Molecular regulation of cork development: the QsMYB1 gene

Cork is a natural and renewable material obtained as a sustainable product from cork oak (Quercus suber L.) during the tree’s life. Cork formation is a secondary growth derived process resulting from the activity of cork cambium. However, despite its economic importance, only very limited knowledge is available about the molecular mechanisms underlying the regulation of cork biosynthesis and differentiation. The work of this PhD thesis was focused on the characterization of an R2R3-MYB transcription factor, the QsMYB1, previously identified as being putatively involved in the regulatory network of cork development. The first chapter introduces cork oak and secondary growth, with special emphasis on cork biosynthesis. Some findings concerning transcriptional regulation of secondary growth are also described. The MYB superfamily and the R2R3-MYB family (in particular) of transcription factors are introduced. Chapter II presents the complete QsMYB1 gene structure with the identification of two alternative splicing variants. Moreover, the results of QsMYB1 expression analysis, done by real-time PCR, in several organs and tissues of cork oak are also reported. Chapter III is dedicate to study the influence of abiotic stresses (drought and high temperature) and recovery on QsMYB1 expression levels. The effects of exogenous application of phytohormones on the expression profile of QsMYB1 gene are evaluated on Chapter IV. Chapter V describes the reverse genetic approach to obtain transgenic lines of Populus tremula L. x tremulóides Michx. overexpressing the QsMYB1 gene. Finally, in Chapter VI the final conclusions of this PhD thesis are presented and some future research directions are pointed based on the obtained results.

Phase transitions and nonlinear phenomena in neuronal network models

Communication and cooperation between billions of neurons underlie the power of the brain. How do complex functions of the brain arise from its cellular constituents? How do groups of neurons self-organize into patterns of activity? These are crucial questions in neuroscience. In order to answer them, it is necessary to have solid theoretical understanding of how single neurons communicate at the microscopic level, and how cooperative activity emerges. In this thesis we aim to understand how complex collective phenomena can arise in a simple model of neuronal networks. We use a model with balanced excitation and inhibition and complex network architecture, and we develop analytical and numerical methods for describing its neuronal dynamics. We study how interaction between neurons generates various collective phenomena, such as spontaneous appearance of network oscillations and seizures, and early warnings of these transitions in neuronal networks. Within our model, we show that phase transitions separate various dynamical regimes, and we investigate the corresponding bifurcations and critical phenomena. It permits us to suggest a qualitative explanation of the Berger effect, and to investigate phenomena such as avalanches, band-pass filter, and stochastic resonance. The role of modular structure in the detection of weak signals is also discussed. Moreover, we find nonlinear excitations that can describe paroxysmal spikes observed in electroencephalograms from epileptic brains. It allows us to propose a method to predict epileptic seizures. Memory and learning are key functions of the brain. There are evidences that these processes result from dynamical changes in the structure of the brain. At the microscopic level, synaptic connections are plastic and are modified according to the dynamics of neurons. Thus, we generalize our cortical model to take into account synaptic plasticity and we show that the repertoire of dynamical regimes becomes richer. In particular, we find mixed-mode oscillations and a chaotic regime in neuronal network dynamics.