3.4.1 A new paradigm for modeling tree structures in biology

There is an ubiquitous presence of tree data in biology: plant structures, tree-like organs in animals (lungs, kidney vasculature), corals, sponges, but also phylogenic trees, cell lineage trees, etc. To represent, analyze and simulate these data, a huge variety of algorithms have been developed. For a majority, their computational time and space complexity is proportional to the size of the trees. In dealing with massive amounts of data, like trees in a plant orchard or cell lineages in tissues containing several thousands of cells, this level of complexity is often intractable. Here, our idea is to make use of a new class of tree structures, that can be efficiently compressed and that can be used to approximate any tree, to cut-down the complexity of usual algorithms on trees.

3.4.2 Efficient computational mechanical models of growing tissues

The ability to simulate efficiently physical forces that drive form development and their consequences in biological tissues is a critical issue of the MOSAIC project. Our aim is thus to design efficient algorithms to compute mechanical stresses within data-structures representing forms as the growth simulation proceeds. The challenge consists of computing the distribution of stresses and corresponding tissue deformations throughout data-structures containing thousands of 3D cells in close to interactive time. For this we will develop new strategies to simulate mechanics based on approaches originally developed in computer graphics to simulate in real time the deformation of natural objects. In particular, we will study how meshless and isogeometric variational methods can be adapted to the simulation of a population of growing and dividing cells.

3.4.3 Realistic integrated digital models

Most of the models developed in MOSAIC correspond to specific parts of real morphogenetic systems, avoiding the overwhelming complexity of real systems. However, as these models will be developed on computational structures representing the detailed geometry of an organ or an organism, it will be possible to assemble several of these sub-models within one single model, to figure out missing components, and to test potential interactions between the model sub-components as the form develops.

Throughout the project, we will thus develop two digital models, one plant and one animal, aimed at integrating various aspects of form development in a single simulation system. The development of these digital models will be made using an agile development strategy, in which the models are created and get functional at a very early stage, and become subsequently refined progressively.

3.4.4 Development of a computational environment for the simulation of biological form development

To support and integrate the software components of the team, we aim to develop a computational environment dedicated to the interactive simulation of biological form development. This environment will be built to support the paradigm of dynamical systems with dynamical structures. In brief, the form is represented at any time by a central data-structure that contains any topological, geometric, genetic and physiological information. The computational environment will provide in a user-friendly manner tools to up-load forms, to create them, to program their development, to analyze, visualize them and interact with them in 3D+time.

6.1.1 Gnomon

• Name:
  Gnomon
• Keywords:
  4D, Modelization and numerical simulations, Finite element modelling, Computational biology, Data visualization
• Scientific Description:
  Gnomon is a user-friendly computer platform developed by the Mosaic team for seamless simulation of form development in silico. It is intended to be a major tool for the team members to develop, integrate and share their models, algorithms and tools. In Gnomon, a developing form is represented at any time by a central data-structure that contains topological, geometric, genetic and physiological information and that represents the state of the growing form. Flexible components (plugins) make it possible to up-load or to create such data-structures, to program their development, to analyze, visualize them and interact with them in 3D+time.
• Functional Description:

  Gnomon is a plugin-based computational platform for the analysis and simulation of morphogenesis. It relies on a scalable software architecture based on the dtk kernel developed by the group of software engineers (SED) from the Sophia-Antipolis Inria Center. The development of Gnomon aims at answering four main challenges:

  * Provide an easily accessible computational tool for the exploration of morphogenesis, by focusing on the deployability of the software (using conda), on the ergonomics of the user interface and the availability of the documentation.

  * Give access to powerful tools for the exploration of dynamical forms, through an interactive visualization framework allowing the exploration in space in time and the access to algorithmic resources developed by the team for image sequences of multicellular tissues or collections of branching forms.

  * Ensure the interoperability of computational libraries within the platform and its extensibility by a generalized plugin-based architecture (facilitated by the dtk framework) for algorithms, visualizations and data structures, enabling the members of the team and future users to feed the platform with their own C++ and Python libraries.

  * Bridge the gap between experimental data and computational simulations by offering the possibility to go from one to the other in the same platform in a nearly transparent way, thanks to a common dynamical system framework integrated to the core of the platform.

  Gnomon project organization: * Project leader: Christophe Godin * Software development coordinator: Guillaume Cerutti * DTK coordinators: Julien Wintz, Thibaud Kloczko * Plugin coordinators: Jonathan Legrand, Romain Azais, Olivier Ali, Frédéric Boudon. * Diffusion coordinator: Teva Vernoux

  This work is part of the Gnomon ADT project supported by the Inria centers of Grenoble Rhône-Alpes and Sophia-Antipolis Méditerranée.

• Release Contributions:
  This version comes with a major GUI redesign through the migration to Qt6/QML, giving rise to a more fluid and user-friendly interface and a unified styling. The layout and action flow has been revised to provide a more intuitive user experience. Regarding functionalities, it focuses on the possibility to save the current session as a .json file, to reload it in the main application and run the same pipeline again, or to replay it directly through a command-line interface. Developments have been made to support cell-tracking in multicellular tissue images, and to provide a connection with the web-based 4 browser Morphonet.
• News of the Year:
  The ADT project, started in 12/2021 with the recruitment of 2 software development engineers in the team, provides significant momentum to the Gnomon project. It allowed to address wide-ranging technical challenges (Qt6/QML migration) but most importantly to ensure a steady development pace, under the supervision of a project manager (T. Cabel). The focus features of this first year (GUI, session reloading, 3D image analysis) have made it possible to pass on the Gnomon application to beta-testers, picked among close collaborators, and to include user feedback in the development process.
• Contact:
  Christophe Godin
• Participants:
  Olivier Ali, Frédéric Boudon, Tristan Cabel, Guillaume Cerutti, Christophe Godin, Jonathan Legrand, Arthur Luciani, Grégoire Malandain, Karamoko Samassa

6.1.2 MorphoNet

• Name:
  MorphoNet
• Keywords:
  3D web, Morphogenesis, Big data, 3D reconstruction
• Functional Description:
  MorphoNet is an open-source web-based morphological browser. It consists of a web application, exploiting the Unity3D gaming engine, which offers the user a comprehensive palette of interactions with the data, in order to explore the structure, the dynamics and the variability of biological systems. Users can also project quantitative and genetic properties onto the morphological scaffold, allowing for instance to easily explore the correlation between shape dynamics and gene expression patterns. On top of that, datasets and associated information can be shared with other selected users or with entire groups. This possibility of directly sharing results within and between research communities, together with the use of a unified, human readable format, makes MorphoNet a unique tool for multidisciplinary research. Its web-based, user-friendly and open-source structure is also ideal for science dissemination and teaching.
• URL:
  http://­www.­morphonet.­org
• Contact:
  Emmanuel Faure
• Partner:
  CRBM - Centre de Recherche en Biologie cellulaire de Montpellier

6.1.3 TimageTK

• Name:
  Tissue Image ToolKit
• Keywords:
  3D, Image segmentation, Fluorescence microscopy, Image registration, Image processing, Image filter
• Scientific Description:
  TimageTK (Tissue Image Toolkit) is a Python package dedicated to image processing of multicellular architectures such as plants or animals and is intended for biologists, modellers and computer scientists.
• Functional Description:
  TimageTK (Tissue Image Toolkit) is a Python package dedicated to image processing of multicellular architectures such as plants or animals and is intended for biologists and modelers. It provides grayscale or labeled image filtering and mathematical morphology algorithms, as well as image registration and segmentation methods.
• Release Contributions:
  - Improve CLI tools - Improve data model - IO fixes - Add physical axes management for image-like data - Improve algorithms module - Improve components module - Improve logging - Improve documentation & docstrings - Better notebooks - Add cluster matching methods - Add analysis reporting tools - Add synthetic data generation algorithms - Add seed detection & signal quantification algorithms - Better conda packaging
• URL:
  https://­mosaic.­gitlabpages.­inria.­fr/­timagetk/­index.­html
• Contact:
  Jonathan Legrand
• Participants:
  Guillaume Cerutti, Jonathan Legrand, Grégoire Malandain

6.1.4 treex

• Name:
  treex
• Keywords:
  Graph algorithmics, Data structures, Combinatorics, Machine learning
• Scientific Description:

  Trees form an expanded family of combinatorial objects that offers a wide range of application fields, especially in biology, from plant modeling to blood vessels network analysis through study of lineages. Consequently, it is crucial for the team to develop numerical tools and algorithms for processing tree data, in particular to answer questions about the representation of biological organisms and their forms in silico.

  treex is a Python 3 library dedicated to the manipulation of tree objects, whatever they are ordered or not, with or without quantitative or qualitative labels.

• Functional Description:
  The package provides a data structure for rooted trees as well as the following main functionalities: - Random generation algorithms - DAG compression for ordered or not, labeled or not, trees - Approximation algorithms for unordered trees - Edit distance for unordered labeled trees - Kernels for ordered or not, labeled or not, trees - Computation of coding processes (Harris path, Lukasiewicz walk and height process) - Visualization algorithms in Matplotlib or in LaTeX
• Release Contributions:
  In 2019, treex has been published in JOSS (Journal of Open Source Software). In 2021, the architecture of treex has been deeply modified. In the new version, treex is made of a main module implementing the data structure and of several self-contained application modules: analysis, simulation, lossy or lossless compression, etc.
• URL:
  https://­gitlab.­inria.­fr/­azais/­treex
• Publication:
  hal-02164364
• Contact:
  Romain Azais
• Participants:
  Romain Azais, Guillaume Cerutti, Didier Gemmerle, Florian Ingels

6.1.5 ctrl

• Name:
  Cell Tracking and Robust Lineaging
• Keywords:
  Image registration, Fluorescence microscopy, 3D, Multi-Object Tracking
• Scientific Description:

  The package provides several robust automatic or semi-automatic cell lineage methods in order to handle different ranges of deformations. All rely on the estimation of a non-linear transformation between consecutive images of a time-lapse sequence obtained using the blockmatching algorithm (provided in the TimageTK library). The choice of the method depends on the quality of the tissue alignment obtained after the registration procedure.

  When tissues are correctly aligned, which usually happens in the case of small deformations, a “one-shot” approach was developed that uses an overlap measure between daughter cells and their transformed candidate mother cells to determine the best cell pairings. This decision can be made either locally (maximum overlap) or through a global flow-graph optimization approach.

  If only a partially correct alignement is observed, a situation commonly related to large deformation cases, this simple tracking method can be embedded in an iterative procedure. More precisely, high-confidence pairings are selected at each iteration to improve the estimated non-linear transformation at each step.

  In the case where no correct alignement is obtained, it is possible to define manually a set of pairings used to initialize the registration procedure. Depending on the quality of resulting alignement, either the “one-shot” or the iterative approach can then be applied.

  The package also provides an intrinsic metric to estimate the quality of a cell lineage based on the preservation of the geometric cell context between a mother cell and its daughter cells. This quality map can be visualized to easily identify the tissue areas where there might be errors in the computed lineage.

  In the case where errors are observed in the computed lineage, we allow the user to specify manually "nudge" pairings, that are used to re-estimate the transformation and subsequently the lineage. This effectively provides a tool to locally correct a cell lineage in a semi-automatic way.

• Functional Description:
  Python package providing tracking methods to compute cell lineages in 3D+T images of growing plant tissues
• URL:
  https://­gitlab.­inria.­fr/­mosaic/­work-in-progress/­ctrl/­-/­tree/­develop
• Publication:
  hal-03683119
• Contact:
  Jonathan Legrand
• Participants:
  Manuel Petit, Jonathan Legrand, Guillaume Cerutti, Grégoire Malandain, Christophe Godin

Comparison of image segmentation methods for cell identification

Accurately identifying cell regions in 3D images is a crucial first step in many biological analysis methods. For a long time, cell segmentation has been performed using techniques that required significant manual parameter tuning. Recently, a new class of algorithms based on deep learning have been proposed which have been shown to achieve high accuracy in identifying objects from images automatically and requiring minimum human intervention. These deep learning based algorithms usually have the structure of a sequential pipeline consisting of a deep learning model which can be trained for prediction of the segmented regions along with set of pre and post processing steps.

To compare the relative performances of deep learning segmentation algorithms and identify their strengths and weaknesses, we proposed a global evaluation strategy that consisted in:

• Selecting several deep learning based pipelines from the literature which could be trained for 3D cell instance segmentation task.
• Using a common expertized dataset of 3D cellular images (confocal images of floral or shoot apical meristems) to train and test the pipelines.
• Modifying raw images by adding artificial artefacts (over/under-exposition, noise, blur) encountered in image acquisition to evaluate the robustness of the pipelines
• Applying the same set of metrics and visualisation tools to compare the performances of the pipelines and in depth evaluation of their segmentation quality.
• Comparing the performance of the deep learning based pipelines with an established watershed based non-deep learning segmentation method 31.

By evaluating segmentation accuracy, rates of and under and over-segmentation in the whole tissue or in individual cellular layers (L1, L2, inner), this analysis provides a deeper insight into the robustness of each of the segmentation pipelines and helps to test their sensitivity to different image artifacts.

A Gitlab repository has been created to make this segmentation evaluation framework publicly available and a paper was published in Plos Computational Biology 15.

Robust extraction and characterization of cellular lineages

The quantitative study of developing tissues is mainly based on the analysis of time-lapse image acquisitions, from which cell-level temporal properties such as volumetric growth rate or cell cycle duration can be recovered through the identification of cell lineages. In plant tissues, accurate and automatic construction of cell lineages remains a real challenge because of the large deformations taking place between consecutive time-points, especially during the post-embryonic morphogenesis processes. In contrast with animal embryogenesis 6, these constraints impose the use of a two-stage procedure where image segmentation and cell lineaging are done separately.

Building on previous tracking methods published by the team 31, 38 and on the TimageTK computational library (developed in collaboration with the Inria team Morpheme), we implemented several robust fully-automatic cell lineage methods in order to handle different range of non-linear deformations. For small deformations, an overlap-based tracking method was implemented and tested on synthetic data and expertized experimental data. We embedded this method in an iterative procedure where cell lineage and registration transformation are alternatively improved. Based on geometric cell context preservation, a cell lineage quality map can be automatically inferred and used later on to select appropriate anchor points that will improve registration transformation. The validation on experimental data showed a significant improvement of the tracking accuracy in the regions presenting larger deformations.

On top of our iterative automatic tracking method we developed a nudge approach for fast and semi-automatic lineage curation. Given a proposed cell lineage, the cell lineage quality map can be used to detect visually low lineage quality areas. Then, in these areas, the user can manually provide a small set of correct cell lineages. This additional information is then used to improve the registration transformation and automatically correct the lineage locally.

These methods have been presented at the IEEE-ISBI 2022 conference 18 and are included in a new Python library called ctrl (Cell Tracking & Robust Lineaging) 6.1.5, built upon the TimageTK package. This work is part of the Inria Défi Naviscope.

Cells spatio-temporal properties and population statistics

Over the past few years, we have developed algorithms and libraries to achieve quantitative characterization of various spatio-temporal properties of the cells, such as volume, volumetric growth rate or strain pattern. A recent addition to this collection of quantification tools is the estimation of the cell heights in a multi-layered tissue, as their maximal extent in the local normal direction to the tissue surface 16.

To structure this rich spatio-temporal data, we have implemented a spatio-temporal graph structure, formalizing the cell tissue network and its dynamics in a dedicated computational object. For practical use, such as statistical analysis and data interaction, we implemented biologically relevant methods to explore the tissues trough an API with a biological semantics. In addition, we proposed a simple data structure model and implemented CLI tools to allow for batch processing of biological tissues. As the biological objects to study may contain several thousands of cells and/or can be observed at a high temporal frequency, the obtained spatio-temporal graph structures can become very large. Therefore we also developed specific 3D visualization tools to manipulate and explore these structures. This work is implemented in the long-term supported Python library TimageTK 6.1.3.

The latest development for this project was mostly improvements or fixes of existing features as we are now making use of this library to tackle broader biological problems than those envisioned in the initial developments. Originally, we aimed at the characterization of Arabidopsis thaliana Floral Meristem cellular patterns by means of clustering. On that topic, we implemented methods to automatically generate analysis reports and easily compare clusters.

Part of these tools and methodology is now also used in a collaborative work aiming at the characterization of the early developmental stages of the moss Physcomitrium patens. The switch to this new species is challenging as, instead of the large and dense tissues of Arabidopsis thaliana Floral Meristem, it presents filamentous structures with emerging young buds composed of just a few cells. The change of biological object and the necessity to have non-computer scientists working with our tools is a great driving force for this project.

Finally, the integration of TimageTK software components as plugins in the Gnomon platform 6.1.1 is still ongoing, and its use in the beta-testing phase has given helpful insight on possible improvements.

Atlases of development: construction and update

Developing digital atlases of organism or organ development is a complex challenge for tissues that do not present a stereotyped cellular layout, as it is the case for most plant organs. For instance, to generate a cell-based atlas representing the development of a floral meristem of Arabidopsis thaliana we had to chose a single representative flower template, on which the spatio-temporal binary expression patterns of 27 genes was then introduced manually 8.

To proceed further, as the manual building of a cellular template remains a bottleneck of the method, we aim to automatize the construction of genetic atlases from time-lapse image acquisitions displaying both cell interface markers and genetic reporters. In such case, we need to consider a pipeline where (1) time-lapse sequences can be spatio-temporally registered and (2) genetic information can be projected from one sequence to another in a quantitative manner. Methods have been developed on these two aspects.

A previous work addressing the spatio-temporal registration of floral meristem time-lapses sequences 38 relied on the meristem size to perform the temporal alignment. This metric is not fully adapted to accurately compare developmental states, since important size variations can be observed from one individual to another. To overcome this limitation, we developed a method based on the surface curvature, a metric that captures all the morphological changes of the floral development. More precisely, curvature profiles are extracted from each image along the lateral symmetry plane of floral meristems, and then compared 2-by-2 to obtain a temporal alignment of the sequences. The method was evaluated on floral meristem datasets against the previous approach and showed improvement on the temporal alignment quality. Moreover, the comparison method naturally provides a spatial alignment between individuals that can be used to initialize an automatic spatial registration procedure.

Superimposing organs with a similar developmental state allows to propagate genetic information across different individuals. Because floral development is not stereotyped at the cell scale, projection can not be performed through a 1-to-1 cell mapping, but has to be considered at the tissue level. We developed methods to transfer gene expression information in various ways (quantity/concentration lossles transfer). The main rationale behind these methods is to rely on the overlap of cells after registration to distribute the genetic information across individuals. The validation of this approach is currently performed on a larger set of experimental data, and should be published in 2023. This work is part of the Inria Défi Naviscope.

Extraction of biological landmarks using Machine Learning

In order to superimpose similar organs coming from different individuals, it is possible to map biological landmarks to compute a geometrical transformation, instead of relying only on image-based registration. For instance, in the case of the shoot apical meristem (SAM), detecting the centers of the central zone (CZ) and of the youngest organ primordium allows to align a population with a great precision 4.

However, locating such landmarks often requires to acquire images with an additional biological marker, which adds experimental constraints. Focusing on the detection of the CZ center, we used a Machine Learning approach trained on existing data of SAMs imaged with both a geometry and a CZ marker (CLV3) to predict the position of the CZ center.

The chosen model is an adaptation of the deep learning model U-Net to downsampled 2D projections of confocal microscopy SAM images. The model trained on 69 SAM images, augmented with random geometrical and intensity transformations, was able to detect the CZ center with an error of less than 1 cell on an independent set of SAM images, even imaged with a different geometry marker. This work, initially carried out in the context of a M2 internship in 2021, was presented in a conference this year 23 and will be applied to biological questions in the coming year in the context of the HydroField ANR project.

Experimental characterization of tissues during morphogenesis

Spanning from tissue to organ scales, we have started to investigate the structure of the tendrils of climbing plants, and their evolution during growth. We aim at monitoring the underlying 3D structure of the tendril as a function of time by destructive and non-destructive technics.

• We started by characterizing the time evolution of tendrils geometry using time-lapse photography. This enabled us to identify the stereotyped stages of tendril evolution at a macroscopic scale: first attachement to the support, then onset of writhing, then formation of helicoidal loops and a so-called perversion which connects two helices of opposite chiralities.
• To access 3D geometrical and histological information about the tendrils at the tissue scale, we have performed serial sectioning on different specimens at specific stages. Staining with appropriate chemicals have revealed cell walls, membranes and lignin, enabling to understand the microscopic organization of the tissue. The main result is the progressive lignification as a function of time and space. In particular lignification seems to evolve linearly with time and space, and does not depend on the geometry. This would indicate that the complex 3D geometry is the result of growth only.
• Preliminary non-destructive experiments have been performed. We plan to perform 3D time-lapse confocal microscopy imaging to follow the temporal evolution of the biological structure using natural auto-fluorescence or Propidium Iodide staining. These experiments will be complemented by X-ray microtomography experiments, which is more costly but should enable a full 3D reconstruction, while confocal imaging won’t give access to the center of the samples.

This work has been carried out during the internship of Lénaelle Doualla in the framework of the Dynavine Emergence grant, and will be continued in 2023.

Automatized characterization of 3D plant architecture.

The digital reconstruction of branching forms and the quantification of phenotypic traits (lengths of inter-nodes, angles between organs, leaf shapes) is of great interest for the analysis of plant morphology at population scale.

The latest algorithmic development in the characterization of 3D plant architecture comes from the thesis of Katia Mirande who developed and published a graph-based approach for simultaneous semantic and instance segmentation of plant 3d point clouds 21. This work has demonstrated the geometry-based methods can be efficient to tackle the problem at hand, even with a large and imperfect point-cloud. Moreover it has proven successful in identifying organs for different types of plants (tomato and chenopodium). This work was defended by Katia Mirand in December for her PhD thesis.

In the final year of the ROMI project, we aimed at making the Plant Imager (robot scanner) and the Plant 3D Vision tools (reconstruction and analysis pipeline) more than the initial proof of concept. To move towards a more mature technology, we notably had to improve its accessibility to standards users (human-readable messages, simplify commands, ...) and make it available on the web. For the Plant Imager, this meant to write a detailed documentation with the bill of materials and instructions to build the robot. For the Plant 3D Vision tools, this meant to release them on GitHub and the Inria GitLab and write a detailed documentation. We also provides Docker images to greatly simplify the installation procedure.

Another aspect that is required to propose a mature technology is the efficiency and robustness of the systems and processes. To that extent, we carried out experiments to determines the optimal set of acquisition parameters (like the number of images to acquire) or the reconstruction parameter to use to get the best digital clone possible. These experiments have been made with real plants and the automated measurements were compared to manual ones. This notably required to gain better and finer control over the used third-party libraries, especially for COLMAP. It also required new development in robotics as some of the original design shortcomings were too important to be ignored. This resulted in the development of a new camera arm for the Plant Imager and to re-think the communication across devices (now mostly wireless).

Altogether this lead to improve our acquisition setup and the software accessibility.

We are now moving to larger scale acquisitions to further test and improve our technology with an biology oriented experimental design that will lead us to publish our first biological paper using our technology.

Characterization of 3D plant shape and texture at the organ scale

Complementary to the full 3D reconstruction of plant architecture (ROMI project), we have developped a new platform to characterize plants in 3D at the organ scale coordinated by Julien Derr (typically at leaf scale). We can have access to the geometry and the texture of the leaf with high spatial (millimetric) and temporal (seconds) resolution. This will make it possible to quantify in 3D the rich spatio-temporal growth patterns of leaves observed during unfolding 40, 29, 39, where “fast” elastic phenomena (buckling) or ample (nutation) motions are occuring.

In collaboration with computer vision scientists from Université de Strasbourg (Franck Hetroy-Wheeler and collaborators), we built a multicamera set up 41. The set up is installed at ENS de Lyon in the new M8 building dedicated to plant growth.

Lucie Poupardin started her PhD in october 2022. Lucie is now finishing to set up and calibrate the platform. Lucie is going to use this platform to research the kinematics of leaf unfolding.

Antagonist cell responses to mechanical stress set organ size

Organ size depends on complex biochemical and mechanical interactions between cells and tissues 30, 42. In collaboration with biologists from the SEED-DEV team, we investigated the regulation of seed size, a key agronomic trait, by mechanical interactions between two compartments: the endosperm and the seed coat 34.

By combining experiments with computational modeling, we tested a mechanosensitive incoherent feedforward loop (ms-IFFL) hypothesis in which pressure-induced stresses play two antagonistic roles; directly driving seed growth, but indirectly inhibiting it through mechanosensitive stiffening of the seed coat. We showed that our ms-IFFL model can recapitulate wild type growth patterns and explain the counter-intuitive small seed phenotype of the haiku2 mutant. Our work further revealed that the developmental regulation of endosperm pressure is needed to prevent a precocious reduction of seed growth rate induced by force-dependent seed coat stiffening.

This work has been under review for publication during most of this year. The corresponding back and forth exchanges with reviewers generated multiple new investigations. We notably added a time dependence on the control variable (endosperm pressure) of our feedforward loop to account for observed phenotypes in some mutants of interest. With these add-ons, our manuscript has been accepted (dec. 9th 2022) for publication in Nature Communications 13.

Mechanical stresses guide the formation of tricellular junction during cell division

In most biological tissues, cells attach each other by forming stable tricellular junctions (3CJ) 36. In plants, the emergence of these tricellular junctions during cell division remains poorly understood. However, the influence of mechanical stresses on cell division orientation has been recently highlighted 37, 26 and suggests that, as in animals 27, mechanical stresses could be central in defining such stable structures in plants.

Together with colleagues from the SICE team, we wondered how tissue topology produces mechanical patterns within cell walls leading to biochemical signaling involved in 3CJ formation. We focused our attention on a very specific tissue: The cortex layer of the root meristem in Arabidopsis thaliana. These tissues have a very stereotypical organization (stagered files of cells) that can be perturbed genetically and chemically, making them ideal structures for such studies.

To estimate pressure-induced stresses in the root cortex, we capitalized on its specific geometry (rotational symmetry around the root axis) and performed FEM-based simulations on 2D mechanical structures mimicking cortex cells, with subcellular resolution. These simulations were performed using the BVPy library 33, developed within the team. To characterize the influence of the cortex topology and cell geometry on the pressure-induced stress field, we conceived purely artificial structures where cell shapes, sizes and arrangements could be tuned. In parallel, to estimate accurately stress patterns in real tissues, we generated meshes directly from microscopic acquisitions of real tissues. Comparing both outputs enabled us to better understand how geometrical and topological features of the root cortex can be translated into mechanical signals at the subcellular scale.

This project is a new collaboration between the MOSAIC & SICE teams. It has been mostly carried out by Elsa Gascon (now PhD student in the team) and supervised by Olivier Ali (MOSAIC) and Marie-Cecile Caillaud (SICE). A manuscript on this work is currently being written. Noteworthily, Olivier Ali & Marie-Cécile Caillaud have been invited to present this work in the context of the Inrae metaprogram Digit-Bio in October 2022.

Multiscale modelling of growing plant tissues

How morphogenesis depends on cell wall rheological properties is an active direction of research, and we are interested in mechanical models of growing plant tissues, where microscopic cellular structure or even subcellular structure is taken into account. In order to establish links between microscopic and macroscopic tissue properties, we perform a multiscale analysis of a model of growing plant tissue with subcellular resolution. We use homogeneisation to rigorously deduce the corresponding tissue scale continuous model. Tissue scale mechanical properties are computed from all microscopic structural and material properties, taking into account advection by the growth field. We then consider case studies and numerically compare the detailed microscopic model and the tissue-scale model, both implemented using finite element method in FreeFEM. We find that the macroscopic model can be used to efficiently make predictions about several configurations of interest. Our work will help making links between microscopic measurements and macroscopic observations in growing tissues. A paper is in preparation about these results.

This project is a collaboration between Annamaria Kiss, who freshly joined the MOSAIC team, Arezki Boudaoud (Ecole Polytechnique, Paris, France) and Mariya Ptashnyk (Heriot-Watt University, Edinburgh, UK).

Force inference

In the context of the HYDROFIELD ANR project and the postdoctoral contract of André-Claude Clapson, we are developing a force inference method in the SAM that derives wall stresses and cell turgor pressures from the geometry of the cells. Plant cells are inflating thanks to their turgor pressure, but this quantity cannot easily be measured. We have suggested a new indirect method inspired by foam mechanics: combining Laplace law (that relates pressure, wall curvature and stress) and the Gauss-Bonnet theorem (that expresses a geometrical constraint on cell shape), we develop a methodology to estimate stresses and pressures from observations of cells shapes in confocal images. We are finalizing this in 2D tissues and plan to extend the method to 3D tissues.

Coupling wall mechanics and water fluxes

Still il the context of Hydrofield and in collaboration with biologists from RDP (Olivier Hamant's group), we showed that a model that we have previously developed 3 is able to explain a set of apparently contradictory experimental facts in the growth of primordia at the SAM. An article is being written with our colleagues biologists and could provide a seminal interpretation of the role of water in the SAM development.

Mechanics of tendrils

In the framework of the Dynavine project, we are investigating the force and torque generation of tendrils of climbing plants as a function of time and growth development. To do so, we have developed an experimental set up that we have been testing on synthetic rods.

• This preliminary work have lead us to discover a new and exciting result about rod mechanics : One can completely change the chirality of a helical rod by unwinding it. Doing so, the rod goes through a transition state involving two helices with opposite chiralities spatially connected by a so-called “perversion”. In our work, we reported an experimental demonstration of this phenomenon. We monitored the axial torque and load upon such a transformation and revealed a phase transition like behaviour. We proposed a biphasic expansion of the elastic energy and reproduced the encountered behaviours. Our experiments also displayed hysteresis upon helical unwinding but numerical simulations seems to indicate that it is due to specific properties of our material. A corresponding paper is beeing submited.
• Now that our set-up is ready, we are monitoring live plants. We are recording universal signatures of force and torque evolution as a function of writhing. Based on our experimental results, we are developing a phenomenological model of tendrils writhing.

Transport of auxin in mosses.

Branching patterns are omnipresent in plant architecture. The molecular mechanisms that drive the emergence of such patterns during the development of the plant are being unravelled, but many questions are still open. It was first discovered in the 1930s, thanks to experiments on flowering plants, that the shoot apical meristem controls the plant architecture by inhibiting the initiation of new branches below it. This so-called apical dominance was later shown to be mediated by the phytohoromone auxin, which circulates from cell to cell thanks to polar membrane transporters, generating a basipetal bulk flow and acting as a morphogen gradient. It was recently discovered that apical dominance is a common mechanism in mosses 28, and that the responsible signal is auxin as well, which is surprising as axillary branching emerged independently in vascular plants and bryophytes, the two main groups of land plants, which respectively include flowering plants and mosses. However, there is no polar flux of auxin in mosses 32, and biological observation and computational modelling led to the hypothesis of a diffusive transport mechanism of auxin in mosses leafy shoots, through plasmodesmata, the channels that connect neighbouring cells cytoplasms. In collaboration with Yoan Coudert (RDP Lab), we aim at modelling the moss development under the later hypothesis in order to test whether diffusion is sufficient to explain the branching patterns observed in our moss model Physcomitrium patens (PhD work of Jeanne Abitbol, year1 in 2022).

Analysis of auxin transport at cellular level in the SAM from confocal images.

Macroscopic model of organ interactions in plants have been particularly successful in explaining phyllotaxis patterns at the SAM. However, the details of the molecular processes allowing the spatiotemporal coordination of the cells necessary to the maintenance of the regularity of the pattern is still a frontier question. Two main actors are thought to contribute to the emergence and maintenance of phyllotactic patterns. On the one hand, the plant hormone auxin accumulates at different sites of the SAM and triggers organ differentiation. On the other hand, polarized PIN1 proteins at the cell membranes directs auxin transport in the tissue. Recent experiments and methods developed in the team provided quantitative spatiotemporal data of auxin and PIN1 localization. These data have been analyzed at cell scale as discrete raw data, and at tissue scale as continuous data allowing to compare different individuals 4. These observations question the mainly adopted interpretation of auxin transport in the SAM, saying that PIN1 are polarized in the cell menbranes according to the gradients of auxin in the tissue. Our ongoing work consists of expanding the analysis of the mass of data collected in 4 and studying alternative auxin accumulation explanations 22. In particular, we develop a continuous model to capture the essence of the interplay between the observed auxin scalar field and PIN1 vector field.

Phenotyping and modeling of root hydraulic architecture

Water uptake by roots is a key adaptation of plants to aerial life. Water uptake depends on root system architecture (RSA) and tissue hydraulic properties that, together, shape the root hydraulic architecture. This work investigates how the interplay between conductivities along radial (e.g. aquaporins) and axial (e.g. xylem vessels) pathways determines the water transport properties of highly branched RSAs as found in adult Arabidopsis (Arabidopsis thaliana) plants. A hydraulic model named HydroRoot was developed, based on multi-scale tree graph representations of RSAs. Root water flow was measured by the pressure chamber technique after successive cuts of a same root system from the tip toward the base. HydroRoot model inversion in corresponding RSAs allowed us to concomitantly determine radial and axial conductivities, providing evidence that the latter is often overestimated by classical evaluation based on the Hagen–Poiseuille law. Organizing principles of Arabidopsis primary and lateral root growth and branching were determined and used to apply the HydroRoot model to an extended set of simulated RSAs. Sensitivity analyses revealed that water transport can be co-limited by radial and axial conductances throughout the whole RSA. The number of roots that can be sectioned (intercepted) at a given distance from the base was defined as an accessible and informative indicator of RSA. The overall set of experimental and theoretical procedures was applied to plants mutated in ESKIMO1 and previously shown to have xylem collapse. This approach will be instrumental to dissect the root water transport phenotype of plants with intricate alterations in root growth or transport functions. This work, resulting from a collaboration with Christophe Maurel's group in Montpellier, has been published this year 12.

The fractal nature of plants.

Inflorescence branching systems are complex and diverse. They result from the interaction between meristem growth and gene regulatory networks that control the flowering transition during morphogenesis. To study these systems, we focused on cauliflower mutants, in which the meristem repeatedly fails in making a complete transition to the flower and for which a complete mechanistic explanation was still lacking.

In collaboration with Eugenio Azpeitia (who started this project as a post-doc in the Virtual Plants team) and François Parcy's group in Grenoble, we have developed a model of the control of floral initiation by genes in Arabidopsis thaliana, refining previous networks from the literature so that they can integrate our hypotheses about the emergence of cauliflower phenotypes. The complete network was validated by multiple analyses, including sensitivity analyses, stable state analysis, mutant analysis, among others. It was then coupled with an architectural model of plant development using L-systems. The coupled model was used to study how changes in gene dynamics and expression could impact in different ways the architectural properties of plants. The model was then used to study how changes in certain parameters could generate different curd morphologies, including the fractal-like Romanesco. This work has been published in the Journal Science 2. This year, we received a price from the French Academie des Sciences for this work (see Highlights of the year section) and we presented the work as invited presentations and in workshops and international conferences.

Goodness-of-fit tests in regression models.

Many goodness-of-fit tests have been developed to assess the different assumptions of a regression model. Most of them are “directional” in that they detect departures from a given assumption of the model. Other tests are said “global” because they assess whether a model fits a dataset on all its assumptions. In the paper 11 published this year, we focus on the task of choosing the structural part of the regression function because it contains easily interpretable information about the studied relationship. We consider 2 nonparametric “directional” tests and one nonparametric “global” test, all based on generalizations of the Cramér-von Mises statistic. To perform these goodness-of-fit tests, we have developed the R package cvmgof providing an easy-to-use tool for practitioners. A simulation study has been carried out in order to show how the package can be exploited to compare the 3 aforementioned tests.

Post-transcriptional regulation of transcription factor codes in immature Neurons in drosophila.

How the vast array of neuronal diversity is generated remains an unsolved problem. Here, we investigated how 29 morphologically distinct leg motoneurons are generated from a single stem cell in Drosophila. We contributed to a study where 19 transcription factor (TF) codes expressed in immature motoneurons just before their morphological differentiation were identified. We developed a novel computational analysis, based on peak detection in motorneuron expression data to show quantitatively that the TFs codes are progressively established in iMNs as a function of their birth order

In this paper, comparison of RNA and protein expression patterns of multiple TFs revealed that post-transcriptional regulation plays an essential role in shaping these TF codes. We showed for the first time that two RNA binding proteins, Imp and Syp, are expressed in opposite gradients in immature post-mitotic motoneurons and control the translation of multiple TFs. The varying sensitivity of TF mRNAs to the opposite gradients of Imp and Syp in immature motoneurons decrypts these gradients into distinct TF codes, which in turn establish the connectome between motoneuron axons and their target muscles.

A fast Dejittering approach for line scanning microscopy

In an internship back in 2020 at Innopsys (a biomedical device designing and producing company) Landry Duguet focused on the image quality restoration of the InnoQuant device. This line-scanning microscope produces 2D images of 125 000 X 375 000 pixels of 200nm size in 3 hours. Its high precision makes it suffer from a jittering artifact where the odd and even lines of the image are shifted, significantly impacting the image analysis. We proposed two fast methods for dejittering, one is based on dynamic programming and is linear in time, the other rely on a convex relaxation designed for parallel architectures. Both provide better time and quality results than the literature. This work has been published this year 17

8.1.1 H2020 projects

H2020 - ROMI (2017-2022)

Participants: Romain Azaïs, Guillaume Cerutti, Christophe Godin, Florian Ingels, Jonathan Legrand, Katia Mirande, Franck Hetroy-Wheeler [External Collaborator], Teva Vernoux [External Collaborator].

This project is aimed at understanding how molecular regulation integrates with mechanics to control overall plant shape, an unresolved problem with wide implications for both fundamental and applied biology. We will address this issue in the Arabidopsis flower, which, besides their obvious importance as reproductive structures, are amongst the best characterised systems in plant developmental biology. From a mechanistic point of view, it is widely accepted that regulatory molecular networks interfere with the properties of the structural cellular elements (cell wall, cytoskeleton) to induce particular growth patterns. How this occurs and how this is coordinated in space is not known. To obtain a mechanistic understanding of such a complex process, information from multiple scales, from molecular networks to physical properties and geometry have to be combined into a single picture. An integrated tool to do so is currently not available. Building on our complementary experience in interdisciplinary research on plant development, we will therefore develop a tool, called the “Computable Flower” that permits (i) integration of data on geometry, gene expression and biomechanics and (ii) the user to explore, interpret and generate hypotheses based on data supported by mechanistic modelling approaches. The tool therefore provides an integrated description in the form of a 3D dynamic template of the growing flower bud.

Partners:

• Sony-Paris(UK)
• Iaac(Spain)
• FEI(France)
• CNRS(France)
• UBER(Germany)
• Chatelain (France)

Inria ADT - Gnomon / Naviscope (2021-2024)

Participants: Olivier Ali, Romain Azaïs, Guillaume Cerutti, Emmanuel Faure [External Collaborator],, Christophe Godin, Jonathan Legrand, Arthur Luciani, Grégoire Malandain [External Collaborator],, Karamoko Samassa, Teva Vernoux [External Collaborator].

Gnomon is a user-friendly computer platform developed by the Mosaic team for the analysis and simulation of form development in silico. It is intended to be a major tool for the team members to develop, integrate and share their models, algorithms and tools. Flexible components (plugins) make it possible to load or to create such data-structures, to program their development, to analyze, visualize them and interact with them in 3D+time.

A first prototype 6.1.1 has been developed in collaboration with the software engineers (SED) from the Sophia-Antipolis Inria Center, relying on the dtk software kernel through the course of the previous ADT Gnomon (2018-2020). The current application is a highly interactive GUI that allows to manipulate 3D+t biological objects, and transform them using plugin-based algorithmic bricks, which implicitly composes a data processing pipeline.

The ambition of the new ADT project, designed in synergy with the partners from the Inria Défi Navicope, is to propose a complete ecosystem for the study of dynamical biological systems. It will focus on the possibility to conceive intuitively both analysis and modelling pipelines in the main Gnomon application, to use those pipelines to batch-process datasets with distributed computation, relying on the technical solutions developed in the BioImage-IT project, and to interact with the resulting objects through the web-based 3D+t browser MorphoNet, both projects being supported by Naviscope. The aim is to reach a software quality that will enable the diffusion of the platform, starting with the immediate collaborators of the partners.

Partners:

• SED Sophia Antipolis Inria Research Centre
• SED Rennes Inria Research Centre
• Serpico Inria project-team, Rennes, France
• Hybrid Inria project-team, Rennes, France
• Morpheme Inria project-team, Sophia Antipolis, France

Inria IPL - Naviscope (2018-2022)

Participants: Guillaume Cerutti, Emmanuel Faure [External Collaborator],, Christophe Godin, Jonathan Legrand, Grégoire Malandain [External Collaborator], Manuel Petit.

In this project, we plan to develop original and cutting-edge visualization and navigation methods to assist scientists, enabling semi-automatic analysis, manipulation, and investigation of temporal series of multi-valued volumetric images, with a strong focus on live cell imaging and microscopy application domains. We will build Naviscope upon the strength of scientific visualization and machine learning methods in order to provide systems capable to assist the scientist to obtain a better understanding of massive amounts of information. Such systems will be able to recognize and highlight the most informative regions of the dataset by reducing the amount of information displayed and guiding the observer attention. Finally, we will overcome the technological challenge of gathering up the software developed in each team to provide a unique original tool for users in biological imaging, and potentially in medical imaging.

ANR Cell Whisper (2020 - 2023)

Participants: Christophe Godin, Patrick Lemaire [External Collaborator],, Grégoire Malandain [External Collaborator].

Successful embryogenesis requires the differentiation of the correct cell types, in defined numbers and in appropriate positions. In most cases, decisions taken by individual cells are instructed by signals emitted by their neighbours. A surprisingly small set of signalling pathways is used for this purpose. The FGF/Ras/ERK pathway is one of these and mutations in some of its individual components cause a class of human developmental syndromes, the RASopathies. Our current knowledge of this pathway is, however, mostly static. We lack an integrated understanding of its spatio-temporal dynamics and we can imperfectly explain its highly non-linear (switch-like) response to a graded increase in input stimulus. This systems biology project combines advanced quantitative live imaging, pharmacological/optogenetics perturbations and computational modelling to address, in an original animal model organism, 3 major unanswered questions, each corresponding to a specific aim of the proposal:

• Aim 1: What is the spatio-temporal dynamic of intracellular signal transduction in response to FGF during embryogenesis?
• Aim 2: How is the switch-like response to graded extracellular signals controlled at the molecular level?
• Aim 3: Can the results be integrated into a predictive computational model of the pathway? Through this approach, in a simple model organism, we hope to gain an integrated molecular understanding of the spatio-temporal dynamics of this pathway and of its robustness to parameter variations.

Partners:

• UMR CRBM, CNRS Montpellier, France
• Morpheme Inria projec-team, Sophia Antipolis, France

ANR Netflux (2022 - 2025)

Participants: Christophe Godin, Ibrahim Cheddadi, Guillaume Cerutti.

The identification during the last decades of the molecular actors involved in guard cells signaling and ion transport highlights the fact that stomata opening or closure relies on the balanced control of ion fluxes across both plasma and vacuole membranes (PM and VM). However, how ion fluxes are coordinated between PM and VM membranes remains almost unknown. In this proposal, we hypothesize that the coupling between the ion transport at the PM and the VM is a major factor controlling stomatal aperture. Therefore, the main objective of the NetFlux project is to understand how cellular membranes are finely and tightly coordinated during cellular responses. For this purpose, we will use the guard cells from Arabidopsis thaliana as our cellular and biophysical model. To reach our goals the Netflux project will:

• characterize the ion flux across the PM and VM combining original genetic resources and highly resolutive techniques in living cells (refers to WP1 and WP2)
• develop mathematical and computational models of intracellular ion fluxes in GCs to quantitatively understand the coupling between ion transport across the PM and VM to control stomatal movements (refers to WP3)
• identify new regulators of ion transport in GCs using an original genetic screen based on a genetically encoded biosensor (refers to WP4).

Partners:

• BPMP Unit, Montpellier
• SAVE BIAM CEA, Cadarache

ANR Hydrofield (2021 - 2024)

Participants: Arezki Boudaoud [External Collaborator], Christophe Godin, Ibrahim Cheddadi, Guillaume Cerutti.

Plant architecture continuously develop throughout their lifetime through the activity of the apical meristems located at the tip of growing axes. The genetic regulation of the shoot apical meristems (SAMs), which produces all plant aerial organs, has extensively been studied, various key molecular actors have been identified and their function in patterning the SAM has been mapped in space and time. In addition, recent work has established that these molecular actors not only regulate cell identities but also likely induce the physical deformation of tissues by modifying cell wall mechanical properties, in turn inducing leaf or flower primordia outgrowth. From these works progressively emerges a new mechanistic insight on the link connecting gene regulation, tissue deformation and organ growth in plants. However, despite these recent progresses, the contribution of turgor pressure and water fluxes regulation, that decisively contribute to tissue morphogenesis, is still elusive.

Partners:

• SIGNAL Team RDP, Lyon,
• Ecole Polytechnique, Saclay
• University of Singapour (Yuchen Long)

AEx Discotik (2021 - 2025)

Participants: Olivier Ali, Elsa Gascon.

Computational morphomechanics is the study of living tissue morphogenesis through the scope of physics-based computational modeling. It has become a forefront tool to study organogenesis, where mechanical stresses play a paramount regulating role. At macroscopic scale, smooth living tissues can be described as Riemannian manifolds, subject to continuous mechanics. Concomitantly, at the cellular scale, they appear as networks of discrete effectors, where mechanics should be expressed in a combinatorial manner. Current state-of-the-art models, based on “classic” Finte Element Methods, struggle to efficiently integrate this cellular (discrete) / tissular (continuous) dichotomy. The Discotik project aims to alleviate this difficulty through the use Discrete Exterior Calculus to express the laws of mechanics. While classic FEM rely solely on simplicial meshing of manifolds, ”DEC” also exploits their dual structure, composed of cellular complexes. Strikingly, such cellular structures appear naturally in living tissues. Our goal is to assess this modeling approach on a specific, circumscribed problem: The morphomechanics of plant seed. We expect the “DEC” framework not only to enable faster computations but also to expose the deep connection between mechanical stress, tissue geometry and the corresponding cellular network topology.

Partners:

• Benoît Landrein, SEED team RDP, Lyon.
• Mathieu Desbrun, EPI Geomerix, Inria Saclay / Ecole Polytechnique.

Appel à projets blanc BAP INRAe : Biomove (2022-2023)

Participants: Julien Derr, Mohammed Bendahmane [External Collaborator].

In this proposal, we aim, to combine 3D tracking and quantification of plant movements using biophysical modeling. Our ambition is to better understand the very process of plant growth, and to discover the processes that regulate posture in plant development. To reach these goals, we will use the model plant Arabidopsis thaliana. A. thaliana is more suitable for the planed experiments, as all required tools and lines are available in the participating teams. The key point of this project is that these rich movements associated with plant development are essentially in 3D, and therefore must be quantitatively tracked in 3D. Our scientific program is broken into 4 interconnected tasks. In Task 1, we will set-up an experimental apparatus for 3D reconstruction. Task 2 we be devoted to data processing, and to the development of a new methodology to obtain a precise and fine growth field. In Task 3, we will use and apply our newly developed system (above) to study and compare movements in wild-type A. thaliana and in mutant lines affected in mitotic growth (cell division and proliferation) or post-mitotic growth (cell expansion). The objective here is to assess the impact of slow or accelerated organ and plant growth on motion. In Task 4, we will use the above data and biophysical modeling to confront different mechanosensitivity scenarios in front of our experiments. By putting together three communities (physicist, computer scientists and biologists) we will achieve to quantify 3D plant motion to an extent which had never been done in the state of the art. Therefore, we expect the project to have strong scientific impact on plant biophysics. In particular, new mechanosensitivity lessons will be learned.

IXXI Grant: Emergent geometry in simplicial complexes inspired by plant tissues (2022 - 2024)

Participants: Olivier Ali.

During morphogenesis, plant tissues are developing macroscopic shapes based solely on two microscopic processes: cell growth and cell division. The regulation of morphogenesis through directional and differential cell expansion rate has been extensively studied but the influence of cell division on shape emergence remains far less understood. Concomitantly, the question of emergent geometry within dynamical networks is a very active field of research that draws inspirations from a wide spectrum of scientific question (neurosciences, social media, quantum gravity...). However, so far, only dynamical rule based on aggregation of new nodes at the network margin have been studied. In this project, we propose to implement a new class of dynamical networks where growth is inspired by plant cell division: pairs of nodes are substituting existing ones within the bulk of the network. We will address the question of emergent curvature within such dynamical systems.

IDEX Emergence Grant (Université de Paris): DynaVine (2021 - 2023)

Participants: Julien Derr, Dražen Zanchi [External Collaborator], Neukirch Sébastien [External Collaborator], Simoneau Thierry [External Collaborator].

The aim of DynaVine interdisciplinary emergence is to constitute a solid base for understanding and control of dynamics, growth and force generation by plant tendrils: soft organs with particularly strong mechanosensitivity including thigmotropism, by which the climbing plant can detect a rod-like support, attach to it and pull. Macroscopic mechanical growth patterns that will be deciphered will also help to reveal the microscopic cellular mechanisms of tendril growth under load. Once the growth and the force onset/evolution are under control, we want to explore the possibility to create a tendril motor: a device in which the tendril differential growth is used to generate rapid mechanical action. In order to produce rapid force impulses by rather slow differential growth of the tendril we will use particular types of reversible snapping transition. Our proof of concept, small size and targeted competences of consortium guarantee the dynamism and success of proposed actions, including the application to more ambitious long-term research program. The proposers, J. Derr (now at ENS Lyon) and D. Zanchi (Université de Paris) with competences in plant morphogenesis, time-lapse monitoring and mechano-biophysics will carry out the experimental, time-lapse and data-analysis part of research, both in the laboratory and on the field. The INRA partner, coordinated by T. Simoneau, (UMR LEPSE, Montpellier) will provide necessary competences in agronomy (ampelography, plant ecophysiology) for on-the-field research. The partner S. Neukirch from Institut d’Alembert of the Sorbonne University will assume the theoretical part of the study, for his competences in theory and numerics of elastic structures.

Fonds recherche ENS Lyon « attractivité nouveau professeur » (2022 - 2024)

Participants: Julien Derr.

The active motions of plants observed during their development have never been seriously exploited yet. In this proposal, we aim for the first time, to combine 3D tracking and quantification of plant movements with physical modeling. Our ambition is to get insights about the very process of plant growth itself, and find out about the posture regulations. We will perform experiments on different kind of leaves. The experimental work will be to obtain kinematics and residual stresses. Thanks to the development of a new algorithm, the growth field will be extracted from kinematics. Finally, physical modeling aiming to reproduce the observations as well as chemical treatments will enable the discrimination between different mechanosensitivity hypotheses. We expect the project to have strong scientific impact both on plant biophysics but also in visual computing as new tools will be developed for the accurate reconstruction and tracking of geometrically non trivial shapes.

General chair, scientific chair

• Christophe Godin was co-Chair of the first international "From genes to plant architecture: the shoot apical meristem in all its states", Poitiers, France 28-30 Novembre 2022.

Member of the conference program committees

• Christophe Godin was a member of the Functional-Structural Plant Modeling international conference program committee.

9.1.1 Journal

9.1.2 Invited talks

• Olivier Ali
  • The size of seed to come; How endosperm pressure both promotes and restricts seed growth and size (Cell wall and hormone meeting, 03/2022, Umeå, Sweden)
  • The mechanics behind fourway junction avoidance in Plants. (Webinaire Digit-Bio Concepts Math pour comprendre le vivant, 10/2022, online, hosted by Inrae)
• Guillaume Cerutti
  • Towards reproducible digital experimentation - The Gnomon Project (Plant Computational Biology Workshop, 09/2022, Lyon, France)
• Julien Derr
  • Plant morphogenesis: motions, growth and mechanics (mechanobio-lyon : Mechanobiology and Physics of Life in Lyon, 27/06/2022, Lyon, France)
  • Capturing plant movement at the macroscopic scale (5th LyMIC day, 22/11/2022, Lyon, France)
  • Plant morphogenesis: motions, growth and mechanics (Physics Department Colloquium, 05/12/2022, Lyon, France)
• Christophe Godin
  • International conference "PhysBio", June, Saclay, France, Juin 2022
  • International workshop on Multiscale Modeling of Plant Growth, Pattern Formation and Actuation (22w5179) BIRS Center in Oaxaca (Mexico), Oct 2022.
  • Departement of biology and ecology at the UNAM Université UNAM, Mexico city, Mexico, Oct 2022.
  • Max Planck Institute for Plant Breeding Research (MPIPZ), Cologne, Nov 2022.
• Annamaria Kiss
  • Structure and function of the morphing dandelion fruit (joint talk with Madeleine Seale, Theory of Living Matter Talks, 12/10/2022, Cambridge, UK)

9.1.3 Scientific expertise

• Christophe Godin
  • Review of a project for the Weismann Institute, Israel Science Fundation
  • Review of a project for the Indo–French Centre for the Promotion of Advanced Research.
  • Member of the International Scientific Advisory Committee of the Plant Phenotyping and Imaging Research Centre (P2IRC), Saskatchewan, Canada.
  • Member of the Scientific Committee of the Biology and Adaptation of plants of l’INRA, (43 units, 14 centres, 1150 permanent staff).

9.1.4 Research administration

• Olivier Ali
  • Member of the CAN (Conseil d'Analyse du Numérique) of the SFR Bioscience at ENS de Lyon.
  • Member of the RDP lab committee.
• Romain Azaïs
  • Member of the sustainability committee of the RDP lab at ENS de Lyon
  • Member of the Comité de Centre at Inria Lyon
• Christophe Godin
  • Member of the project-team committee of Lyon Inria Center
  • Member of the managing committee of the RDP Lab
  • President of the Jury of the Inria competition for positions of Chargés de Recherche Classe Normale in Grenoble, 2022.
• Annamaria Kiss
  • Leading the data management committee of the RDP lab
  • Webmaster of the RDP website

9.2.1 Teaching

• Olivier Ali
  • Master class 'The mechanics of growing Plants: Introduction to Plant morphomechanics', M2 SBCP Paris-Saclay University, visiting ENS Lyon (2h).
• Elsa Gascon
  • Techniques de microscopie et utilisation de Molécules Fluorescentes en Biologie Cellulaire, L3 Biosciences ENS Lyon, 10/2022 (2x2h).
  • Travaux pratiques de biologie moléculaire, option Physique et Chimie des systèmes Biologiques, L3 Sciences de la matière ENS de Lyon, 12/2022 (5h)
• Christophe Godin
  • Master Class (20h) in the context of the Romi project Creating Virtual Plants with L-systems, April 2022. Available on Youtube.
  • Master class 'Les plantes dans tous leurs états' for non-specialists, ENS de Lyon: Phyllotaxis. Coord A. Vialette (2h).
• Annamaria Kiss
  • in charge of the "Modelling in biology" L3 level course at the ENS de Lyon, Department of Biology (8h course, 8h practicals, exam).
• Julien Derr
  • in charge of the "Mathematics for biology" M1 level course at the ENS de Lyon, Department of Biology (8h course, exam). This teaching unit involves several members of our team : Jeanne Abitbol (8h tutorials), Guillaume Cerutti (5h lecture, tutorials), Landry Duguet (5h lecture, tutorials) and Romain Azaïs (6h lecture, tutorials, exam).
  • in charge of the "Biostatistics" L3 level course at the ENS de Lyon, Department of Biology (16h tutorials, exam).
  • "Biophysics" M2 level course at the ENS de Lyon, Department of Physics (8h lectures, 12h tutorials, exam).
  • "Computational modeling for developmental biology" M1 level course at the ENS de Lyon, Department of Biology (10h projects, exam).
  • "Bio-modeling " M2 level course at the ENS de Lyon, Department of Biology (18h projects, exam).
  • participation in the "Developmental Biology" L3 level course at the ENS de Lyon, Department of Biology (4h).
  • "Scientific Communications" M1 level course at the ENS de Lyon, Department of Biology (25h).
  • in charge of the internship program for L3 level at the ENS de Lyon, Department of Biology (20h).
  • "Modelling in biology" L3 level course at the ENS de Lyon, Department of Biology ( exam).
  • Master class 'Introduction to plant movements', M2 SBCP Paris-Saclay University, visiting ENS Lyon (<1h).

9.2.2 Supervision

• Olivier Ali
  • Co-supervisor of Elsa Gascon, as Research Engineer, from 09/2021 to 09/2022.
  • PhD Supervisor of Elsa Gascon, started 10/2022.
• Romain Azaïs
  • PhD co-supervisor (with Christophe Godin) of Florian Ingels (started 10/2019 and defended 09/2022)
• Julien Derr
  • PhD Supervisor of Paul Jeammet, started 10/2019. Co-supervision with S. Douady
  • PhD Supervisor of Camille Le Scao, started 10/2020. Co-supervision with S. Douady
  • PhD Supervisor of Émillien Dilly, started 10/2021. Co-supervision with D. Zanchi
  • PhD Supervisor of Lucie Poupardin, started 10/2022. Co-Supervision with M. Bendahmane
• Christophe Godin
  • PhD Advisor of Katia Mirande, started 10/2018, co-supervision with Franck Hetroy-Wheeler. (Defense held in December 2022)
  • PhD Advisor of Manuel Petit, started 10/2019, co-supervision with Grégoire Malandain. (Defense planned in May 2023)
  • PhD Supervisor of Florian Ingels, started 10/2019, co-supervision with Romain Azais.
  • PhD Supervisor of Jeanne Abitbol, started 10/2021, co-supervision with Yoan Coudert.
  • PhD Supervisor of Landry Duguet, started 10/2021, co-supervision with Teva Vernoux.
  • PhD Supervisor of Corentin Bisot, started 10/2021, co-supervision with Tom Shimizu.
  • Advisor for the Habilitation thesis at ENS de Lyon of Romain Azais (defense help in December 2022)
  • Advisor for the Habilitation thesis at ENS de Lyon of Olivier Ali

9.2.3 Juries

• Romain Azaïs and Christophe Godin were members of the PhD thesis jury of Florian Ingels as supervisors.
• Christophe Godin was
  • a member of Romain Azaïs Habilitation thesis jury as advisor (December 2022),
  • a member of the PhD thesis jury of Katia Mirande as supervisor.
• Julien Derr was member of the PhD Thesis jury of Baptiste Tesson as president.

9.3.1 Articles and contents

Christophe Godin wrote in collaboration with François Parcy a paper "Les mystères biologiques de la formation des choux-fleurs." La Recherche, N 570, p 72.

International journals

• 10 articleO.Olivier Ali, I.Ibrahim Cheddadi, B.Benoit Landrein and Y.Yuchen Long. Revisiting the relationship between turgor pressure and plant cell growth.New PhytologistDecember 2022
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• 11 articleR.Romain Azaïs, S.Sandie Ferrigno and M.-J.Marie-José Martinez. cvmgof: an R package for Cramér-von Mises goodness-of-fit tests in regression models.Journal of Statistical Computation and Simulation9262022, 1246-1266
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• 12 articleY.Yann Boursiac, C.Christophe Pradal, F.Fabrice Bauget, M.Mikaël Lucas, S.Stathis Delivorias, C.Christophe Godin and C.Christophe Maurel. Phenotyping and modeling of root hydraulic architecture reveal critical determinants of axial water transport.Plant Physiology1902June 2022, 1289-1306
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• 13 articleA.Audrey Creff, O.Olivier Ali, C.Camille Bied, V.Vincent Bayle, G.Gwyneth Ingram and B.Benoit Landrein. Evidence that endosperm turgor pressure both promotes and restricts seed growth and size.Nature Communications1412023, 67
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• 14 articleF.Florian Ingels and R.Romain Azaïs. Enumeration of Irredundant Forests.Theoretical Computer Science922April 2022, 312-334
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• 15 articleA.Anuradha Kar, M.Manuel Petit, Y.Yassin Refahi, G.Guillaume Cerutti, C.Christophe Godin and J.Jan Traas. Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.PLoS Computational Biology184April 2022, e1009879
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• 16 articleE.Eliana Mor, M.Markéta Pernisová, M.Max Minne, G.Guillaume Cerutti, D.Dagmar Ripper, J.Jonah Nolf, J.Jennifer Andres, L.Laura Ragni, M. D.Matias D Zurbriggen, B.Bert de Rybel and T.Teva Vernoux. bHLH heterodimer complex variations regulate cell proliferation activity in the meristems of Arabidopsis thaliana.iScience2511October 2022, 105364
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International peer-reviewed conferences

• 17 inproceedingsL.Landry Duguet, J.Julien Calve, C.Cyril Cauchois and P.Pierre Weiss. A fast Dejittering approach for line scanning microscopy.ICIP 2022 - IEEE International Conference on Image ProcesBordeaux, FranceOctober 2022
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• 18 inproceedingsM.Manuel Petit, G.Guillaume Cerutti, C.Christophe Godin and G.Grégoire Malandain. Robust Plant Cell Tracking in Fluorescence Microscopy 3D+T Series.ISBI 2022 - IEEE 19th International Symposium on Biomedical Imaging2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)Kolkata, IndiaIEEEMarch 2022, 1-4
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Scientific book chapters

• 19 inbookJ.-L.Jean-Louis Dinh, C.Christophe Godin and E.Eugenio Azpeitia. Introduction to to Computational Modeling of Multicellular Tissues.2395Plant Systems BiologyMethods in Molecular BiologySpringer New YorkNovember 2022, 107-145
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Reports & preprints

• 20 miscF.Farah Ben-Naoum, C.Christophe Godin and R.Romain Azaïs. Characterization of random walks on space of unordered trees using efficient metric simulation.December 2022
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• 21 miscK.Katia Mirande, C.Christophe Godin, M.Marie Tisserand, J.Julie Charlaix, F.Fabrice Besnard and F.Franck Hétroy-Wheeler. A Graph-Based Approach for Simultaneous Semantic and Instance Segmentation of Plant 3D Point Clouds.August 2022
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Other scientific publications

• 22 inproceedingsL.Landry Duguet, G.Guillaume Cerutti, C.Carlos Galvan-Ampudia, E.Etienne Farcot, T.Teva Vernoux and C.Christophe Godin. Revisiting PIN1 polarity interpretation at the SAM based on automated multi-level reconstruction of PIN1 polarities from confocal images.From genes to plant architecture: the shoot apical meristem in all its statesPoitiers, FranceNovember 2022
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• 23 inproceedingsA.Anthony Scriven, C.Carlos Galvan-Ampudia and G.Guillaume Cerutti. Can U-Net replace CLV3? Machine learning for the identification of biological landmarks in Shoot Apical Meristem images.From genes to plant architecture: the shoot apical meristem in all its statesPoitiers, FranceNovember 2022
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