Sharp bending of double-stranded DNA (dsDNA) plays an essential role in genome structure and function. However, the elastic limit of dsDNA bending remains controversial. Here, we measured the opening rates of small dsDNA loops with contour lengths ranging between 40 and 200 bp using single-molecule Fluorescence Resonance Energy Transfer. The relationship of loop lifetime to loop size revealed a critical transition in bending stress. Above the critical loop size, the loop lifetime changed with loop size in a manner consistent with elastic bending stress, but below it, became less sensitive to loop size, indicative of softened dsDNA. The critical loop size increased from ∼ 60 bp to ∼ 100 bp with the addition of 5 mM magnesium. We show that our result is in quantitative agreement with the kinkable worm-like chain model, and furthermore, can reproduce previously reported looping probabilities of dsDNA over the range between 50 and 200 bp. Our findings shed new light on the energetics of sharply bent dsDNA.

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Progress in Biophysics and Molecular Biology, 14

Probing the elastic limit of DNA bending

Transfer entropy has been used to quantify the directed flow of information between source and target variables in many complex systems While transfer entropy was originally formulated in discrete time, in this paper we provide a framework for considering transfer entropy in continuous time systems, based on Radon-Nikodym derivatives between measures of complete path realizations To describe the information dynamics of individual path realizations, we introduce the pathwise transfer entropy, the expectation of which is the transfer entropy accumulated over a finite time interval We demonstrate that this formalism permits an instantaneous transfer entropy rate These properties are analogous to the behavior of physical quantities defined along paths such as work and heat We use this approach to produce an explicit form for the transfer entropy for pure jump processes, and highlight the simplified form in the specific case of point processes (frequently used in neuroscience to model neural spike trains) Finally, we present two synthetic spiking neuron model examples to exhibit the pertinent features of our formalism, namely, that the information flow for point processes consists of discontinuous jump contributions (at spikes in the target) interrupting a continuously varying contribution (relating to waiting times between target spikes) Numerical schemes based on our formalism promise significant benefits over existing strategies based on discrete time formalisms

Transfer entropy in continuous time, with applications to jump and neural spiking processes

We introduce a new method for inferring effective network structure given a multivariate timeseries of activation levels of the nodes in the network. For each destination node in the network, the method identifies the set of source nodes which can be used to provide the most statistically significant information regarding outcomes of the destination, and are thus inferred as those source information nodes from which the destination is computed. This is done using incrementally conditioned transfer entropy measurements, gradually building the set of source nodes for a destination conditioned on the previously identified sources. Our method is model-free and non-linear, but more importantly it handles multivariate interactions between sources in creating outcomes at destinations, rejects spurious connections for correlated sources, and incorporates measures to avoid combinatorial explosions in the number of source combinations evaluated. We apply the method to probabilistic Boolean networks (serving as models of Gene Regulatory Networks), demonstrating the utility of the method in revealing significant proportions of the underlying structural network given only short time-series of the network dynamics, particularly in comparison to other methods.

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Multivariate construction of effective computational networks from observational data

Atrial fibrillation (AF) is the most common abnormal heart rhythm and the single biggest cause of stroke. Ablation, destroying regions of the atria, is applied largely empirically and can be curative but with a disappointing clinical success rate. We design a simple model of activation wave front propagation on an anisotropic structure mimicking the branching network of heart muscle cells. This integration of phenomenological dynamics and pertinent structure shows how AF emerges spontaneously when the transverse cell-to-cell coupling decreases, as occurs with age, beyond a threshold value. We identify critical regions responsible for the initiation and maintenance of AF, the ablation of which terminates AF. The simplicity of the model allows us to calculate analytically the risk of arrhythmia and express the threshold value of transversal cell-to-cell coupling as a function of the model parameters. This threshold value decreases with increasing refractory period by reducing the number of critical regions which can initiate and sustain microreentrant circuits. These biologically testable predictions might inform ablation therapies and arrhythmic risk assessment.

Progress in Biophysics and Molecular Biology, 14

Citations

Probing the elastic limit of DNA bending

Transfer entropy in continuous time, with applications to jump and neural spiking processes

Multivariate construction of effective computational networks from observational data

Simple model for identifying critical regions in atrial fibrillation.

Membrane potential of phospholipid bilayers: Ion concentration and pH difference