Toward gene based crop simulation models for use in

climate change studies

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 ISpecies & Changing Climate I

0

4 0 0

8 0 0

1 2 0 0

1 6 0 0

2 0 0 0

2 4 0 0

1 - A p r 1 - M a y 3 1 - M a y 3 0 - J u n 3 0 - J u l 2 9 - A u g 2 8 - S e p

Cum. De

g. Days

• 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.

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• 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.

Day Length

1-Jan

20-Feb

10-Apr

30-May

19-Jul

7-Sep

27-Oct

16-Dec

0.35 0.4 0.45 0.5 0.55 0.6 0.65

Species & Changing Climate IISpecies & Changing Climate II

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• 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.

Climate & Changing Species IIIClimate & Changing Species III

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Modeling a single geneModeling a single gene

M Amount of gene product at time t

Controlled by levels of upstream regulatory gene products

Some fraction of M degrades per unit time

Temperature

Change in amount Influx amount Efflux amountRate

unit time unit time unit time

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Simplified Network Model Simplified Network Model

SOC1

LFY

FT

“Autonomous Pathway”

Floral Commitment Switch AP1

FLC

Vernalization Pathway

FRI VIN3

CO

Clock

Photoreceptors

Photoperiod Pathway

GI

FVELD

SOC1

LFY

FT

“Autonomous Pathway”

Floral Commitment Switch AP1

FLC

Vernalization Pathway

FRI VIN3

CO

Clock

Photoreceptors

Photoperiod Pathway

GI

FVELD

SOC1

LFY

FT

“Autonomous Pathway”

Floral Commitment Switch AP1

FLCFLC

Vernalization Pathway

FRI VIN3

CO

Clock

PhotoreceptorsPhotoreceptors

Photoperiod Pathway

GIGI

FVELD

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Simplified Network Model Simplified Network Model

SOC1

LFY

FT

“Autonomous Pathway”

Floral Commitment Switch AP1

FLC

Vernalization Pathway

FRI VIN3

CO

Clock

Photoreceptors

Photoperiod Pathway

GI

FVELD

SOC1

LFY

FT

“Autonomous Pathway”

Floral Commitment Switch AP1

FLC

Vernalization Pathway

FRI VIN3

CO

Clock

Photoreceptors

Photoperiod Pathway

GI

FVELD

SOC1

LFY

FT

“Autonomous Pathway”

Floral Commitment Switch AP1

FLCFLC

Vernalization Pathway

FRI VIN3

CO

Clock

PhotoreceptorsPhotoreceptors

Photoperiod Pathway

GIGI

FVELD

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Model fit to OsCO mRNA dataModel fit to OsCO mRNA data

15 h9 h

0

2

4

6

8

10

0 10 20 30 40 50 60

Time (h)

OsC

O m

RN

A E

xpre

ssio

n L

evel

15 h9 h

Kojima et al. 2002

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Decoding Development RateDecoding Development Rate

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12 14 16 18 20

Photoperiod (h)

Hd

1 E

xp.

or

Dev

. R

ate

(Arb

itra

ry u

nit

s)

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Data on Arabidopsis

thaliana

Data on Arabidopsis

thaliana

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15

30

45

60

4 10 16 22

Photoperiod (hrs)

To

tal

Lea

f N

um

ber

Ler

co-2

0.00

0.02

0.04

0.06

0.08

0.10

0.12

4 10 16 22

Photoperiod (hrs)

1/T

LN

Ler

Field

co-2

B

A

Rean

aly

zed

by S

. W

elc

h

Data

fro

m A

. G

iako

un

tis

an

d G

. C

ou

pla

nd

.

Wilczek, et al, 2009.

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Assembling the Pieces:Gene Meta-mechanism Models

Assembling the Pieces:Gene Meta-mechanism Models

Dev

. Rat

e

Temperature

PhotoperiodD

ev. R

ate

Dev

. Rat

e

Effect Hrs. VernalizationTemperature

Ver

n. E

ffect

.

-10

0

10

20

30

40

100 300 500 700

Tem

per

atu

re (

Deg

. C

)

A Norwich

Hour-by-hour

Days

Wilczek et al, Science, 13 Feb 2009

Accumulation to a Common Threshold

Wilczek, et al, 2009.

Copyright restrictions may apply

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Actual vs. Predicted Bolting Dates

Actual vs. Predicted Bolting Dates

Copyright restrictions may apply

Wilczek, et al, 2009.

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Sensitivity to Germination TimingSensitivity to Germination Timing

Wilczek, et al, 2009.Copyright restrictions may apply

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Two QuestionsTwo 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?

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P

T

18

24

32

54

80 92Bolting

DecisionOutput

P

T

18

24

32

54

80 92Bolting

DecisionOutput

P

T

24

80

18 92Bolting

DecisionOutput

Gene Expression

Output

P

T

24

80

18 92Bolting

DecisionOutput

Gene Expression

Output

20 30 40 50 60 70 80 90 100 110 12020

40

60

80

100

120

140

Actual Bolting Date

Pre

dict

ed B

oltin

g D

ate

y = 0.955*x-4.09

R2 = 0.996

0 20 40 60 80 100 120 140 160 180 2000

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

Time

Gen

e E

xpre

ssio

n Le

vel

Actual Gene Expression

Predicted Gene Expression

“Real” network

One solution

The method does not find just one solution but rather a set of plausible ones.

The solutions may add/omit real genes, have them in the wrong orders or with the wrong functions.

But how good are they??

Cai et al. Int. Jour. Bioinformatics Res. and Appl. (in press)

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0 1 2 3 4 5 6 736

38

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54

Number of Generation

Ave

rage

Bol

ting

Tim

eAverage Over Multiple Runs

normal

markernetwork1

network2

Can network-assisted selection with approximate networks outperform phenotype and marker assisted selection based on the real network?

Can network-assisted selection with approximate networks outperform phenotype and marker assisted selection based on the real network?

• Perhaps so•But example is limited•Next step: Real data

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Take-away MessagesTake-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 machine-learnable and perhaps able to support efficient new crop improvement strategies.

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ThanksThanksFIBR “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)