101 research outputs found
Energy landscape picture of supercooled liquids: Application of a generalized random energy model
The thermodynamic and kinetic anomalies of supercooled liquids are analyzed
from the perspective of energy landscapes. A mean field model, a generalized
random energy model of liquids is developed, which exhibits a dynamical
transition of the onset of slow dynamics at T_0, alteration of the nature of
motion from the saddle-to-saddle to minimum-to-minimum motion at T_c, and an
ideal glass transition at T_k. If the energy spectrum of the configurations has
a low energy tail, the model also allows a thermodynamic liquid-liquid
transition at T_l. The liquid-liquid transition of the model is correlated to
the kinetic fragile-strong transition accompanied by the anomalous slowing down
of motion. Fragility of the system is classified in terms of features of the
energy landscape such as ruggedness of the potential energy surface, size of
the cooperative motion invoked in a transition from one configuration to
another, and energy needed to deform the local structure in the cooperative
motion. A simple relation is found between diffusion constant, D and the saddle
index of the potential energy surface, f, as , where a depends
on the size of the cooperative motion.Comment: to appear in J. Chem. Phy
Effects of the DNA state fluctuation on single-cell dynamics of self-regulating gene
A dynamical mean-field theory is developed to analyze stochastic single-cell
dynamics of gene expression. By explicitly taking account of nonequilibrium and
nonadiabatic features of the DNA state fluctuation, two-time correlation
functions and response functions of single-cell dynamics are derived. The
method is applied to a self-regulating gene to predict a rich variety of
dynamical phenomena such as anomalous increase of relaxation time and
oscillatory decay of correlations. Effective "temperature" defined as the ratio
of the correlation to the response in the protein number is small when the DNA
state change is frequent, while it grows large when the DNA state change is
infrequent, indicating the strong enhancement of noise in the latter case.Comment: 18 pages, 5 figure
Organization of fast and slow chromatin revealed by single-nucleosome dynamics
Understanding chromatin organization and dynamics is important since they
crucially affect DNA functions. In this study, we investigate chromatin
dynamics by statistically analyzing single-nucleosome movement in living human
cells. Bi-modal nature of the mean squared displacement distribution of
nucleosomes allows for a natural categorization of the nucleosomes as fast and
slow. Analyses of the nucleosome-nucleosome correlation functions within these
categories along with the density of vibrational modes show that the
nucleosomes form dynamically correlated fluid regions, i.e., dynamic domains of
fast and slow nucleosomes. Perturbed nucleosome dynamics by global histone
acetylation or cohesin inactivation indicate that nucleosome-nucleosome
interactions along with tethering of chromatin chains organize nucleosomes into
fast and slow dynamic domains. A simple polymer model is introduced, which
shows the consistency of this dynamic domain picture. Statistical analyses of
single-nucleosome movement provide rich information on how chromatin is
dynamically organized in a fluid manner in living cells
Testing the transition state theory in stochastic dynamics of a genetic switch
Stochastic dynamics of chemical reactions in a mutually repressing two-gene
circuit is numerically simulated. The circuit has a rich variety of different
states when the kinetic change of DNA status is slow. The stochastic switching
transition between those states are compared with the theoretical estimation of
the switching rate derived from the idea similar to the transition state
theory. Even though the circuit is kept far from equilibrium, the method gives
a consistent explanation of the switching kinetics for a wide range of
parameters. The transition state theory-like estimation, however, fails to
describe transitions involving the state which has the extremely small numbers
of protein molecules
Stable stochastic dynamics in yeast cell cycle
Chemical reactions in cell are subject to intense stochastic fluctuations. An
important question is how the fundamental physiological behavior of cell is
kept stable against those noisy perturbations. In this paper a stochastic model
of cell cycle of budding yeast is constructed to analyze the effects of noise
on the cell cycle oscillation. The model predicts intense noise in levels of
mRNAs and proteins, and the simulated protein levels explain the observed
statistical tendency of noise in populations of synchronous and asynchronous
cells. In spite of intense noise in levels of proteins and mRNAs, cell cycle is
stable enough to bring the largely perturbed cells back to the physiological
cyclic oscillation. The model shows that consecutively appearing fixed points
are the origin of this stability of cell cycle.Comment: main text, 2 supporting texts, 3 supplementary table
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