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: http://www.mccme.ru/~ansobol/seminaire_jeune.html
Дата изменения: Tue Mar 8 20:05:07 2011 Дата индексирования: Tue Oct 2 00:50:19 2012 Кодировка: Поисковые слова: barnard 68 |
We mostly meet at the premises of Laboratoire Jean-Victore Poncelet at the Independent University of Moscow (access instructions in Russian are here).
The seminar has a dedicated Google group, poncelet-seminar-jeune (in Russian, in spite of its French name).
Sergei NECHAEV, P. N. Lebedev Physics Institute & LPTMS, Orsay: Combinatorics of locally free groups and statistical physics
We will talk on word enumeration in a “locally free group”, which approximates the braid group and give a nontrivial estimate on the growth rate of irreducible words in the braid group. This problem will be related to the evaluation of partition function for “lattice animals.”
Olga Valba, PhysTech & LPTMS, Orsay: From sequence comparison algorithms to the determination of pairing energy between two RNAs.
Noncoding RNAs are those RNAs that do not encode proteins. However they have other important functions, such as regulation of gene expression. A non-coding RNA may pair with a matrix RNA, thus stopping translation from the latter. We present an algorithm to determine the pairing energy of two RNAs in the most general setting, where each RNA may form “cactus-like” structures. An algorithm for reconstruction of the ground state of two paired RNAs will also be described.
Olga STETIUKHINA, MSU Math. & Mech. Dept & N. N. Semenov Institute for Chemical Physics: Correspondence between random walks on an ultrametric discrete lattice and on the p-adic line
Behaviour of complex biological systems, such as protein structure and dynamics, can often be modeled with ultrametric random processes. The corresponding analytical approaches employ a p-adic equation of ultrametric diffusion whereas numerical approaches emphasize a random walk on ultrametric discrete lattice. In this talk we present discrete and contiunuos descriptions of ultrametric random walk or diffusion. All the necessary background from p-adic analysis will be introduced in the course of exposition.
Dmitry USHAKOV, MSU Psychology Dept: Some [neural] network models of the human cognitive system.
Anna BODROVA, MSU Physics Dept: Size distribution of particles in the rings of Saturn.
The rings of Saturn are comprised with microscopic particles of ice dust as well as with boulders as big as a house. The size distribution of these objects is described with a power law for small scales, while the large scales show exponential decay. This distribution can be obtained analytically from a kinetic model for hard spheres that can coalesce or undergo fission upon collisions.
Alexander MIKHAILOV, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin: Understanding protein machines.
Misha TAMM, MSU Physics Dept: A queue model (continued).
Misha TAMM, MSU Physics Dept: A queue model (continued).
Misha TAMM, MSU Physics Dept: A queue model.
We consider a one-dimensional model of a driven motion which differs from the original ASEP (asymmetric simple exclusion process) in a sense that the moving particles have two internal states - ground (unable to move) and excited (ready to move). Thus, before a particle moves it has to be excited, and after a move the excitation is relaxed. The important feature of the model, which makes it essentially different from the conventional ASEP is that in a case of failed (due to a traffic jam) movement attempt the particle's excitation does not relax.
Gregory KUCHEROV, LIFL & Laboratoire J.-V. Poncelet: On the distribution of word frequencies in genomes.
We observe that the distribution of occurrence numbers of k-words (k-mers) in genomic sequences cannot be explained by Bernoulli ou Markov distributions, usually used in bioinformatics to model DNA sequences. I will speculate on causes and consequences of this observation and propose a probability law that fits well to the observed distribution.
Olga VALBA, PhysTech: Statistical comparison algorithms for RNA-like macromolecules.
Alexei SHKARIN, PhysTech: Statistical properties of random hierarchical networks.