Showing results 1661 to 1680 on 1936 in total
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When humans reason about functional structures of RNA, they speak of long hairpins with miRNA precursors, of clover leaf structures with tRNA, of neighouring hairpins with attenuators, and so on. Most of the time, we do not care about individual base pairs or helix sizes, while the overall arrangment of helices and loops really matters.RNA folding programs, however, used to be ignorant of abstraction in RNA, deceiving us with a single, minimum free energy structure, or overwhelming us with a plethora of near-optimal structures, most of which are quite similar and therefore redundant.RNA shape abstraction alleviates this situation. RNA shapes are abstract structure images, retaining adjacency and nesting of structural features, but disregarding size information. Shape abstraction integrates well with dynamic programming algorithms, and hence it can be applied during structure prediction rather than afterwards. This avoids exponential explosion in the near-optimal folding space, and provides a non-heuristic and complete account of an RNA molecule's structural inclinations. Quite magically, some long-studied problems become easy.In the presentation, I will shortly review the notion of abstract shapes. I will then discuss several applications of this concept, including a highly effective filtering method when working with structural classes of RNA described by covariance models, such as provided by the Rfam data base.
Thèse de Stéphanie Periquet - Jeudi 10 juillet 2014 - 14:30 - Salle de conférence de la Bibliothèque - La Doua
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Among biologist as well as linguists, it is now widely accepted that there are many striking parallels between the evolution of life forms and the history of languages. Starting from the rise of language studies as a scientific discipline in the early 19th century, up to today's recent "quantitative turn" in historical linguistics, scholars from both disciplines have repeatedly pointed to similarities between the respective research objects in biology and linguistics. During the last two decades, this has lead to a new school of ''quantitative historical linguistics''. Based on the key assumption that the characteristic processes of language change and biological evolution are so similar that the methods designed for one discipline may also be used in the other one, methods which were originally designed to study biological evolution (methods for phylogenetic reconstruction, sequence alignment, or biological network analysis) have now repeatedly been applied to linguistic data.Unfortunately, not all analogies which have been made between evolutionary processes in linguistics and biology reflect true similarities in the processes. Striking differences between the research objects of both disciplines are often ignored. In the talk, I will review proposed similarities between evolutionary processes in the two disciplines and discuss their methodological implications.
Living organisms are complex systems, and stressing them with toxicants only increases the complexity. In ecotoxicology, the common strategy for addressing toxic effects is to accept this complexity and provide descriptions of parts of the response of the system. Such descriptions will not advance our understanding and cannot address the problems of environmental risk assessment. Complexity is of course not unique for ecotoxicology. In related disciplines such as environmental chemistry, the common approach is to simplify the system to its bare essence and study the behaviour of the simplification. Something similar does exist for toxic effects, which can be placed under the designation "toxicokinetic-toxicodynamic" (TKTD) modelling. Toxicokinetics deals with the uptake of chemicals into the organism, whereas toxicodynamics addresses the relationship between internal concentrations and effects over time. In this presentation, I will focus on toxicodynamic models, and discuss how biology can be radically simplified to suit our purpose. Furthermore, I demonstrate how experimental data can be analysed, and discuss the statistical problems associated with fitting the models to data.
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In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a "simple" program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002585