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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 LECOEUR Professor of Plant Biology Director of Plant Science Department Montpellier SupAgro. 1. Context. Corresponding Virtual plant.

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Use of ecophysiological approaches and biophysic plant modelling

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  1. Use of ecophysiological approaches and biophysic plant modelling in determination of complex phenotypic traits and analysis of interactions GxE Pr. Jérémie LECOEUR Professor of Plant Biology Director of Plant Science Department Montpellier SupAgro

  2. 1. Context

  3. Corresponding Virtual plant An example of an « Integrated Plant Phenotype »: The architecture of the At rosette Picture Col • This integrated phenotype results from: • organogenesis • morphogenesis • carbon metabolism… • in interaction with the environment se rot 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.

  4.  Responses Genotype x Environment = = Phenotype genotype 2 x = = genotype 1 Response Environment 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.

  5. Ecophysiological modelling Process based models (crop models) Organ populations in relation with environment through correlative relationships géno 2 fruits géno 1 Response =  Leaves Environment roots « Virtual plants » Genetic modelling Mainly statistical approaches Set of phytomeres with topological connections with matter flows phenotype = G + E + GxE + e Context : a need to understand the building of the plant phenotype Choice of the plant representation

  6. 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 Purslane plant Cell protein tree (d’après Jeong, 2003) The same level of complexity could be find at organelle, cell, tissue…

  7. Context : a need to understand the building of the plant phenotype Theory of the increase in scientific progress through combinatories of conceptual and technic artefacts (Lebeau, 2005) 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,

  8. 2. Advances in Ecophysiology

  9. Step 0 : Characterization of the physical environment at plant boundaries

  10. Advances in Ecophysiology The absolute necessary to take into account the physical environment Systematic characterization of plant microclimate The minimum data set includes temperature, radiation and atmospheric humidity, wind speed and rainfall In field In growth chamber To allow the comparison between experiments and the establishment of trial network typologies or a future use of models

  11. Advances in Ecophysiology To be as close as possible to the microclimate sensed by the plant or by its organs First use of modelling: to estimate the environmental variables instead of measuring them. To model the energy, radiative and water balances…. (from Rey, 2003; Lhomme and Guilioni, 2004 and 2006; Chenu et al., 2005 and 2007; Louarn et al., 2007)

  12. Phytomere production rate (CDD-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 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)

  13. Advances in Ecophysiology To be as close as possible to the microclimate sensed by the plant or by its organs A lot can be done by using standard bioclimatological indicators… Thermal time, Cumulative solar radiation, Photothermal coefficient, Climatic water balance…

  14. Step 1 : Ecophysiologic diagnosis of the phenotypic variability To dissect the genotype – environment interaction

  15. Wild type Col Ws Ler Dij ron se 3.5 mutants rot Advances in Ecophysiology Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes Analysis of a panel of wild types and their mutants in At (from Chenu et al, 2007)

  16. 0.15 L er Dij 0.10 0.05 0.00 0.01 0.1 1 10 0.001 0.01 0.1 1 10 3.5 ron 0.01 0.1 1 10 0.01 0.1 1 10 0.15 Col / se / rot Ws / 3.5 0.10 0.05 0.00 0.001 0.01 0.1 1 10 0.01 0.1 1 10 -1 -1 Absorbed PAR (mmol plte j ) [log scale] Advances in Ecophysiology Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes All wild type 0.15 Col Ws 0.10 0.05 0.00 se 0.10 0.05 0.00 0.10 0.05 0.00 0.001 0.01 0.1 1 10 All genotypes Comparison wild types vs corresponding mutants 0.15 L er / ron Génotypes 0.10 0.05 0.00 0.01 0.1 1 10 0.001 0.01 0.1 1 10 (from Chenu et al, 2007)

  17. Establishment of consistent relatioship betwen plant and environment variables Response curve families For instance, leaf expansion…

  18. Columbia Serrate Advances in Ecophysiology Second use of modelling: formalization of plant – environment interaction to identify unknown phenotypes Vini = aini log(PARa) + bini This approach allowed to identify a new involvement of the Serrate gene in plant organogenesis. G G x E G (from Chenu et al., 2007)

  19. 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)

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

  21. Advances in Ecophysiology 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.

  22. Advances in Ecophysiology Third use of modelling: to analyse the consequences of multi-trait differences on integrated plant phenotypes Virtual sensitivity analysis of light interception to various phenotypic traits Changes in position of the largest leaf on the stem Changes in plant leaf area Average virtual plant Changes in leaf number (from Casadebaig, 2004)

  23. Virtual plot at flowering (6.6 plants m-2 cv Heliasol) i Sunflower virtual plant cv Heliasol Estimation of light interception 1.0 0.8 0.6 Fraction of radiation intercepted 0.4 Ei 0.2 0.0 Days (from Rey, 2003; Casadebaig, 2004)

  24. A hidden trait affecting the light interception was identified: the distribution of leaf sizes along the stem 200 Plant leaf area Leaf number Position of the largest leaf on the stem Changes in light interception (in % of average plant) 150 Plant heigth Duration of leaf life Blade angle 100 50 -400 -200 0 200 400 Evaluated ranges of variation in observed traits (in % of the average value) Advances in Ecophysiology Third use of modelling: to analyse the consequences of multi-trait differences on integrated plant phenotypes Sensitivity analysis (from Casadebaig, 2004)

  25. Advances in Ecophysiology 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 (adapted from Chenu et al., 2005)

  26. 1- decrease in trophic competition due to the increase in sources 1 2 3a 1 2 3b 2- Increase in trophic competition due to rapid production of new sinks 3-(0C)- Decreasein trophic competition due to the end of secondary axes development 1 2 3a 1 2 3b 3-(6C)- Increase trophic competition due the second growth phasis of clusters Change with time in trophic competition inside the grapevine shoot • 3 phases F V F V

  27. Primary axes P0 secondary axes P1- P2 secondary axes 1.0 0.8 Probability to maintain the development 0.6 Sigmoidial adjustment Syr 0C 0.4 Syr 6C Gre 0C 0.2 Gre 6C A B C • Primary axes are not affected by the trophic competition 0.0 0.00 0.05 0.10 0.15 0.20 0.25 0.00 0.05 0.10 0.15 0.20 0.25 0.00 0.05 0.10 0.15 0.20 0.25 Q/D ratio (arbitrary units) • 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 Relationship between axis development and trophic competition Relationship between Q/D values and the probability of end of secondary axes development

  28. A 1.0 1-5 0.8 0.6 0.4 0.2 0.0 B 1.0 6-10 Probability to maintain the development 0.8 1-5 leaves (0.31g) 0.6 0.4 0.2 6-10 leaves (2.87g) 0.0 C 1.0 11-16 0.8 0.6 P0 secondary axes 0.4 P1-P2 secondary axes 11-16 leaves (10.21g) P1-P2 sigmoid adjustment 0.2 P0 sigmoid adjustment 0.0 0.00 0.05 0.10 0.15 0.20 0.25 Q/D ratio (arbitrary units) Relationship between axis development and trophic competition Relationship between Q/D values and the probability of end of secondary axes development according to their type and size

  29. 3. The front of « modelling experiences »

  30. 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

  31. 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…

  32. Input data Phenology Architecture (3D) Light interception (3D) Biomass production Biomass partitioning The front of modelling experiences To evaluate the genotype performances Construction of dedicated models Flow chart of potential yield estimation in sunflower (adapted from Lecoeur et al., 2008)

  33. Input data Phenology Architecture (3D) Light interception (3D) Biomass production Biomass partitioning The front of modelling experiences To evaluate the genotype performances Construction of dedicated models Flow chart of potential yield estimation in sunflower (adapted from Lecoeur et al., 2008)

  34. The front of modelling experiences To evaluate the genotype performances Estimation of a productivity index from the genotypic traits (from Lecoeur et al., 2008) 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.

  35. The front of modelling experiences To evaluate the genotype performances A sensitivity analysis allowed to quantify the impact on plant productivity of the genotypic traits All the major functions contributed to the productivity variability. Classical ANOVA detected only the contribution of the harvest index (from Lecoeur et al., 2008)

  36. Reminder : first setting of the biomass partitioning model (Greenlab) Objective : to understand the genotype variability of harvest index Fitting on experimental data on 4 genotypes Sink strengths : petiole < leaf < stem < capitulum 0,45 < 1,00 < 1,07 < 3000 Leaf area Leaf biomass Actually, we are combining SunFlo (crop model) with GreenLab (FSPM) in order to analyse the genotypic variability of harvest index Leaf sink strength (d’après Rey et al., 2006)

  37. The front of modelling experiences First attempt in combining genetics modules and crop model to test the potentialities of a virtual breeding on 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.

  38. 3. Potentialities and present limitations

  39. Conclusions • 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.

  40. Conclusions • Present limitations • Low spreading of the biophysical modelling culture. • Heavy cost of phenotypic information. • Lack of applied mathematic adapted to complex systems.

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