VisuAlea, Towards a Scientific Modelling Environment using Visual Programming

International audienceData-flows and Visual Programming Environments (VPE) are used in many scientific domains such as data analysis and visualisation (LabView, VTK, VisTrails, Orange). One of the main interests of VPEs is the ability for users to create and modify dataflows using a graphical interface without any specific programming knowledge. We developed OpenAlea ( OpenAlea wiki) that is a python-based open-source software. It provides a visual programming environment, called VisuAlea , which is written in Python and PyQt. Within VisuAlea, users are able to dynamically combine existing and independent pieces of software into a customizable dataflow. Although OpenAlea and VisuAlea are mainly used in the context of plant science, they can be efficiently used in other scientific domains. A VPE that is implemented in Python language offers several interesting points for managing scientific workflows. First, users have access to the program state at run-time and thus can introspect, modify, document and debug their workflows interactively. Second, users can build graphically complex workflow by assembling both scientific python packages (e.g., numpy, scipy and matplotlib) with other software components written in different languages (C, C++, Fortran, Java or R). Finally, the visualisation of the workflow's structure ease the communication between different users. In VisuAlea, each component is provided with a graphical interface that is automatically generated. Moreover, dataflow model of computation allows to implement lazy evaluation, and automatic parallelisation. VPEs have major drawbacks compared to textual programming (e.g., iteration structures are difficult to represent since dataflow is a directed acyclic graph). However, solutions may be found to cope with these issues. For instance, in VisuAlea, we have introduced functional loops like map to represent iteration structures. Although several VPE have been implemented in Python (e.g. Orange, VisTrails, Viper), their visual environments are not reusable because functional and graphical parts are not separated. In OpenAlea, we have separated the dataflow graph, the evaluation algorithm and the GUI to allow other projects to reuse only the GUI part with their own data structure. The OpenAlea.GraphEditor package implements the logic and the abstract widgets to edit any types of graph such as dataflow without dependency on other OpenAlea packages. Therefore, other projects can reuse OpenAlea.GraphEditor with their own graph data structure via a pluggable system. In this talk, we will briefly present the status of OpenAlea/VisuAlea, dicuss the advantages and drawbacks of a visual programming language, and present some key functionalities of Visualea such as looping technics and parallelism. Then, we will present the GraphEditor package with a brief tutorial. Finally, we will present our methodology to manage the project (e.g. deployment, documentation and quality assurance using continuous integration and a compilation farm)

VisuAlea, Towards a Scientific Modelling Environment using Visual Programming

International audienceData-flows and Visual Programming Environments (VPE) are used in many scientific domains such as data analysis and visualisation (LabView, VTK, VisTrails, Orange). One of the main interests of VPEs is the ability for users to create and modify dataflows using a graphical interface without any specific programming knowledge. We developed OpenAlea ( OpenAlea wiki) that is a python-based open-source software. It provides a visual programming environment, called VisuAlea , which is written in Python and PyQt. Within VisuAlea, users are able to dynamically combine existing and independent pieces of software into a customizable dataflow. Although OpenAlea and VisuAlea are mainly used in the context of plant science, they can be efficiently used in other scientific domains. A VPE that is implemented in Python language offers several interesting points for managing scientific workflows. First, users have access to the program state at run-time and thus can introspect, modify, document and debug their workflows interactively. Second, users can build graphically complex workflow by assembling both scientific python packages (e.g., numpy, scipy and matplotlib) with other software components written in different languages (C, C++, Fortran, Java or R). Finally, the visualisation of the workflow's structure ease the communication between different users. In VisuAlea, each component is provided with a graphical interface that is automatically generated. Moreover, dataflow model of computation allows to implement lazy evaluation, and automatic parallelisation. VPEs have major drawbacks compared to textual programming (e.g., iteration structures are difficult to represent since dataflow is a directed acyclic graph). However, solutions may be found to cope with these issues. For instance, in VisuAlea, we have introduced functional loops like map to represent iteration structures. Although several VPE have been implemented in Python (e.g. Orange, VisTrails, Viper), their visual environments are not reusable because functional and graphical parts are not separated. In OpenAlea, we have separated the dataflow graph, the evaluation algorithm and the GUI to allow other projects to reuse only the GUI part with their own data structure. The OpenAlea.GraphEditor package implements the logic and the abstract widgets to edit any types of graph such as dataflow without dependency on other OpenAlea packages. Therefore, other projects can reuse OpenAlea.GraphEditor with their own graph data structure via a pluggable system. In this talk, we will briefly present the status of OpenAlea/VisuAlea, dicuss the advantages and drawbacks of a visual programming language, and present some key functionalities of Visualea such as looping technics and parallelism. Then, we will present the GraphEditor package with a brief tutorial. Finally, we will present our methodology to manage the project (e.g. deployment, documentation and quality assurance using continuous integration and a compilation farm)

Modeling Strategies Using Visual Programming in OpenAlea

International audienceno abstrac

Lateral root morphogenesis is dependent on the mechanical properties of the overlaying tissues

In Arabidopsis, lateral root primordia (LRPs) originate from pericycle cells located deep within the parental root and have to emerge through endodermal, cortical, and epidermal tissues. These overlaying tissues place biomechanical constraints on the LRPs that are likely to impact their morphogenesis. This study probes the interplay between the patterns of cell division, organ shape, and overlaying tissues on LRP morphogenesis by exploiting recent advances in live plant cell imaging and image analysis. Our 3D/4D image analysis revealed that early stage LRPs exhibit tangential divisions that create a ring of cells corralling a population of rapidly dividing cells at its center. The patterns of division in the latter population of cells during LRP morphogenesis are not stereotypical. In contrast, statistical analysis demonstrated that the shape of new LRPs is highly conserved. We tested the relative importance of cell division pattern versus overlaying tissues on LRP morphogenesis using mutant and transgenic approaches. The double mutant aurora1 (aur1) aur2 disrupts the pattern of LRP cell divisions and impacts its growth dynamics, yet the new organ’s dome shape remains normal. In contrast, manipulating the properties of overlaying tissues disrupted LRP morphogenesis. We conclude that the interaction with overlaying tissues, rather than the precise pattern of divisions, is most important for LRP morphogenesis and optimizes the process of lateral root emergence

Imaging plant growth in 4D : robust tissue reconstruction and lineaging at cell resolution.

International audienceQuantitative information on growing organs is required to better understand morphogenesis in both plants and animals. However, detailed analyses of growth patterns at cellular resolution have remained elusive. We developed an approach, multiangle image acquisition, three-dimensional reconstruction and cell segmentation-automated lineage tracking (MARS-ALT), in which we imaged whole organs from multiple angles, computationally merged and segmented these images to provide accurate cell identification in three dimensions and automatically tracked cell lineages through multiple rounds of cell division during development. Using these methods, we quantitatively analyzed Arabidopsis thaliana flower development at cell resolution, which revealed differential growth patterns of key regions during early stages of floral morphogenesis. Lastly, using rice roots, we demonstrated that this approach is both generic and scalable