PlantGL : a Python-based geometric library for 3D plant modelling at different scales

In this paper, we present PlantGL, an open-source graphic toolkit for the creation, simulation and analysis of 3D virtual plants. This C++ geometric library is embedded in the Python language which makes it a powerful user-interactive platform for plant modelling in various biological application domains. PlantGL makes it possible to build and manipulate geometric models of plants or plant parts, ranging from tissues and organs to plant populations. Based on a scene graph augmented with primitives dedicated to plant representation, several methods are provided to create plant architectures from either field measurements or procedural algorithms. Because they reveal particularly useful in plant design and analysis, special attention has been paid to the definition and use of branching system envelopes. Several examples from different modelling applications illustrate how PlantGL can be used to construct, analyse or manipulate geometric models at different scales

Using mechanics in the modelling of meristem morphogenesis

International audienceno abstrac

Intensification of Ester Production in a Continuous Reactor

Numerous continuous intensified reactors are now accessible on the market that offer enhanced thermal performances in a continuous reactor. Such reactors are then particularly suited to fast and highly exothermic reactions. In this paper, the ability to also manage a slow and equilibrated system, the methyl acetate esterification reaction, on condition of intensification in terms of design and operating conditions is presented. To achieve this purpose, a new kinetics model has been developed and validated from experiments carried out in a lab scale batch reactor. Implemented in a simulation framework, this model leads to an intensified design of the reactor and the associated operating conditions. All this intensification methodology has been supported and validated by experimental studies

Motifs tree: a new method for predicting post-translational modifications

Motivation: Post-translational modifications (PTMs) are important steps in the maturation of proteins. Several models exist to predict specific PTMs, from manually detected patterns to machine learning methods. On one hand, the manual detection of patterns does not provide the most efficient classifiers and requires an important workload, and on the other hand, models built by machine learning methods are hard to interpret and do not increase biological knowledge. Therefore, we developed a novel method based on patterns discovery and decision trees to predict PTMs. The proposed algorithm builds a decision tree, by coupling the C4.5 algorithm with genetic algorithms, producing high-performance white box classifiers. Our method was tested on the initiator methionine cleavage (IMC) and Nα-terminal acetylation (N-Ac), two of the most common PTMs. Results: The resulting classifiers perform well when compared with existing models. On a set of eukaryotic proteins, they display a cross-validated Matthews correlation coefficient of 0.83 (IMC) and 0.65 (N-Ac). When used to predict potential substrates of N-terminal acetyltransferaseB and N-terminal acetyltransferaseC, our classifiers display better performance than the state of the art. Moreover, we present an analysis of the model predicting IMC for Homo sapiens proteins and demonstrate that we are able to extract experimentally known facts without prior knowledge. Those results validate the fact that our method produces white box models. Availability and implementation: Predictors for IMC and N-Ac and all datasets are freely available at http://terminus.unige.ch/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Flux-Based Transport Enhancement as a Plausible Unifying Mechanism for Auxin Transport in Meristem Development

Plants continuously generate new organs through the activity of populations of stem cells called meristems. The shoot apical meristem initiates leaves, flowers, and lateral meristems in highly ordered, spiralled, or whorled patterns via a process called phyllotaxis. It is commonly accepted that the active transport of the plant hormone auxin plays a major role in this process. Current hypotheses propose that cellular hormone transporters of the PIN family would create local auxin maxima at precise positions, which in turn would lead to organ initiation. To explain how auxin transporters could create hormone fluxes to distinct regions within the plant, different concepts have been proposed. A major hypothesis, canalization, proposes that the auxin transporters act by amplifying and stabilizing existing fluxes, which could be initiated, for example, by local diffusion. This convincingly explains the organised auxin fluxes during vein formation, but for the shoot apical meristem a second hypothesis was proposed, where the hormone would be systematically transported towards the areas with the highest concentrations. This implies the coexistence of two radically different mechanisms for PIN allocation in the membrane, one based on flux sensing and the other on local concentration sensing. Because these patterning processes require the interaction of hundreds of cells, it is impossible to estimate on a purely intuitive basis if a particular scenario is plausible or not. Therefore, computational modelling provides a powerful means to test this type of complex hypothesis. Here, using a dedicated computer simulation tool, we show that a flux-based polarization hypothesis is able to explain auxin transport at the shoot meristem as well, thus providing a unifying concept for the control of auxin distribution in the plant. Further experiments are now required to distinguish between flux-based polarization and other hypotheses

Using mechanics in the modelling of meristem morphogenesis

International audienceno abstrac

InfraPhenoGrid: A scientific workflow infrastructure for Plant Phenomics on the Grid

International audiencePlant phenotyping consists in the observation of physical and biochemical traits of plant genotypes in response to environmental conditions. Challenges , in particular in context of climate change and food security, are numerous. High-throughput platforms have been introduced to observe the dynamic growth of a large number of plants in different environmental conditions. Instead of considering a few genotypes at a time (as it is the case when phenomic traits are measured manually), such platforms make it possible to use completely new kinds of approaches. However, the data sets produced by such widely instrumented platforms are huge, constantly augmenting and produced by increasingly complex experiments, reaching a point where distributed computation is mandatory to extract knowledge from data. In this paper, we introduce InfraPhenoGrid, the infrastructure we designed and deploy to efficiently manage data sets produced by the PhenoArch plant phenomics platform in the context of the French Phenome Project. Our solution consists in deploying scientific workflows on a Grid using a middle-ware to pilot workflow executions. Our approach is user-friendly in the sense that despite the intrinsic complexity of the infrastructure, running scientific workflows and understanding results obtained (using provenance information) is kept as simple as possible for end-users

Phenomenal: a software framework for model-assisted analysis of high throughput plant phenotyping data

International audiencePlant high-throughput phenotyping aims at capturing the genetic variability of plant response to environmental factors for thousands of plants, hence identifying heritable traits for genomic selection and predicting the genetic values of allelic combinations in different environment. This first implies the automation of the measurement of a large number of traits to characterize plant growth, plant development and plant functioning. It also requires a fluent and versatile interaction between data and continuously evolving plant response models, that are essential in the analysis of the marker x environment interaction and in the integration of processes for predicting crop performance [1]. In the frame of the Phenome high throughput phenotyping infrastructure, we develop Phenomenal: a software framework dedicated to the analysis of high throughput phenotyping data and models. It is based on the OpenAlea platform [2] that provides methods and softwares for the modelling of plants, together with a user-friendly interface for the design and execution of scientific workflows. OpenAlea is also part of the InfraPhenoGrid infrastructure that allows high throughput computation and recording of provenance during the execution [3]. Figure 1: The 3D plant reconstruction and segmentation pipeline. Muti-view plants images from PhenoArch are binarised and used to reconstruct plants in3D. The 3D skeleton is extracted and separated into stem (central vertical elements) and leaves. 3D voxels are segmented by propagating skeleton segmentation