Biology subjects

Lobet, G.

Publications and source records attributed to Lobet, G..

4 recordsLinked to original sources

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↗