Alexandra Fronville

Morphogenesis Model for Systematic Simulation of Forms’ Co-evolution with Constraints: Application to Mitosis

We present a new approach to understand forms’ emergence in a cellular system. We set the hypothesis that beyond the influence of mechanical forces and gene expression, constraints applied to the cells over time play a key role in the acquisition of specific shape. We consider that these constraints are the fundamental principles and basic cause of morphogenesis. In our model, it’s due to these constraints that cells choose a particular direction while dividing, migrate or die. Our approach of morphogenesis based on constraints has been used to get effectively for a given form all possible evolutions by growth at latter times. Such work ensures to do some pattern prediction.

Mutational Analysis-Inspired Algorithms for Cells Self-Organization towards a Dynamic under Viability Constraints

In biology, recent techniques in confocal microscopy have produced experimental data which highlights the importance of cellular dynamics in the evolution of biological shapes. Thus, to understand the mechanisms underlying the morphogenesis of multi-cellular organisms, we study this cellular dynamic system in terms of its properties: cell multiplication, cell migration, and apoptosis. Besides, understanding the convergence of the system toward a stable form, involves local interactions between cells. Indeed, the way that cells self-organize through these interactions determines the resulting form. Along with the mechanisms of convergence highlighted above, the dynamic system also undergoes controls established by the nature on the organisms growth. Hence, to let the system viable, the global behavior of cells has to be assessed at every state of their developement and must satisfy the constraints. Otherwise, the whole system self-adapts in regard to its global behavior. Thus, we must be able to formalize in a proper metric space a metaphor of cell dynamics in order to find conditions (decisions, states) that would make cells to self-organize and in which cells self-adapt so as to always satisfy operational constraints (such as those induced by the tissue or the use of resources). Therefore, the main point remains to find conditions in which the system is viable and maintains its shape while renewing. The aim of this paper is to explain the mathematical foundations of this work and describe a simulation tool to study the morphogenesis of a virtual organism.

French Flag Tracking by Morphogenetic Simulation Under Developmental Constraints

Below the influence of the mechanical cues and genetic expression, constraints underlying the developmental process play a key role in forms’ emergence. Theses constraints lead to cells’ differentiation and sometimes determine the directions of cells growth. To better understand these phenomena, we present in this paper our work focused primarily on a development of a mathematical model. A one which takes into account the co-evolution of cellular dynamics with it’s environment. To study the influence of the developmental constraints, we have developed algorithms to make and explore a base of genomes. The purpose of this exploration is first to check conditions under which specific genes are activated. Then, this exploration allows us to follow the conditions of emergence of some patterns that lead to a specific shape. From our model, we found a genome that can generate the French flag. With this French flag pattern and its genome starting, we addressed the following question: is there another genome in the simulated base that achieves the same shape, i.e. the French flag pattern?

Directional Cellular Dynamics for Tissue Morphogenesis and Tumour Characterization by Aggressive Cancer Cells Identification

Due to the availability of large amount of medical data and the improvements of computers’ capacities, an increase of tools for medical applications has been noted. In the case of cancer, this results in some application and treatment successes in radiotherapy. However, on the one hand, high therapeutic results are yet to be seen, and on the other hand, unpleasant side effects are still widely observed. In the first case, it may arise from the avoidance of any damage to healthy structures implying ineffective treatment, and in the second case it may be, due to lethal doses deposited in the tumour, leading to an unacceptable damage to one or more healthy structures. Thus, it would be useful to simulate the effects of any treatment prior to its application. Thereby, we are focusing on the proposition of computational methods serving to give insights for decisions aid tools in radiotherapy. In this paper, we provide algorithms for tissue growth prediction where cells are elements of a 2D cellular automaton oriented multi-agent system. Then, we propose a novel method to predict and characterize the evolution of a pathological tissue under cells irradiation. We show that the more cells destroyed during the radiotherapy are linked to aggressive cancer cells, the more the treatment lead to an impaired result in terms of growth. By contrast, we highlight that there exists cells less linked to these aggressive cancer cells that are more suitable to target for an effective and efficient radiotherapy. Based on the dominant cells (linked or not linked to aggressive cancer cells), we introduce a novel method to classify tumours.

A Directional Cellular Dynamic Under the Control of a Diffusing Energy for Tissue Morphogenesis: Phenotype and Genotype

We present a new approach to understand shape emergence in a multicellular system. We set the hypothesis that beyond the influence of mechanical forces and gene expression, spatial constraints applied to the cells over time play a key role, mainly at the early stage of the embryo. In our model, cells are elements of a 2D cellular automaton-oriented multiagent system. To achieve a specific shape, they read actions within a genetic program according to the current timestep and logical constraints. This model has been used to generate effectively all possible tissue phenotypes (shapes) at any stage of the early embryo and their associated genotypes (genetic programs). We also build a model that couples the morphological equation governing the growth of the phenotypes with differential equations that diffuse energy in the system. This model has been used to do some pattern predictions, to define properties on tissues for the purpose of classification, and to simulate responses to therapy in the case of pathological tissues.