Artificial Gene Regulatory Networks and Spatial Computation: A Case Study

International audienceThis paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular Artificial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchronization as well as various kind of behavioral patterns, ranging from smooth diffusion to abrupt transition patterns

Chromatin dynamics in Arbidopsis development: a live cell imaging approach

Dissertation presented to obtain the Ph.D degree in BiologyThe proper development of multicellular organisms demands the distinct specification of a variety of specialized cell types. While this is one of the oldest statements of developmental genetics, how different patterns of gene expression are established in genetically identical cells and maintained during somatic cell divisions is still an active topic of research. Chromatin structure is now recognized to regulate gene activity playing a crucial role in cell differentiation and development. Chromatin is not simply a packaging tool but a dynamic entity that reflects the regulatory cues necessary to program appropriate cellular pathways. There are several ways by which chromatin structure can be remodelled. These mechanisms include DNA-methylation, post-translational modifications of histone proteins, histone variants and, nuclear localization. While the dynamic nature of chromatin structure has been previously described its biological function and repercussions on development are only now beginning to be revealed. In this work we used in vivo microscopy techniques to assess how different aspects of chromatin organization play a role on various aspects of development.(...)A bolsa de doutoramento com a referência SFRH/BD/23202/2005 foi atribuída pela Fundação para a Ciência e Tecnologia (FCT), no âmbito do Quadro Comunitário de Apoio, comparticipado pelo Fundo Social Europeu (FSE)

Artificial Gene Regulatory Network and Spatial Computation: A Case Study

International audienceThis paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular Artificial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchronization as well as various kind of behavioral patterns, ranging from smooth diffusion to abrupt transition patterns

Morphogenesis by coupled regulatory networks: Reliable control of positional information and proportion regulation

Based on a non-equilibrium mechanism for spatial pattern formation we study how position information can be controlled by locally coupled discrete dynamical networks, similar to gene regulation networks of cells in a developing multicellular organism. As an example we study the developmental problems of domain formation and proportion regulation in the presence of noise, as well as in the presence of cell flow. We find that networks that solve this task exhibit a hierarchical structure of information processing and are of similar complexity as developmental circuits of living cells. Proportion regulation is scalable with system size and leads to sharp, precisely localized boundaries of gene expression domains, even for large numbers of cells. A detailed analysis of noise-induced dynamics, using a mean-field approximation, shows that noise in gene expression states stabilizes (rather than disrupts) the spatial pattern in the presence of cell movements, both for stationary as well as growing systems. Finally, we discuss how this mechanism could be realized in the highly dynamic environment of growing tissues in multi-cellular organisms.Comment: Journal of Theoretical Biology, in pres

An artificial development model for cell pattern generation

La formation de structures cellulaires a un rôle crucial dans le développement tant artificiel que naturel. Cette thèse présente un modèle de développement artificiel pour la génération de structures cellulaires basé sur le paradigme des automates cellulaires (AC). La croissance cellulaire est contrôlée par un génome comportant un réseau de régulation artificiel (RRA) et une série de gènes structurels. Ce génome a subi une évolution par algorithme génétique (AG) afin de produire des structures cellulaires en 2D grâce à l'activation et inhibition sélective des gènes. De plus des gradients morphogénétiques ont été utilisés pour fournir aux cellules une information de position permettant de contraindre leur reproduction. Après évolution d'un génome par algorithme génétique, une cellule unique est placée au milieu de la grille de l’AC où sa reproduction, contrôlée par le RRA, produit une structure cellulaire cible. Le modèle a été appliqué avec succès au problème classique de génération de la structure d’un drapeau français (French flag problem).Cell pattern formation has a crucial role in both artificial and natural development. This thesis presents an artificial development model for cell pattern generation based on the cellular automata (CA) paradigm. Cellular growth is controlled by a genome consisting of an artificial regulatory network (ARN) and a series of structural genes. The genome was evolved by a genetic algorithm (GA) in order to produce 2D cell patterns through the selective activation and inhibition of genes. Morphogenetic gradients were used to provide cells with positional information that constrained cellular replication. After a genome was evolved, a single cell in the middle of the CA lattice was allowed to reproduce controlled by the ARN until a cell pattern was formed. The model was applied to the canonical problem of growing a French flag pattern.