Biology subjects

Lobet, G.

Publications and source records attributed to Lobet, G..

26 records · Page 2Linked to original sources

bioRxiv2019

CPlantBox, a whole plant modeling framework for the simulation of water and carbon related processes

The interaction between carbon and flows within the plant is at the center of most growth and developmental processes. Understanding how these fluxes influence each other, and how they respond to heterogeneous environmental conditions, is important to answer diverse questions in forest, agriculture and environmental sciences. However, due to the high complexity of the plant-environment system, specific tools are needed to perform such quantitative analyses.\n\nHere we present CPlantBox, full plant modelling framework based on the root system model CRootBox. CPlantbox is capable of simulating the growth and development of a variety of plant architectures (root and shoot). In addition, the flexibility of CPlantBox enables its coupling with external modeling tools. Here, we connected it to an existing mechanistic model of water and carbon flows in the plant, PiafMunch.\n\nThe usefulness of the CPlantBox modelling framework is exemplified in four case studies. Firstly, we illustrate the range of plant structures that can be simulated using CPlantBox. In the second example, we simulated diurnal carbon and water flows, which corroborates published experimental data. In the third case study, we simulated impacts of heterogeneous environment on carbon and water flows. Finally, we showed that our modelling framework can be used to fit phloem pressure and flow speed to (published) experimental data.\n\nThe CPlantBox modelling framework is open-source, highly accessible and flexible. Its aim is to provide a quantitative framework for the understanding of plant-environment interaction.

plant biology↗

bioRxiv2019

Call for participation: Collaborative benchmarking of functional-structural root architecture models. The case of root water uptake.

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute towards improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.

plant biology↗

bioRxiv2019

MARSHAL, a novel tool for virtual phenotyping of maize root system hydraulic architectures

Functional-structural root system models combine functional and structural root traits to represent the growth and development of root systems. In general, they are characterized by a large number of growth, architectural and functional root parameters, generating contrasted root systems evolving in a highly nonlinear environment (soil, atmosphere), which makes unclear what impact of each single root system on root system functioning actually is. On the other end of the root system modelling continuum, macroscopic root system models associate to each root system instance a set of plant-scale, easily interpretable parameters. However, as of today, it is unclear how these macroscopic parameters relate to root-scale traits and whether the upscaling of local root traits are compatible with macroscopic parameter measurements. The aim of this study was to bridge the gap between these two modelling approaches by providing a fast and reliable tool, which eventually can help performing plant virtual breeding.\n\nWe describe here the MAize Root System Hydraulic Architecture soLver (MARSHAL), a new efficient and user-friendly computational tool that couples a root architecture model (CRootBox) with fast and accurate algorithms of water flow through hydraulic architectures and plant-scale parameter calculations, and a review of architectural and hydraulic parameters of maize.\n\nTo illustrate the tools potential, we generated contrasted maize hydraulic architectures that we compared with architectural (root length density) and hydraulic (root system conductance) observations. Observed variability of these traits was well captured by model ensemble runs We also analyzed the multivariate sensitivity of mature root system conductance, mean depth of uptake, root system volume and convex hull to the input parameters to highlight the key parameters to vary for efficient virtual root system breeding. MARSHAL enables inverse optimisations, sensitivity analyses and virtual breeding of maize hydraulic root architecture. It is available as an R package, an RMarkdown pipeline, and a web application.\n\nOne-sentence summaryWe developed a dynamic hydraulic-architectural model of the root system, parameterized for maize, to generate contrasted hydraulic architectures, compatible with field and lab observations and that can be further analyzed in soil-root system models for virtual breeding.\n\nAuthors contributionsF.M., X.D., M.J. and G.L. designed the study and defined its scope; F.M. and G.L. developed the model while associated tools were created by A.H. and G.L.; F.M. ran the model simulations and analyzed the results together with M.J and G.L.; F.M. and M.J. wrote the first version of this manuscript; all co-authors critically revised it.

plant biology↗

bioRxiv2019

GRANAR, a new computational tool to better understand the functional importance of root anatomy

Root hydraulic conductivity is an important determinant of plant water uptake capacity. In particular, the root radial conductivity is often thought to be a limiting factor along the water pathways between the soil and the leaf. The root radial conductivity is itself defined by cell scale hydraulic properties and anatomical features. However, quantifying the influence of anatomical features on the radial conductivity remains challenging due to complex, and time-consuming, experimental procedures.\n\nWe present a new computation tool, the Generator of Root ANAtomy in R (GRANAR) that can be used to rapidly generate digital versions of root anatomical networks. GRANAR uses a limited set of root anatomical parameters, easily acquired with existing image analysis tools. The generated anatomical network can then be used in combination with hydraulic models to estimate the corresponding hydraulic properties.\n\nWe used GRANAR to re-analyse large maize (Zea mays) anatomical datasets from the literature. Our model was successful at creating virtual anatomies for each experimental observation. We also used GRANAR to generate anatomies not observed experimentally, over wider ranges of anatomical parameters. The generated anatomies were then used to estimate the corresponding radial conductivities with the hydraulic model MECHA. This enabled us to quantify the effect of individual anatomical features on the root radial conductivity. In particular, our simulations highlight the large importance of the width of the stele and the cortex.\n\nGRANAR is an open-source project available here: http://granar.github.io\n\nOne-Sentence summaryGenerator of Root ANAtomy in R (GRANAR) is a new open-source computational tool that can be used to rapidly generate digital versions of root anatomical networks.

plant biology↗

bioRxiv2018

Connecting the dots between computational tools to analyse soil-root water relations

In the recent years, many computational tools, such as image analysis, data management, process-based simulation and upscaling tools, were developed to help quantify and understand water flow in the soil-root system, at multiple scales (tissue, organ, plant and population). Several of these tools work together or, at least, are compatible. However, for the un-informed researcher, they might seem disconnected, forming a unclear and disorganised succession of tools.\n\nIn this article, we present how different pieces of work can be further developed by connecting them to analyse soil-root-water relations in a comprehensive and structured network. This \"explicit network of soil-root computational tools\" informs the reader about existing tools and help them understand how their data (past and future) might fit within the network. We also demonstrate the novel possibilities of scale-consistent parameterizations made possible by the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to fill them.\n\nHighlightsMany computational tools exist to quantify water flow in the soil-root system. These tools can be arranged in a comprehensive network that can be leveraged to better interpret experimental data.

plant biology↗

bioRxiv2017

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

BackgroundGenetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).\n\nFindingsWe trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.\n\nConclusionsWe have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.

plant biology↗

bioRxiv2017

Novel multiscale insights into the composite nature of water transport in roots

- MECHA is a novel mathematical model that computes the flow of water through the walls, membranes and plasmodesmata of each individual cell throughout complete root cross-sections, from a minimal set of cell level hydraulic properties and detailed root anatomical descriptions.\n- Using the hydraulic anatomical framework of the Zea mays root reveals that hydraulic principles at the cell and root segment scales, derived independently by Katchalsky and Curran [1967] and Fiscus and Kramer [1975], are fully compatible, irrespective of apoplastic barriers leakiness.\n- The hydraulic anatomy model accurately predicts empirical root radial permeability (kr) from relatively high cell wall hydraulic conductivity and low plasmodesmatal conductance reported in the literature.\n- MECHA brings novel insights into contradictory interpretations of experiments from the literature by quantifying the impact of intercellular spaces, cortical cell permeability and plasmodesmata among others on root kr, and suggests new experiments efficiently addressing questions of root water relations.\n\n\nSymbols

plant biology↗

bioRxiv2017

CRootBox: A Structural-Functional Modelling Framework For Root Systems

Background and AimsRoot architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architectural models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. We present a root architectural model, CRootBox, as a flexible framework to model architecture and its interactions with static and dynamic soil environments.\n\nMethodsCRootBox is a C++ -based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and CSV. We further created a database of published root architectural parameters. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted into a freely available web application.\n\nKey resultsWe demonstrate the use of CRootBox for 5 different cases (1) free growth of individual root systems (2) growth of root systems in containers as a way to mimic experimental setups, (3), field scale simulation, (4) root growth as affected by heterogeneous, static soil conditions, and (5) coupling CRootBox with Soil Physics with Python code to dynamically compute water flow in soil, root water uptake, and water flow inside roots.\n\nConclusionsIn conclusion, we present a fast and flexible functional-structural root model which is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of root on soil. In the future, we plan to extend this approach to the aboveground part of the plant.

plant biology↗