The ability of cells to react to changes in their environment is critical to their survival. Effective decision making strategies leading to the activation of specific intracellular pathways hinge on cells sensing and processing extracellular variation. We will only be able to understand and manipulate how cells make decisions if we understand the “design” of the mechanisms that enable them to make such decisions, in terms of how they function, and in terms of their limitations and architecture. In this thesis, using mathematical modelling, I revisited classical signal sensing and transduction mechanisms in light of recent developments in methodological approaches and data collection. I studied the sensing characteristics of one of the simplest of sensors, the allosteric sensor, to understand the limits and effectiveness of its “design”. Using the classical Monod-Wyman-Changeux model of allostery, I defined and evaluated six engineering-inspired characteristics as a function of the parameters and number of sensors. I found that specifying one characteristic strongly constrains others and I determined the trade-offs that follow from these constraints. I also calculated the probability distribution of the number of input molecules that maximizes information transfer and, as a consequence, the number of environmental states a given population of sensors can discriminate between. Next, I proposed a new general model of phosphorylation cycles that can account for experimental reports of ultrasensitivity occurring in regimes that are far away from Goldbeter and Koshland’s zero-order saturation, the classical ultrasensitivity-generating mechanism. The new model exhibits robust ultrasensitivity in “anti-zero-order” regimes. The degree of ultrasensitivity, its limits, and its dependence on the parameters of the system are analytically tractable. The model can, additionally, explain in an intuitive way some puzzling experimental observations. Finally, I addressed the problem of integrating different types of signals from multiple sources, and performed some preliminary exploration of how cells can “learn” to associate and dissociate correlated signals in non-evolutionary time-scales. This work provides insights into the function and robustness of signal sensing and transduction mechanisms and as such is applicable to both the study of endogenous systems and the design of synthetic ones.

We present new spatially resolved observations of MWC 349A from the Large Binocular Telescope Interferometer (LBTI), a 23 m baseline interferometer made up of two, co-mounted 8 m telescopes. MWC 349A is a B[e] star with an unknown evolutionary state. Proposed scenarios range from a young stellar object, to a B[e] supergiant, to a tight binary system. Radio continuum and recombination line observations of this source revealed a sub-arcsecond bipolar outflow surrounding an similar to 100 mas circumstellar disk. Follow-up infrared studies detected the disk, and suggested that it may have skew and an inner clearing. Our new infrared interferometric observations, which have more than twice the resolution of previously published data sets, support the presence of both skew and a compact infrared excess. They rule out inner clearings with radii greater than similar to 14 mas. We show the improvements in disk parameter constraints provided by LBTI, and discuss the inferred disk parameters in the context of the posited evolutionary states for MWC 349A.

Cette thèse traite de la conception de récepteurs radiofréquences basés sur l'acquisition compressée pour de l'estimation paramétrique en radio cognitive.L'acquisition compressée est un changement de paradigme dans la conversion analogique-numérique qui permet de s'affranchir de la fréquence d'échantillonnage de Nyquist.Dans ces travaux, les estimations sont effectuées directement sur les échantillons compressés vu le coût prohibitif de la reconstruction du signal d'entrée.Tout d'abord, l'aspect architecture du récepteur est abordé,avec notamment le choix des codes de mélange pour le convertisseur modulé à large bande (MWC).Une analyse haut niveau des propriétés de la matrice d'acquisition, à savoir la cohérence pour réduire le nombre de mesures et l'isométrie pour la robustesse au bruit,est menée puis validée par une plateforme de simulation.Enfin l'estimation paramétrique à partir des échantillons compressés est abordée à travers la borne de Cramér-Rao sur la variance d'un estimateur non biaisé.Une forme analytique de la matrice de Fisher est établie sous certaines hypothèses et permet de dissocier les effets de la compression et de la création de diversité.L'influence du processus d'acquisition compressée, notamment le couplage entre paramètres et la fuite spectrale, est illustré par l'exemple. / This work deals with the topic of radiofrequency receivers based on Compressive Sampling for feature extraction in Cognitive Radio.Compressive Sampling is a paradigm shift in analog to digital conversion that bypasses the Nyquist sampling frequency.In this work, estimations are carried out directly on the compressed samples due to the prohibitive cost of signal reconstruction.First, the receiver architecture is considered, in particular through the choice of the mixing codes of the Modulated Wideband Converter.A high-level analysis on properties of the sensing matrix, coherence to reduce the number of measurement and isometry for noise robustness,is carried out and validated by a simulation platform.Finally, parametric estimation based on compressed samples is tackled through the prism of the Cram'{e}r-Rao lower bound on unbiased estimators.A closed form expression of the Fisher matrix is established under certain assumptions and enables to dissociate the effects of compression and diversity creation.The influence of Compressive Sampling on estimation bounds, in particular coupling between parameters and spectral leakage, is illustrated by the example.

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To explore spectral opportunities in wideband regime for cognitive radio we need a wideband spectrum sensing receiver. Current wideband receiver architectures need wideband analog to digital converter (ADC) to sample wideband signal. As current state-of-art ADC has limitation in terms of power and sampling rate, we need to explore some alternative solutions. Compressive sampling (CS) data acquisition method is one of the solutions. Cognitive Radio signal, which is sparse in frequency domain can be sampled at Sub-Nyquist rate using low rate ADC. To relax the receiver complexity in terms of performance requirement we can use Modulated Wideband Converter (MWC) architecture, a Sub-Nyquist sampling method. In this thesis circuit design of this architecture covers signal within a frequency range of 500 MHz to 2.1 GHz, with a channel bandwidth of 1600 MHz. By using 8 parallel lines with channel trading factor of 11, effective sampling rate of 550 MHz is achieved for successful support recovery of multi-band input signal of size N=12.