Use of ecophysiological approaches and biophysic plant modelling

Use of ecophysiological approaches and biophysic plant modelling

in determination of complex phenotypic traits and analysis of interactions GxE

Pr. Jérémie LECOEURProfessor of Plant BiologyDirector of Plant Science DepartmentMontpellier SupAgro

1. Context

Context : a need to understand the building of the plant phenotype

The plant phenotype is always a complex object resulting from the spatial and temporal integration of various biological processes « Integrated Plant Phenotype »

Integrated plant phenotype: Plant traits resulting of the integration of the major plant functions in response to environment.

An example of an « Integrated Plant Phenotype »:

The architecture of the At rosette

Corresponding Virtual plantPicture

Col

se

rot

This integrated phenotype results from:• organogenesis• morphogenesis• carbon metabolism…

in interaction with the environment

x

= Phenotype Genotype Environment Responses x

=

=

Re

spo

nse

Environment

genotype 1

genotype 2

=


The plant phenotype is always a complex object resulting from the spatial and temporal integration of various biological processes « Integrated Plant Phenotype »

Integrated plant phenotype: Plant traits resulting of the integration of the major plant functions in response to environment.

Choice of the plant representation

Process based models (crop models)

Leaves

fruits

roots

Genetic modelling

phenotype = G + E + GxE +

Mainly statistical approaches

Ecophysiological modelling

Organ populations in relation with environment through correlative relationships

= Re

spo

nse

Environment

géno 1

géno 2

« Virtual plants »

Set of phytomeres with topological connections with matter flows


The plant is a complex system = a large number of sub-units with the same organisation and topological connection resulting in a network

The same level of complexity could be find at organelle, cell, tissue…

Cell protein tree

(d’après Jeong, 2003)


Purslane plant

A postulate ?«The only way to make significant progress in understanding the genotype - environment interaction is to associate several scientific disciplines»

The needed scientific disciplines would be:- genetic and genomic,- plant biology and plant physiology, - ecophysiology and biophysic- applied mathematics,

Theory of the increase in scientific progress through combinatories of conceptual and technic artefacts (Lebeau, 2005)


2. Advances in Ecophysiology

Step 0 : Characterization of the physical environment at plant boundaries

The absolute necessary to take into account the physical environment

Systematic characterization of plant microclimate

Advances in Ecophysiology

To allow the comparison between experiments and the establishment of trial network typologies or a future use of models

In field

In growth chamber

The minimum data set includes temperature, radiation and atmospheric humidity, wind speed and rainfall

First use of modelling: to estimate the environmental variables instead of measuring them.

To model the energy, radiative and water balances….

Reference height

Capitulum height

Leaves canopysource height

Ta,0

Ta,1

Ta,2

ea,0

ea,1

ea,2TL,2

gH,1,1

gH,2

ga,1

ga,0

gH,1,2TL,1,2 e*(TL,1,2)

ga,1

ga,0

e*(TL,1,1)

gv,1,2

gv,2

gv,1,1

layer 1

layer 2

TL,1,1

e*(TL,2)

kc,1

(from Rey, 2003; Lhomme and Guilioni, 2004 and 2006; Chenu et al., 2005 and 2007; Louarn et al., 2007)

To be as close as possible to the microclimate sensed by the plant or by its organs


To identify the environmental variables quantitatively related to plant development and growth.

For instance, what is the radiative variable well related to the organogenesis on At?

Incident PAR Light quality(R/FR - Blue)

Absorbed PAR


(from Chenu et al., 2005)

Ph

yto

me

re p

rod

uct

ion

ra

te (

CD

D-1

)

Incident PAR (mol m-2 d-1) Absorbed PAR (mmol plt-1 d-1) Absorbed PAR (log scale)




A lot can be done by using standard bioclimatological indicators…

Thermal time,Cumulative solar radiation,Photothermal coefficient,Climatic water balance…

Step 1 : Ecophysiologic diagnosis of the phenotypic variability

To dissect the genotype – environment interaction

Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes

(from Chenu et al, 2007)

Analysis of a panel of wild types and their mutants in At


Col

ronse

rot

3.5

Ws Ler Dij

Wild type

mutants


Col

0.00

0.05

0.10

0.15

se

0.00

0.05

0.10

0.001 0.01 0.1 1 100.00

0.05

0.10

Ws

3.5

0.01 0.1 1 10

Ler

ron

0.01 0.1 1 10

Dij

0.01 0.1 1 10 0.001 0.01 0.1 1 100.00

0.05

0.10

0.15

Col / se / rot

0.001 0.01 0.1 1 100.00

0.05

0.10

0.15Ws / 3.5

Absorbed PAR (mmol plte-1 j-1) [log scale]

0.01 0.1 1 10

Ler / ron

0.01 0.1 1 10

Génotypes

0.001 0.01 0.1 1 100.00

0.05

0.10

0.15

All wild type

All genotypesComparison wild types vs corresponding mutants

(from Chenu et al, 2007)


FTSW

0.0 0.2 0.4 0.6 0.8 1.0Vite

sse

re

lativ

e d

'exp

an

sio

n

0.0

0.2

0.4

0.6

0.8

1.0

TempératureDéficit hydrique

édaphiqueRayonnement

absorbé

PARa (m-2 mol j-1)

0 10 20 30 40Vite

sse

re

lativ

e d

'exp

an

sio

n

0.0

0.2

0.4

0.6

0.8

1.0

Température des feuilles (°c)

0 10 20 30 40Vite

sse

re

lativ

e d

'exp

an

sio

n

0.0

0.2

0.4

0.6

0.8

1.0

Pois

FTSW

0.0 0.2 0.4 0.6 0.8 1.0Vite

sse

re

lativ

e d

'exp

an

sio

n

0.0

0.2

0.4

0.6

0.8

1.0

Tournesol

Vigne

Laitue

Arabidopsisthaliana

Haricot

FTSW

0.0 0.2 0.4 0.6 0.8 1.0Vite

sse

re

lativ

e d

'exp

an

sio

n

0.0

0.2

0.4

0.6

0.8

1.0

FTSW

0.0 0.2 0.4 0.6 0.8 1.0Vite

sse

re

lativ

e d

'exp

an

sio

n

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

PARa (mol j-1)

0.1 0.2 0.3 0.4

RE

R (

mm

2 m

m-2

°C

j-1)

0.00

0.02

0.04

0.06

PARa (mol j-1)

0.0000.0010.0020.0030.0040.0050.006

RE

R (

mm

2 m

m-2

°C

j-1)

0.030

0.035

0.040

0.045

0.050

FTSW

0.0 0.2 0.4 0.6 0.8 1.0Vite

sse

re

lativ

e d

'exp

an

sio

n0.0

0.2

0.4

0.6

0.8

1.0

a

b

c d

e

f g

h

i

Response curve families

For instance, leaf expansion…

Establishment of consistent relatioship betwen plant and

environment variables

(from Chenu et al., 2007)

Vini = aini log(PARa) + bini

G GG x E

ColumbiaSerrate

This approach allowed to identify a new involvement of the Serrate gene in plant organogenesis.



Time consuming ecophysiological measurements require « industrial phenotyping » or a large field trail network

It will be necessary to increase by 10 to 100 the number of characterized experimental situations

(From Joined Unit LEPSE – INRA / SupAgro, 2006 report)


Step 2 : To quantify the impact of the observed phenotypic differences

Third use of modelling: to analyse the consequences of multi-trait differences on integrated plant phenotypes

The sensitivity analyses allow to rank the traits in term of their quantitative effects on the integrated phenotype.

An example: phenotypic variability in light interception in sunflower during seed development.

Among a panel of 20 genotypes, the following phenotypic differences were observed:

- plant leaf area,- individual leaf area,- leaf number,- leaf size distribution along the stem,- blade angle,- duration of leaf life.


Virtual sensitivity analysis of light interception to various phenotypic traits

Average virtual plant

Changes in position of the largest leaf on

the stem

Changes in plant leaf

area

Changes in leaf number

(from Casadebaig, 2004)



Virtual plot at flowering (6.6 plants m-2

cv Heliasol)

Sunflower virtual plantcv Heliasol

Estimation of light interception

Days0.0

0.2

0.4

0.6

0.8

1.0Fra

cti

on

of

rad

iati

on

in

terc

ep

ted

Eii

(from Rey, 2003; Casadebaig, 2004)

-400 -200 0 200 400

50

10

01

50

20

0

Evaluated ranges of variation in observed traits (in % of the average value)

Ch

an

ges in

lig

ht

inte

rcep

tion

(in %

of

avera

ge p

lant)

Plant leaf area

Leaf number

Position of the largest leaf on the stem

Plant heigth

Duration of leaf life

Blade angle

Sensitivity analysis

A hidden trait affecting the light interception was identified: the distribution of leaf sizes along the stem

(from Casadebaig, 2004)



(adapted from Chenu et al., 2005)

Emerging properties at plant level in At?

The changes in organogenesis, organ expansion and morphology lead to unexpected property: the life irradiance is improved in response to reductions in incident light


0 200 400 600 800 1000 12000.00

0.05

0.10

0.15

0.20

0.25

0 200 400 600 800 1000 1200

0.00

0.05

0.10

0.15

0.20

0.25

Q/D

ratio

(arb

itrar

y un

its)

0C 6C

Thermal time from budburst (°Cd)

'GRENACHE N'

'SYRAH'

A B

C D

• 3 phases

1

1- decrease in trophic competition due to the increase in sources

1

1 12- Increase in trophic competition due to rapid production of new sinks

2

2 2

2

3-(0C)- Decreasein trophic competition due to the end of secondary axes development

3a

3a

3-(6C)- Increase trophic competition due the second growth phasis of clusters

3b

3b

Change with time in trophic competition inside the grapevine shoot

F

F V

V

Relationship between axis development and trophic competition

0.00 0.05 0.10 0.15 0.20 0.250.0

0.2

0.4

0.6

0.8

1.0

0.00 0.05 0.10 0.15 0.20 0.250.00 0.05 0.10 0.15 0.20 0.25

Sigmoidial adjustmentSyr 0CSyr 6C Gre 0CGre 6C

Q/D ratio (arbitrary units)Pro

babili

ty t

o m

ain

tain

the

develo

pm

ent

Primary axes P0 secondary axes P1- P2 secondary axes

A B C

Relationship between Q/D values and the probability of end of secondary axes development

• Primary axes are not affected by the trophic competition

• Secondary axis are affected by the trophic competition

• A single sigmoidal relationship P=f(Q/D).

• A difference in sensitivity according to the type of axes 0.00 0.05 0.10 0.15 0.20 0.250.0

0.2

0.4

0.6

0.8

1.0

Primary axis P0 secondary axisP1-P2 secondary axis

0.00 0.05 0.10 0.15 0.20 0.250.0

0.2

0.4

0.6

0.8

1.00.0

0.2

0.4

0.6

0.8

1.00.0

0.2

0.4

0.6

0.8

1.0

P0 secondary axesP1-P2 secondary axesP1-P2 sigmoid adjustmentP0 sigmoid adjustment

Pro

babili

ty t

o m

ain

tain

the d

evelo

pm

ent

Q/D ratio (arbitrary units)

A1-5

B6-10

C11-16

Relationship between Q/D values and the probability of end of secondary axes development according to their type and size

1-5 leaves

(0.31g)

6-10 leaves (2.87g)

11-16 leaves

(10.21g)

Relationship between axis development and trophic competition

3. The front of « modelling experiences »

Step 3 : To model the impact of genotypic variability on the plant phenotypic

plasticity

To associate various kind of models to predict the integrated plant phenotypes

The front of modelling experiences

To evaluate the genotype performances

The biophysical modelling approaches are now enough tried and tested to be revisited to predict the genotype – environment interaction.

The available modelling approaches (not exhaustive):- biophysical balances, - crop models,- ecophysiological descriptions of regulations and signals in

plants,- 3D architectural plant and canopy models,- mathematical models to estimate parameters in complex

systems…

D o n n é e s c l i m a t i q u e sT m o y T m i nT m a x P A R i

T T > T T _ M 3 g e n

F i n

N o m b r e d e f e u i l l e s à f i n e x p a n s i o n

N F < N F f i n a l g e n N F = P h y l l o c h r o n e g e n x T T j

N F = N F f i n a l g e n

P h é n o l o g i eT T _ E 1 g e n T T _ F 1 g e nT T _ M 0 g r n T T _ M 3 g e n

T e m p s t h e r m i q u e d e p u i s l a l e v é e

it

levée

i Tb a s e )d t(Tmo yTT

o u i

I n d i c e f o l i a i r eL A I = d e n s x S F p l a n t e

E f f i c i e n c e d ’ i n t e r c e p t i o n

i = 1 – e x p ( - k g e n x L A I i )

R a n g d e l a d e r n i è r e f e u i l l e m o r t e

T T i > T T _ M 0 g e n

E f f i c i e n c e b i o l o g i q u e p o t e n t i e l l e

T T i < T T _ E 1 g e n , b p o t i = 1

T T i < T T _ F 1 g e n , b p o t i =

T T i < T T _ M 0 g e n , b p o t i = b g e n

T T i < T T _ M 3 g e n , b p o t i = b g e n x

B i o m a s s e a é r i e n n e t o t a l eM S i = M S i - 1 + d M S i

S u r f a c e f o l i a i r e d e l a p l a n t e p r o d u i t e

NFj

0j

gen

gengen

gen d j)

a SFjb SFc SFe x p (41

a SFro d u i teSF p l a n te _ p

S u r f a c e f o l i a i r e s é n e s c e n t e

NFm or t ek

0kgen

gengen

gen d k)a SF

kb SFc SFe x p (41

a SFé n e s c e n c eSF p l a n te _ s

S u r f a c e f o l i a i r e d e l a p l a n t eS F p l a n t e = S F p l a n t e _ p r o d u i t e – S F p l a n t e _ s é n e s c e n t e

F a c t e u r t h e r m i q u eF T i = 1 – 0 , 0 0 2 5 ( 0 , 2 5 T m i n + 0 , 7 5 T m a x – 2 5 ) ²

P r o d u c t i o n j o u r n a l i è r e d e b i o m a s s e

d M S i = b x i x P A R i

E f f i c i e n c e b i o l o g i q u e

b i = b p o t i x F T i

R e n d e m e n t e n g r a i n e sM S g r a i n e = M S c a p i x H I _ g r a i n e g e n

n o n

B i o m a s s e d u c a p i t u l e

T T i < T T _ E 1 M S c a p i i = 0

T T i < T T _ M 3

M S c a p i > = M S i x H I _ c a p i g e n M S c a p i = M S i x H I _ c a p i g e n

igeni

2. 83i MS

7 7 4TT_ E1TT1

0 ,6 3 2MSc a p i

1)-( TT_E1-F1-TT

TTi - TT_F11 b g e ng e ng e n

g e n

g e ng e n

g e ni

TT_M0-TT_M3TT_M0TT - 1 ( 2 ( exp 0, 38

ge nge n

ige nge n

TT_M0TT_M3TTTT_M3 NFf inal NFmorte

Construction of dedicated models

(adapted from Lecoeur et al., 2008)

Flow chart of potential yield estimation in sunflower

Input data

Phenology

Architecture (3D)

Light interception (3D)

Biomass production

Biomass partitioning



D o n n é e s c l i m a t i q u e sT m o y T m i nT m a x P A R i

T T > T T _ M 3 g e n

F i n

N o m b r e d e f e u i l l e s à f i n e x p a n s i o n

N F < N F f i n a l g e n N F = P h y l l o c h r o n e g e n x T T j

N F = N F f i n a l g e n

P h é n o l o g i eT T _ E 1 g e n T T _ F 1 g e nT T _ M 0 g r n T T _ M 3 g e n

T e m p s t h e r m i q u e d e p u i s l a l e v é e

it

levée

i Tb a s e )d t(Tmo yTT

o u i

I n d i c e f o l i a i r eL A I = d e n s x S F p l a n t e

E f f i c i e n c e d ’ i n t e r c e p t i o n

i = 1 – e x p ( - k g e n x L A I i )

R a n g d e l a d e r n i è r e f e u i l l e m o r t e

T T i > T T _ M 0 g e n

E f f i c i e n c e b i o l o g i q u e p o t e n t i e l l e

T T i < T T _ E 1 g e n , b p o t i = 1

T T i < T T _ F 1 g e n , b p o t i =

T T i < T T _ M 0 g e n , b p o t i = b g e n

T T i < T T _ M 3 g e n , b p o t i = b g e n x

B i o m a s s e a é r i e n n e t o t a l eM S i = M S i - 1 + d M S i

S u r f a c e f o l i a i r e d e l a p l a n t e p r o d u i t e

NFj

0j

gen

gengen

gen d j)

a SFjb SFc SFe x p (41

a SFro d u i teSF p l a n te _ p

S u r f a c e f o l i a i r e s é n e s c e n t e

NFm or t ek

0kgen

gengen

gen d k)a SF

kb SFc SFe x p (41

a SFé n e s c e n c eSF p l a n te _ s

S u r f a c e f o l i a i r e d e l a p l a n t eS F p l a n t e = S F p l a n t e _ p r o d u i t e – S F p l a n t e _ s é n e s c e n t e

F a c t e u r t h e r m i q u eF T i = 1 – 0 , 0 0 2 5 ( 0 , 2 5 T m i n + 0 , 7 5 T m a x – 2 5 ) ²

P r o d u c t i o n j o u r n a l i è r e d e b i o m a s s e

d M S i = b x i x P A R i

E f f i c i e n c e b i o l o g i q u e

b i = b p o t i x F T i

R e n d e m e n t e n g r a i n e sM S g r a i n e = M S c a p i x H I _ g r a i n e g e n

n o n

B i o m a s s e d u c a p i t u l e

T T i < T T _ E 1 M S c a p i i = 0

T T i < T T _ M 3

M S c a p i > = M S i x H I _ c a p i g e n M S c a p i = M S i x H I _ c a p i g e n

igeni

2. 83i MS

7 7 4TT_ E1TT1

0 ,6 3 2MSc a p i

1)-( TT_E1-F1-TT

TTi - TT_F11 b g e ng e ng e n

g e n

g e ng e n

g e ni

TT_M0-TT_M3TT_M0TT - 1 ( 2 ( exp 0, 38

ge nge n

ige nge n

TT_M0TT_M3TTTT_M3 NFf inal NFmorte

Construction of dedicated models

(adapted from Lecoeur et al., 2008)

Flow chart of potential yield estimation in sunflower

Input data

Phenology

Architecture (3D)

Light interception (3D)

Biomass production

Biomass partitioning



Estimation of a productivity index from the genotypic traits

A simple biophysic model allows to take into account from 80 to 90% of the observed phenotypic variability in potential yield among a panel of 30 genotypes.

(from Lecoeur et al., 2008)

To evaluate the genotype performancesThe front of modelling experiences

A sensitivity analysis allowed to quantify the impact on plant productivity of the genotypic traits

(from Lecoeur et al., 2008)

All the major functions contributed to the productivity variability.

Classical ANOVA detected only the contribution of the harvest index

To evaluate the genotype performancesThe front of modelling experiences

biomass production and partitioning along growth cycles

0

5

10

15

20

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133

growth cycles

bio

mass p

rod

ucti

on Biom Tot

internode

blade

petiol

Flow er

Reminder : first setting of the biomass partitioning model (Greenlab)

Objective : to understand the genotype variability of harvest index

(d’après Rey et al., 2006)

Fitting on experimental data on 4 genotypes

Leafarea

Leaf biomass

Leaf sink

strength

Sink strengths : petiole < leaf < stem < capitulum

0,45 < 1,00 < 1,07 < 3000

Actually, we are combining SunFlo (crop model) with GreenLab (FSPM) in order to analyse the genotypic

variability of harvest index

Sunflo, a crop model including :• A description of plant compartiments (vegetative parts, reproductive parts, roots),• A description of main processes (organogenesis, morphogenesis, photosynthesis, biomass partitioning),• Responses to temperature, solar radiation and water availability.• Each genotype is described by a set of 15 to 20 traits

Quantitative Genetics Modules :• Estimation of genetic correlation between phenotypic traits,• Estimation of heritabilities,• Choice of selection pressure on the traits according the target environnement,

Applying several selection cycles resulting in population with new phenotypic characterics. The performance of each new genotype is tested in various environnement. This leads to estimate the potential genetic progress.


First attempt in combining genetics modules and crop model to test the potentialities of a virtual breeding on index

3. Potentialities and present limitations

Potentialities

The past 10-20 years plant modelling could be now an effective tool to analyse and model the genotype – environment interaction:

• Estimations of microclimate variables• Modelling plant responses to environment• Ranking plant traits in term of quantitative impact on phenotypic variability• Predictions of integrated plant phenotypic

The links between concepts and methologies from various disciplines may increase the progress in understanding integrated plant phenotypes.

Conclusions

Present limitations

• Low spreading of the biophysical modelling culture.

• Heavy cost of phenotypic information.

• Lack of applied mathematic adapted to complex systems.

Conclusions

Documents

Use of ecophysiological approaches and biophysic plant modelling