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Personne# Alexandre Emmanuel Grandchamp

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The complex mechanisms involved in cellular processes have been increasingly understood this past century and the central role of the DNA molecule has been recognized. The base pair sequence along a DNA fragment is observed not only to encode the genomic information, but also to induce locally very specific physical properties, such as significantly bent or stiff regions. These variations in the molecule constitution are for instance believed to be involved in DNA-protein recognition and in nucleosomes positioning. Modelling the sequence dependent DNA mechanical properties is consequently an important step towards understanding many biological functions. However, in a cell, vastly different length scales are involved, ranging from a few base pairs to several thousands, which makes difficult the definition of \textit{one} appropriate model. A promising strategy seems then to be given by the multi-scale modeling of sequence dependent DNA mechanics. In this framework, the sequence dependent rigid base and rigid base pair models have been proposed. In these coarse grain models either each base pair or each base is described as a rigid body configuration, which leads to either a chain or a bichain representation of the DNA molecule. A sequence dependent configurational distribution has then been parametrized, either from experimental data or directly from atomistic molecular dynamic simulations, and provides an efficient and realistic description at the scale of hundreds of base pairs. Important questions that can be studied in these models are for instance the influence of the sequence on the probability of contact of two sites, which are distant along the molecule length, or on the expectation of the relative configuration of these two sites. In this thesis, we propose to approach these physical situations both from the discrete and the continuum modeling point of view, and then to discuss in which sense they actually constitute only one multi-scale point of view. In the first part, we discuss mechanical properties of heterogeneous rigid body chains and bichains, as well as continuum rod and birods, in classical statics and in equilibrium statistical physics. Equilibirum conditions, variational principles and configurational distributions are studied for single chains and rods, and then extended to bichains and birods. We have introduced in particular an original coordinate free Hamiltonian formulation in arc-length of the birod equilibrium conditions, and the notion of the persistence matrix for the configurational moment for chains and rods. We then present deterministic and stochastic exponential Cauchy-Born rules allowing to bridge the scales between the discrete and continuum representations. In the second part, we present applications of the proposed multi-scale mechanical theory for chains and rods to sequence dependent DNA modelling. We discuss the approximation using the birod model of most probable bichain configurations satisfying prescribed end conditions. Similarly, we then present the computation of the sequence dependent frame correlation matrix and the Flory persistence vector for chains using a continuum rod model. In addition, a homogenization method is proposed. These results are believed to constitute a substantial improvement in the multi-scale modeling of DNA mechanics.

2010

Aurelio Bay, Alexey Boyarsky, Nikolaos Charitonidis, Hans Dijkstra, Sebastian Gerlach, Alexandre Emmanuel Grandchamp, Elena Graverini, Guido Haefeli, Sun Hee Kim, Ekaterina Kuznetsova, Anton Petrov, Federico Leo Redi, Ana Bárbara Rodrigues Cavalcante, Oleg Ruchayskiy, Lesya Shchutska, Sergei Smirnov, Maria Elena Stramaglia, Jean-Loup Tastet

This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHIP experiment will be able to search for new long-lived particles produced in a 400 GeV/c SPS proton beam dump and which travel distances between fifty metres and tens of kilometers. The SHiP detector needs to operate under ultra-low background conditions and requires large simulated samples of muon induced background processes. Through the use of Generative Adversarial Networks it is possible to emulate the simulation of the interaction of 400 GeV/c proton beams with the SHiP target, an otherwise computationally intensive process. For the simulation requirements of the SHiP experiment, generative networks are capable of approximating the full simulation of the dense fixed target, offering a speed increase by a factor of O(10(6)). To evaluate the performance of such an approach, comparisons of the distributions of reconstructed muon momenta in SHiP's spectrometer between samples using the full simulation and samples produced through generative models are presented. The methods discussed in this paper can be generalised and applied to modelling any non-discrete multi-dimensional distribution.