Estimating landmarks on 2D images of beetle mandibles

Studying links between phenotype/genotype and agricultural practices is one of the main topics in agronomy research. Phenotypes can be characterized by informations like age, sex of animals/plants and more and more often with the help of image analysis of their morphology. From now, getting good quality of images for numerous individuals is easy but that leads to design automatic procedures to replace manual exploration of such amount of images. Several bottlenecks have been identified to analyze automatically images. One of them is segmentation of selected area and/or shapes, and another well-known one is setting automatically morphometric landmarks. Landmarks are points on the object which can be used to identify or to classify the objects. It exists a lot of methods to experiment landmarks setting, depending on the image contents. This work has been initiated by using the article of Palaniswamy et al. "Automatic identification of landmarks in digital images"[6]. They proposed a method based on calculus of a probabilistic Hough transform coupling to a template matching algorithm. They applied their method to the Drosophilia wings. In our study, we have gotten a set of 291 beetles . For each one 2D images of 5 different parts of their anatomy have been taken: mandibles left and right, head, pronotum and elytra. The first part of the project was to test how the Palaniswamy’s method could be used to analyze them. We have implemented all the required algorithms to compute positions of mandibles landmarks and compared the obtained results to landmarks which have been manually set by biologists. We will see that even positions automatically obtained are not fully precised, if we used centroid size to characterize mandibles, the size computed from automatic landmarks is closed to this one computed from the manual ones. Future works will focus on definition of a semi-landmarks procedure which would add some features as the measure of the curve between two landmarks

Investigating Oxidoreduction Kinetics using Protein Dynamics.

International audienceFor twenty years, there are more and more crystallographic structures of enzymatic macromolecular complexes available in international data banks. At the same time, there is always an interest in better understanding enzyme functionalities. The movements of large protein structures are key components in ligand docking and enzymatic catalysis. Hence, simulations of enzymatic reactions must take into account such structural movements. Our aim is to combine modeling of the redox reactions and modeling of the conformational changes of enzymes structures in order to describe the dynamical functioning of redox enzymes. An agent based system has been developed to simulate internal enzymatic movements and redox reactions. We applied our approach to the complex II and III of the mitochondrial respiratory chain. Using this model, we are able to assess quantitative and qualitative enzymatic kinetic behaviors such as conversion rate of the overall reaction, individual electrons path within the complexes and potentially pathological short-circuits

Multi-Agent Design to Reduce Complexity of Biological Processes

International audienc

Simulation of Electron Transfer with a Shape-Based Grained Multi-Agent Model

The respiratory chain is a biological process which produces energy in the cell. This process relies on electron transfer between macromolecular proteic complexes located in the mitochondrion inner membrane. During this process, the elec- trons are driven from different sites inside the macromolecules. The redox state change of a site has a local effect on the atom positions and can cause a global change of the proteic structure: this is a conformational change. Such modification can affect the behavior of the protein and can disturb the whole process. In addition, proteins are mobile in the membrane and this special medium has serious constraints on molecule encounter which is necessary for electron to be transfered from one complex to another. Models based on differential equations have already been developed for the respiratory chain but they do not include spatial properties, such as localization or conformation of proteins. So, to be more realistic, models have to take into account the possibility of conformational changes and spatial localization of proteins. A spatial representation of proteins can be obtained by using atom positions and, thus, modeling movements of proteins is possible. Molecular dynamics is a simulation technique which use these data to simulate the movements of all atoms of molecules. Though it allows very realistic simulations, the systems that could be simulated are limited in their size and the total simulated time is short (a few nanoseconds). To simulate larger systems, the original molecular dynamic model has been adapted by reducing the number of objects to simulate. Specific models for proteins, like the Residue- Based Coarse-Grained (RBCG) or the Shape-Based Coarse-Grained (SBCG) models, have been designed to allow the simulation of multiproteic systems and the simulated time can reach the microsecond time scale. To model the respiratory chain at the molecular level, both spatial representation and enzymatic behavior are required. As differential equations nor MD simulations allows us to put together this two aspects, we consider to use a multi-agent system. Multi-agent systems are composed of multiple autonomous entities, the agents, in interaction. These agents can interact following a set of embedded rules and with the agent paradigm, it is possible to model a situated entity with interactions based on a physical model. To design the movement of proteins we have defined a model base on Shape-Based Coarse-Grained approach. A first application have been made including two complexes of the respiratory chain (complex II and complex III) and a generic lipid for membrane model. A specific reactive agent model includes the definition of a physical body composed of grains. A lot of simulations have shown that the electron chain can be influenced by movements of proteins and give explanations of several experimental results that are not easy to interpret directly from experiment. Next step will focus on the description of the two others complexes of the respiratory chain (complex I and complex IV) and will test the hypothesis of hyperstructure building

Towards landmarks prediction with Deep Network

International audienc

Using Multi-Agent Systems to Design Internal Movements of Proteins

International audienceMulti-Agent Systems (MAS) are systems composed of multiple autonomous entities (agents) which can collaborate. Applied to biological issues, they were used to model ecological systems such as insect colonies where agents can easily model the simple relationships between entities and the complex behaviour of the entire system. In the field of cellular and molecular biology, an agent is often a cell but rarely a single molecule. Moreover, these kinds of agents are not influenced by their shape or their physical features as they generally are non-physical agents. To model biological processes at the molecular level, especially enzymatic reactions, we have to take into account physical properties and explicit three-dimensional representation of molecules. Thus, we define a type of reactive agents composed of rules which correspond to the behaviour of agents to model enzymatic reactions and of a body which consists in the three-dimensionnal representation and the rules for physical interactions and internal movements. Three-dimensionnal structure are given by structural data extracted from the Protein DataBank (PDB). To determine possible internal movements, we analyzed these data with hinge determination algorithms and elastic network models to identify rigid parts of macromolecules and thus the possible conformational changes. The first application to respiratory chain complexes allows us to split these macromolecules in few parts (3 or 4) and we obtained a simple yet realistic representation

Multi-Agent Model for Simulation at the Subcellular Level

International audienceFrom the beginning of biological modeling, simulations were an efficient way to understand local mechanisms linked to whole system behaviors. Cellular automata and more recently Multi-Agent Systems (MAS) are currently used to model ecological systems. Virus dissemination through population or insect collaborations are well known examples of how simple interactions between entities (agents) are able to build a complex situation at the level of the whole population. But use of MAS to design biological system at the cellular or subcellular level, mainly for enzymatic reactions, is a relatively new application of the agent paradigm. In fact, agents used for ecology simulations are 'non-physical' agents, i.e. in general they do not have any explicit representation of their geometry, their space bulk or the articulated movements of their body parts. These characteristics are essential in enzymatic behaviors. The three dimension structure and movements of this structure condition the realisation of the reaction that can be stopped or conversely favored by specific conformations. In order to simulate subcellular biological processes, we defined agents capable of simulating molecular conformational changes. These agents integrate molecular modeling data, for conformational change methods, and biological ontology data, for conformational change conditions. As some biological entities are motor of the enzymatic reactions while others are simple partners, we defined two agent subtypes, active and passive agents. As a proof of concept, we applied our model to the simulation of enzymatic oxydo-reduction reactions

Mitochondrial OxydoReduction Simulation using Multi-Agent System

International audienc

Identification of functional hubs through metabolic networks

International audienc