Chiral symmetry analysis and rigid rotational invariance for the lattice dynamics of single-wall carbon nanotubes

We provide a detailed expression of the vibrational potential for the lattice dynamics of single-wall carbon nanotubes (SWCNT's) satisfying the requirements of the exact rigid translational as well as rotational symmetries, which is a nontrivial generalization of the valence force model for the planar graphene sheet. With the model, the low-frequency behavior of the dispersion of the acoustic modes as well as the flexure mode can be precisely calculated. Based upon a comprehensive chiral symmetry analysis, the calculated mode frequencies (including all the Raman- and infrared-active modes), velocities of acoustic modes, and the polarization vectors are systematically fitted in terms of the chiral angle and radius, where the restrictions of various symmetry operations of SWCNT's are fulfilled.

Exploring the mechanism of flexible biomolecular recognition with single molecule dynamics

Combining a single-molecule study of protein binding with a coarse grained molecular dynamics model including solvent (water molecules) effects, we find that biomolecular recognition is determined by flexibilities in addition to structures. Our single-molecule study shows that binding of CBD (a fragment of Wiskott-Aldrich syndrome protein) to Cdc42 involves bound and loosely bound states, which can be quantitatively explained in our model as a result of binding with large conformational changes. Our model identified certain key residues for binding consistent with mutational experiments. Our study reveals the role of flexibility and a new scenario of dimeric binding between the monomers: first bind and then fold.

Single molecule dynamics and statistical fluctuations of gene regulatory networks: A repressilator

The authors developed a time dependent method to study the single molecule dynamics of a simple gene regulatory network: a repressilator with three genes mutually repressing each other. They quantitatively characterize the time evolution dynamics of the repressilator. Furthermore, they study purely dynamical issues such as statistical fluctuations and noise evolution. They illustrated some important features of the biological network such as monostability, spirals, and limit cycle oscillation. Explicit time dependent Fano factors which describe noise evolution and show statistical fluctuations out of equilibrium can be significant and far from the Poisson distribution. They explore the phase space and the interrelationships among fluctuations, order, amplitude, and period of oscillations of the repressilators. The authors found that repressilators follow ordered limit cycle orbits and are more likely to appear in the lower fluctuating regions. The amplitude of the repressilators increases as the suppressing of the genes decreases and production of proteins increases. The oscillation period of the repressilators decreases as the suppressing of the genes decreases and production of proteins increases.

Diffusion and single molecule dynamics on biomolecular interface binding energy landscape

We propose a new approach to study the diffusion dynamics on biomolecular interface binding energy landscape. The resulting mean first passage time (MFPT) has 'U'curve dependence on the temperature. It is shown that the large specificity ratio of gap to roughness of the underlying binding energy landscape not only guarantees the thermodynamic stability and the specificity [P.A. Rejto, G.M. Verkhivker, in: Proc. Natl. Acad. Sci. 93 (1996) 8945; C.J. Tsai, S. Kumar, B. Ma, R. Nussinov, Protein Sci. 8 (1999) 1181; G.A. Papoian, P.G. Wolynes, Biopolymers 68 (2003) 333; J. Wang, G.M. Verkhivker, Phys. Rev. Lett. 90 (2003) 198101] but also the kinetic accessibility. The complex kinetics and the associated fluctuations reflecting the structures of the binding energy landscape emerge upon temperature changes. The theory suggests a way of connecting the models/simulations with single molecule experiments by analysing the kinetic trajectories.

Single-molecule dynamics reveals cooperative binding-folding in protein recognition

The study of associations between two biomolecules is the key to understanding molecular function and recognition. Molecular function is often thought to be determined by underlying structures. Here, combining a single-molecule study of protein binding with an energy-landscape-inspired microscopic model, we found strong evidence that biomolecular recognition is determined by flexibilities in addition to structures. Our model is based on coarse-grained molecular dynamics on the residue level with the energy function biased toward the native binding structure ( the Go model). With our model, the underlying free-energy landscape of the binding can be explored. There are two distinct conformational states at the free-energy minimum, one with partial folding of CBD itself and significant interface binding of CBD to Cdc42, and the other with native folding of CBD itself and native interface binding of CBD to Cdc42. This shows that the binding process proceeds with a significant interface binding of CBD with Cdc42 first, without a complete folding of CBD itself, and that binding and folding are then coupled to reach the native binding state.

Diffusion dynamics, moments, and distribution of first-passage time on the protein-folding energy landscape, with applications to single molecules

We study the dynamics of protein folding via statistical energy-landscape theory. In particular, we concentrate on the local-connectivity case with the folding progress described by the fraction of native conformations. We found that the first passage-time (FPT) distribution undergoes a dynamic transition at a temperature below which the FPT distribution develops a power-law tail, a signature of the intermittent nonexponential kinetic phenomena for the folding dynamics. Possible applications to single-molecule dynamics experiments are discussed.

Transmission of single HIV-1 genomes and dynamics of early immune escape revealed by ultra-deep sequencing.

We used ultra-deep sequencing to obtain tens of thousands of HIV-1 sequences from regions targeted by CD8+ T lymphocytes from longitudinal samples from three acutely infected subjects, and modeled viral evolution during the critical first weeks of infection. Previous studies suggested that a single virus established productive infection, but these conclusions were tempered because of limited sampling; now, we have greatly increased our confidence in this observation through modeling the observed earliest sample diversity based on vastly more extensive sampling. Conventional sequencing of HIV-1 from acute/early infection has shown different patterns of escape at different epitopes; we investigated the earliest escapes in exquisite detail. Over 3-6 weeks, ultradeep sequencing revealed that the virus explored an extraordinary array of potential escape routes in the process of evading the earliest CD8 T-lymphocyte responses--using 454 sequencing, we identified over 50 variant forms of each targeted epitope during early immune escape, while only 2-7 variants were detected in the same samples via conventional sequencing. In contrast to the diversity seen within epitopes, non-epitope regions, including the Envelope V3 region, which was sequenced as a control in each subject, displayed very low levels of variation. In early infection, in the regions sequenced, the consensus forms did not have a fitness advantage large enough to trigger reversion to consensus amino acids in the absence of immune pressure. In one subject, a genetic bottleneck was observed, with extensive diversity at the second time point narrowing to two dominant escape forms by the third time point, all within two months of infection. Traces of immune escape were observed in the earliest samples, suggesting that immune pressure is present and effective earlier than previously reported; quantifying the loss rate of the founder virus suggests a direct role for CD8 T-lymphocyte responses in viral containment after peak viremia. Dramatic shifts in the frequencies of epitope variants during the first weeks of infection revealed a complex interplay between viral fitness and immune escape.

An Analysis of the Effect of Electric Vehicle Charging on the Operation of the Single Electricity Market

To meet European Union renewable energy and greenhouse gas emissions reduction targets the Irish government set a target in 2008 that 10% of all vehicles in the transport fleet be powered by electricity by 2020. Similar electric vehicle targets have been introduced in other countries. However, reducing energy consumption and decreasing greenhouse gas emissions in transport is a considerable challenge due to heavy reliance on fossil fuels. In fact, transport in the Republic of Ireland in 2009 accounted for 29% of non-emissions trading scheme greenhouse gas emissions, 32% of energy-related greenhouse gas emissions, 21% of total greenhouse gas emissions and approximately 50% of energy-related non-emission trading scheme greenhouse gas emissions. In this paper the effect of electric vehicle charging on the operation of the single wholesale electricity market for the Republic of Ireland and Northern Ireland is analysed. The energy consumed, greenhouse gas emissions generated and changes to the wholesale price of electricity under peak and off-peak charging scenarios are quantified and discussed. Results from the study show that off-peak charging is more beneficial than peak charging.

Molecular dynamics simulation model for the quantitative assessment of tool wear during single point diamond turning of cubic silicon carbide

Silicon carbide (SiC) is a material of great technological interest for engineering applications concerning hostile environments where silicon-based components cannot work (beyond 623 K). Single point diamond turning (SPDT) has remained a superior and viable method to harness process efficiency and freeform shapes on this harder material. However, it is extremely difficult to machine this ceramic consistently in the ductile regime due to sudden and rapid tool wear. It thus becomes non trivial to develop an accurate understanding of tool wear mechanism during SPDT of SiC in order to identify measures to suppress wear to minimize operational cost.

In this paper, molecular dynamics (MD) simulation has been deployed with a realistic analytical bond order potential (ABOP) formalism based potential energy function to understand tool wear mechanism during single point diamond turning of SiC. The most significant result was obtained using the radial distribution function which suggests graphitization of diamond tool during the machining process. This phenomenon occurs due to the abrasive processes between these two ultra hard materials. The abrasive action results in locally high temperature which compounds with the massive cutting forces leading to sp3–sp2 order–disorder transition of diamond tool. This represents the root cause of tool wear during SPDT operation of cubic SiC. Further testing led to the development of a novel method for quantitative assessment of the progression of diamond tool wear from MD simulations.

Nanoindentation of polysilicon and single crystal silicon: Molecular dynamics simulation and experimental validation

This paper presents novel advances on the deformation behaviour of polycrystalline and single crystal silicon using molecular dynamics (MD) simulation and validation of the same via nanoindentation experiments. In order to unravel the mechanism of deformation, four simulations were performed: Indentation of polycrystalline silicon substrate with a (i) Berkovich pyramidal and a (ii) spherical (arc) indenter, and indentation of a single crystal silicon substrate with these two indenters. The simulation results reveal that high pressure phase transformation (HPPT) in silicon (Si-I to Si-II phase transformation) occurred in all cases, however, its extent and the manner in which it occurred differed significantly between polycrystalline silicon and single crystal silicon, and was the main driver of differences in nanoindentation deformation behaviour between the two types of silicon. An interesting observation was that in polycrystalline silicon, the HPPT was observed to occur preferentially along the grain boundaries than across the grain boundaries. An automated dislocation extraction algorithm (DXA) revealed no dislocations in the deformation zone, suggesting HPPT to be the primary mechanism in inducing plasticity in silicon.

Influence of temperature on the anisotropic cutting behaviour of single crystal silicon: A molecular dynamics simulation investigation

Using molecular dynamics (MD) simulation, this paper investigates anisotropic cutting behaviour of single crystal silicon in vacuum under a wide range of substrate temperatures (300 K, 500 K, 750 K, 850 K, 1173 K and 1500 K). Specific cutting energy, force ratio, stress in the cutting zone and cutting temperature were the indicators used to quantify the differences in the cutting behaviour of silicon. A key observation was that the specific cutting energy required to cut the (111) surface of silicon and the von Mises stress to yield the silicon reduces by 25% and 32%, respectively, at 1173 K compared to what is required at 300 K. The room temperature cutting anisotropy in the von Mises stress and the room temperature cutting anisotropy in the specific cutting energy (work done by the tool in removing unit volume of material) were obtained as 12% and 16% respectively. It was observed that this changes to 20% and 40%, respectively, when cutting was performed at 1500 K, signifying a very strong correlation between the anisotropy observed during cutting and the machining temperature. Furthermore, using the atomic strain criterion, the width of primary shear zone was found to vary with the orientation of workpiece surface and temperature i.e. it remains narrower while cutting the (111) surface of silicon or at higher machining temperatures. A major anecdote of the study based on the potential function employed in the study is that, irrespective of the cutting plane or the cutting temperature, the state of the cutting edge of the diamond tool did not show direct diamond to graphitic phase transformation.

Modeling entangled dynamics: comparison between stochastic single-chain and multichain models

To test the effectiveness of stochastic single-chain models in describing the dynamics of entangled polymers, we systematically compare one such model; the slip-spring model; to a multichain model solved using stochastic molecular dynamics(MD) simulations (the Kremer-Grest model). The comparison involves investigating if the single-chain model can adequately describe both a microscopic dynamical and a macroscopic rheological quantity for a range of chain lengths. Choosing a particular chain length in the slip-spring model, the parameter values that best reproduce the mean-square displacement of a group of monomers is determined by fitting toMDdata. Using the same set of parameters we then test if the predictions of the mean-square displacements for other chain lengths agree with the MD calculations. We followed this by a comparison of the time dependent stress relaxation moduli obtained from the two models for a range of chain lengths. After identifying a limitation of the original slip-spring model in describing the static structure of the polymer chain as seen in MD, we remedy this by introducing a pairwise repulsive potential between the monomers in the chains. Poor agreement of the mean-square monomer displacements at short times can be rectified by the use of generalized Langevin equations for the dynamics and resulted in significantly improved agreement.

Adaptation-induced collective dynamics of a single-cell protozoan

We investigate the behavior of a single-cell protozoan in a narrow tubular ring. This environment forces them to swim under a one-dimensional periodic boundary condition. Above a critical density, single-cell protozoa aggregate spontaneously without external stimulation. The high-density zone of swimming cells exhibits a characteristic collective dynamics including translation and boundary fluctuation. We analyzed the velocity distribution and turn rate of swimming cells and found that the regulation of the turing rate leads to a stable aggregation and that acceleration of velocity triggers instability of aggregation. These two opposing effects may help to explain the spontaneous dynamics of collective behavior. We also propose a stochastic model for the mechanism underlying the collective behavior of swimming cells.