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
=
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.
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
Context : a need to understand the building of the plant phenotype
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)
Context : a need to understand the building of the plant phenotype
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)
Context : a need to understand the building of the plant phenotype
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
Advances in Ecophysiology
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
To be as close as possible to the microclimate sensed by the plant or by its organs
(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)
Advances in Ecophysiology
To be as close as possible to the microclimate sensed by the plant or by its organs
Advances in Ecophysiology
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
Advances in Ecophysiology
Col
ronse
rot
3.5
Ws Ler Dij
Wild type
mutants
Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes
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)
Advances in Ecophysiology
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.
Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes
Advances in Ecophysiology
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)
Advances in Ecophysiology
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.
Advances in Ecophysiology
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)
Third use of modelling: to analyse the consequences of multi-trait differences on integrated plant phenotypes
Advances in Ecophysiology
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)
Third use of modelling: to analyse the consequences of multi-trait differences on integrated plant phenotypes
Advances in Ecophysiology
(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
Advances in Ecophysiology
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
To evaluate the genotype performances
The front of modelling experiences
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
To evaluate the genotype performances
The front of modelling experiences
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.
The front of modelling experiences
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
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