2,944 research outputs found

    Slow nucleic acid unzipping kinetics from sequence-defined barriers

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    Recent experiments on unzipping of RNA helix-loop structures by force have shown that about 40-base molecules can undergo kinetic transitions between two well-defined `open' and `closed' states, on a timescale = 1 sec [Liphardt et al., Science 297, 733-737 (2001)]. Using a simple dynamical model, we show that these phenomena result from the slow kinetics of crossing large free energy barriers which separate the open and closed conformations. The dependence of barriers on sequence along the helix, and on the size of the loop(s) is analyzed. Some DNAs and RNAs sequences that could show dynamics on different time scales, or three(or more)-state unzipping, are proposed.Comment: 8 pages Revtex, including 4 figure

    Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data

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    We introduce a procedure to infer the interactions among a set of binary variables, based on their sampled frequencies and pairwise correlations. The algorithm builds the clusters of variables contributing most to the entropy of the inferred Ising model, and rejects the small contributions due to the sampling noise. Our procedure successfully recovers benchmark Ising models even at criticality and in the low temperature phase, and is applied to neurobiological data.Comment: Accepted for publication in Physical Review Letters (2011

    Large Pseudo-Counts and L2L_2-Norm Penalties Are Necessary for the Mean-Field Inference of Ising and Potts Models

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    Mean field (MF) approximation offers a simple, fast way to infer direct interactions between elements in a network of correlated variables, a common, computationally challenging problem with practical applications in fields ranging from physics and biology to the social sciences. However, MF methods achieve their best performance with strong regularization, well beyond Bayesian expectations, an empirical fact that is poorly understood. In this work, we study the influence of pseudo-count and L2L_2-norm regularization schemes on the quality of inferred Ising or Potts interaction networks from correlation data within the MF approximation. We argue, based on the analysis of small systems, that the optimal value of the regularization strength remains finite even if the sampling noise tends to zero, in order to correct for systematic biases introduced by the MF approximation. Our claim is corroborated by extensive numerical studies of diverse model systems and by the analytical study of the mm-component spin model, for large but finite mm. Additionally we find that pseudo-count regularization is robust against sampling noise, and often outperforms L2L_2-norm regularization, particularly when the underlying network of interactions is strongly heterogeneous. Much better performances are generally obtained for the Ising model than for the Potts model, for which only couplings incoming onto medium-frequency symbols are reliably inferred.Comment: 25 pages, 17 figure

    Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case

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    The complementary strands of DNA molecules can be separated when stretched apart by a force; the unzipping signal is correlated to the base content of the sequence but is affected by thermal and instrumental noise. We consider here the ideal case where opening events are known to a very good time resolution (very large bandwidth), and study how the sequence can be reconstructed from the unzipping data. Our approach relies on the use of statistical Bayesian inference and of Viterbi decoding algorithm. Performances are studied numerically on Monte Carlo generated data, and analytically. We show how multiple unzippings of the same molecule may be exploited to improve the quality of the prediction, and calculate analytically the number of required unzippings as a function of the bandwidth, the sequence content, the elasticity parameters of the unzipped strands

    Different haemodynamic (24-h ambulatory blood-pressure monitoring) and rennin-inhibiting effect of a 1 week treatment with enalapril and lisinopril

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    Ambulatory blood pressure and heart rate monitoring were used for comparing the antihypertensive effect of a 1-week treatment with enalapril and lisinopril 10 mg once daily (double-blind crossover placebo-controlled d y ). Twelve outpatients with mild to moderate hypertension were treated. Both drugs had a significant and identical hypotensive effect. Neither drug affected the diurnal rhythm of blood pressure or heart rate. Therefore the two drugs are equipotent antihypertensive agents. Both drugs inhibited ACE activity to a highly significant extent, but in this regard lisinopril was more effective than enalapril. However, lisinopril's greater ACE inhibition was not accompanied by a greater hypotensive effect. The clinical value of this difference is not yet established

    Unzipping Dynamics of Long DNAs

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    The two strands of the DNA double helix can be `unzipped' by application of 15 pN force. We analyze the dynamics of unzipping and rezipping, for the case where the molecule ends are separated and re-approached at constant velocity. For unzipping of 50 kilobase DNAs at less than about 1000 bases per second, thermal equilibrium-based theory applies. However, for higher unzipping velocities, rotational viscous drag creates a buildup of elastic torque to levels above kBT in the dsDNA region, causing the unzipping force to be well above or well below the equilibrium unzipping force during respectively unzipping and rezipping, in accord with recent experimental results of Thomen et al. [Phys. Rev. Lett. 88, 248102 (2002)]. Our analysis includes the effect of sequence on unzipping and rezipping, and the transient delay in buildup of the unzipping force due to the approach to the steady state.Comment: 15 pages Revtex file including 9 figure
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