We present a method for computing thermodynamically feasible elementary flux modes (tEFMs) using equilibrium constants without need of internal metabolite concentrations. The method is compared with the method based on a binary distinction between reversible and irreversible reactions. When all reactions are reversible, adding the constraints based on equilibrium constants reduces the number of elementary flux modes (EFMs) by a factor of two. Declaring in advance some reactions as irreversible, based on reliable biochemical expertise, can in general reduce the number of EFMs by a greater factor. But, even in this case, computing tEFMs can rule out some EFMs which are biochemically irrelevant. We applied our method to two published models described with binary distinction: the monosaccharide metabolism and the central carbon metabolism of Chinese hamster ovary cells. The results show that the binary distinction is in good agreement with biochemical observations. Moreover, the suppression of the EFMs that are not consistent with the equilibrium constants appears to be biologically relevant.

To understand the phenotypic capabilities of organisms, it is useful to characterise cellular metabolism through the analysis of its pathways. Dynamic mathematical modelling of metabolic networks is of high interest as it provides the time evolution of the metabolic components. However, it also has limitations, such as the necessary mechanistic details and kinetic parameters are not always available. On the other hand, large metabolic networks exhibit a complex topological structure which can be studied rather efficiently in their stationary regime by constraint-based methods. These methods produce useful predictions on pathway operations. In this review, we present both modelling techniques and we show how they bring complementary views of metabolism. In particular, we show on a simple example how both approaches can be used in conjunction to shed some light on the dynamics of metabolic networks.

Thickness uniformity of the silicon top layer is extremely important for the next generation of CMOS electronics based on Fully-Depleted Silicon-On-Insulator (FD-SOI) wafers. It requires accurate thickness characterization over a wide spatial bandwidth, from transistor to wafer scales [10 nm-300 mm]. We propose a data processing method using one-dimensional Power Spectral Density functions to describe both roughness and thickness variations of thin layers over the bandwidth of interest. The study of the correlation between thickness and roughness of the silicon top layer of SOI wafers leads to the distinction of three spectral domains: high-frequencies, where thickness variations are larger than roughness variations, mid-frequencies, where thickness and roughness variations are comparable, and low-frequencies, where the top layer conformably follows the buried oxide roughness. Furthermore, the spectral analysis of thickness variations, points out fractal behaviors over three regions (scaling exponent 0.3, 1.2 and 0.2 respectively). These regions provide the spectral foot-prints of fracture propagation [1.10⁻⁵−2.10⁻³ μm⁻¹], surface self-diffusion [0.3−1 μm⁻¹] and sacrificial oxidation [1−10 μm⁻¹] respectively, all phenomena involved in fabrication of SOI wafers by the ion-cut process. Additionally, over the mid-frequencies region [0.03−1 μm⁻¹], we observe a “bump” which results from the depth and size-distribution of micro-cracks generated during the ion-implantation step.

L’objectif de cette thèse est d’étudier comment la cellule mammifère adapte son métabolisme aux étapes du cycle cellulaire. Le cycle cellulaire est l’ensemble des étapes menant une cellule à se diviser. Le rôle du métabolisme est de fournir à la cellule les éléments et l’énergie dont elle a besoin pour fonctionner. En particulier, à chaque étape du cycle cellulaire, la cellule a besoin de différents éléments pour pouvoir, à terme, se diviser correctement. Il est donc crucial pour la cellule de coordonner le métabolisme et le cycle cellulaire et en particulier de contrôler ce que le métabolisme produit au cours du cycle cellulaire. Pour mieux comprendre ce lien entre ces deux processus, nous avons étudié comment un modèle mathématique du métabolisme répondait à différentes variations imposées par le cycle cellulaire et nous avons comparé ces réponses à la littérature. Satisfaits des résultats obtenus, nous avons alors construit un modèle hybride représentant l’évolution du métabolisme au cours du cycle cellulaire. Nous retrouvons dans ce modèle hybride les grandes variations connues des voies métaboliques au cours des phases du cycle cellulaire ainsi que des variations expérimentales des métabolites énergétiques et redox. Encouragés par ces résultats, nous avons finalement perturbé notre modèle hybride pour retrouver des tendances du métabolisme dues au cancer, un ensemble de maladies touchant à la fois le cycle cellulaire et le métabolisme.