophysics_101/2007
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
Steady-state flux optima
Flux Balance Constraints:
x1
C
RC
RB
R
A
RA < 1 molecule/sec (external)
A
B
RA = RB (because no net increase)
x2
D
x1 + x2 < 1 (mass conservation)
RD
x1 >0
(positive rates)
x2
x2 > 0
Max Z=3 at (x2=1, x1=0)
Feasible flux
Z = 3RD + RC
distributions
(But what if we really wanted to
select for a fixed ratio of 3:1?)
x1
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
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
in silico cells
E. coli
Genes
695
Reactions
720
Metabolites 436
H. influenzae
362
488
343
H. pylori
268
444
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/
Where do the
Stochiometric
matrices (& kinetic
parameters) come
from?
EMP RBC, E.coli
KEGG, Ecocyc
Biomass Composition
ATP
coeff. in growth reaction
2
10
GLY
0
10
LEU
-2
10
-4
10
ACCOA
NADH
COA
-6
10
0
5
10
FAD
15
SUCCOA
20
25
metabolites
30
35
40
45
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
Predicted Fluxes
C009-limited
200
180
160
140
120
100
80
60
40
20
0
WT (LP)
9
10
1
2
6 17
1545
0
250
18
150
8
2
7
9
100
14
5
46
3
r=-0.06
p=6e-1
10
13
11
12
Predicted Fluxes
Predicted Fluxes
200
50
250
Dpyk (LP)
200
15
17
141311
312
r=0.91
p=8e-8
16
18
50
100
150
Experimental Fluxes
8
150
100
14
10
9 13
11
31 12
50
0
200
Dpyk (QP)
7
16
0
7
8
r=0.56
P=7e-3
16
15
62
5
4 18
17
1
-50
-50
0
50 100 150 200 250
Experimental Fluxes
-50
-50
0
50 100 150 200 250
Experimental Fluxes
Competitive growth data:
reproducibility
Correlation between two selection experiments
Badarinarayana, et al. Nature Biotech.19: 1060
Competitive growth data
On minimal media
negative
selection
FBA
LP
QP
MOMA
small
effect
C 2 p-values
Essential
Reduced growth
Non essential
142
46
299
80
24
119
62
22
180
-3
p = 4∙10
4x10
Essential
Reduced growth
Non essential
162
44
281
96
19
108
66
25
173
p = 10-5
Position effects
-3
1x10-5
Novel redundancies
Hypothesis: next optima are achieved by regulation of activities.
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.
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
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 Dec;38(12):1406-12.
Desai RP, Nielsen LK, Papoutsakis ET. Stoichiometric modeling of
Clostridium acetobutylicum fermentations with non-linear
constraints. J Biotechnol. 1999 May 28;71(1-3):191-205.