Transcript Short version for Ann

Toward gene based crop
simulation models for use in
climate change studies
SM Welch, A Wilczek, L Burghardt, JL Roe,
B Moyer, R Petipas, M Cooper, J Schmitt,
S Das, P Koduru, X Cai
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Species & Changing Climate I
• General warming
advances spring &
retards fall, altering
the timing of many
life cycle events;
• Few timing changes
will be proportional;
• Prior inter-species
synchronies will be
broken and new
ones formed.
2
Species & Changing Climate II
Day Length
• Day length varies by
latitude in complex,
seasonal ways;
• Day length sensitivity
will vary by species;
• Effects may reinforce
or offset temperature
influences;
• Prior inter-species
synchronies will be
broken and new ones
formed.
16Dec
27Oct
7Sep
19Jul
30May
10Apr
20Feb
1Jan
0. 35
0. 4
0. 45
0. 5
0. 55
0. 6
0. 65
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Climate & Changing Species III
• Climate models take
plant physiology into
account;
• They allow the
distribution of plants
to vary according to
plant competition;
• But plant response to
the environment
remains unaltered;
• There is no genetic
change.
4
Modeling a single gene
Temperature
Controlled by levels of upstream
regulatory gene products
Amount of
M  gene product
at time t
Some fraction of M
degrades per unit time
Change in amount Influx amount Efflux amount
Rate 


unit time
unit time
unit time
5
Simplified Network Model
Photoperiod Pathway
Vernalization Pathway
Clock
Photoperiod Pathway
Vernalization Pathway
FRI
Clock
FRI
GI
FLC
GI
FLC
VIN3
VIN3
“Autonomous
Pathway”
“Autonomous
Pathway”
LD
LD
Photoreceptors
Photoreceptors
FVE
CO
CO
FT
FT
Floral
Commitment
Floral
Commitment
Switch
Switch
FVE
AP1
AP1
SOC1
SOC1
LFY
LFY
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Simplified Network Model
Photoperiod Pathway
Vernalization Pathway
Clock
Photoperiod Pathway
Vernalization Pathway
FRI
Clock
FRI
GI
FLC
GI
FLC
VIN3
VIN3
“Autonomous
Pathway”
“Autonomous
Pathway”
LD
LD
Photoreceptors
Photoreceptors
FVE
CO
CO
FT
FT
Floral
Commitment
Floral
Commitment
Switch
Switch
FVE
AP1
AP1
SOC1
SOC1
LFY
LFY
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Model fit to OsCO mRNA data
OsCO mRNA Expression Level
10
15 h
9h
8
6
4
2
0
0
10
20
30
40
50
60
Time (h)
Kojima et al. 2002
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Hd1 Exp. or Dev. Rate (Arbitrary units)
Decoding Development Rate
1.2
1
0.8
0.6
0.4
0.2
0
0
2
4
6
8
10
12
14
16
18
20
Photoperiod (h)
9
45
30
Ler
co-2
15
0
4
10
16
22
0.12
B
0.10
0.08
Ler
Field
0.06
co-2
0.04
Reanalyzed by S. Welch
Photoperiod (hrs)
1/TLN
Data on
Arabidopsis
thaliana
Total Leaf Number
A
Data from A. Giakountis and G. Coupland.
60
0.02
0.00
4
10
16
22
Photoperiod (hrs)
Wilczek, et al, 2009.
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Dev. Rate
Vern. Effect.
Assembling the Pieces:
Gene Meta-mechanism Models
Temperature
Effect Hrs. Vernalization


Norwich
30
Dev. Rate
20
10
0
Hour-by-hour
-10
100
300
Days
500
700
Temperature
Dev. Rate
Temperature (Deg. C)
40 A
Wilczek et al, Science, 13 Feb 2009
Photoperiod
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Accumulation to a Common
Threshold
Wilczek, et al, 2009.
Copyright restrictions may apply
Actual vs. Predicted Bolting
Dates
Wilczek, et al, 2009.
Copyright restrictions may apply
13
Sensitivity to Germination Timing
Copyright restrictions may apply
Wilczek, et al, 2009.
14
Two Questions
• Can the path from basic phenotype and
genomic data to meta-mechanisms
and/or the corresponding networks be
automated?
• Can incomplete/imperfect network
models predict crosses well enough to
enable “network assisted selection”
outperform traditional methods?
15
“Real” network
P
The method does not find just
one solution but rather a set of
plausible ones.
18
32
80
T
Bolting
Decision
Output
92
54
24
One solution
P
24
The solutions may add/omit real
genes, have them in the wrong
orders or with the wrong functions.
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Bolting
Decision
Output
92
T
Gene
Expression
Output
80
But how good are they??
0.016
140
Actual Gene Expression
Predicted Gene Expression
y = 0.955*x-4.09
0.014
R2 = 0.996
120
Gene Expression Level
Predicted Bolting Date
0.012
100
80
60
0.01
0.008
0.006
0.004
40
0.002
20
20
30
40
50
60
70
80
Actual Bolting Date
90
100
110
120
0
0
20
40
60
80
100
Time
120
140
160
180
200
Cai et al. Int. Jour. Bioinformatics Res. and Appl. (in press)
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Can network-assisted selection with approximate
networks outperform phenotype and marker assisted
selection based on the real network?
Average Over Multiple Runs
54
52
Average Bolting Time
50
48
• Perhaps so
•But example is limited
•Next step: Real data
46
44
42
normal
marker
network1
network2
40
38
36
0
1
2
3
4
Number of Generation
5
6
7
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Take-away Messages
• It is possible to quantify the combined effects of
individual pathways in complex natural settings
• There are significant opportunities to synergize
ecophysiological and gene network modeling to
describe gene meta-mechanisms
• Phenology gene meta-mechanisms seem likely
to be broadly applicable to across plant taxa.
• Gene meta-mechanisms may be machinelearnable and perhaps able to support efficient
new crop improvement strategies.
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Thanks
FIBR “Post bacs”: Lindsey
Albertson, J. Franklin Egan,
Laura Martin, Chris Muir,
Sheina Sim, Alexis Walker,
Jillian Anderson, Deren
Eaton, Robert Schaeffer
Clint Oakley
Cristina Lopez-Gallego (UNO),
Eric Von Wettberg
Rosie Dent, Lisa Mandle, Emily
Josephs
NSF FIBR PROGRAM
NSF FIBR collaborators:
Michael Purugganan, Ian
Ehrenreich, Yoshie Hanzawa,
Megan Hall, Kitty Engelmann,
Ana Caicedo, Christina Richards,
A. Stathos (NYU)
Rick Amasino, Chris Schwartz
(Wisc.)
C. Dean, Amy Strange, C. Lister
(JIC), H. Kuittinen O. Savolainen
(Oulu), G. Coupland, A.
Giakountis, M. Koornneef (MPI,
Cologne), M. Hoffmann (Martin
Luther U.), M. Blázquez
(Valencia), D. Weigel (MPI,
Tübingen)
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