342 research outputs found
State Differentiation by Transient Truncation in Coupled Threshold Dynamics
Dynamics with a threshold input--output relation commonly exist in gene,
signal-transduction, and neural networks. Coupled dynamical systems of such
threshold elements are investigated, in an effort to find differentiation of
elements induced by the interaction. Through global diffusive coupling, novel
states are found to be generated that are not the original attractor of
single-element threshold dynamics, but are sustained through the interaction
with the elements located at the original attractor. This stabilization of the
novel state(s) is not related to symmetry breaking, but is explained as the
truncation of transient trajectories to the original attractor due to the
coupling. Single-element dynamics with winding transient trajectories located
at a low-dimensional manifold and having turning points are shown to be
essential to the generation of such novel state(s) in a coupled system.
Universality of this mechanism for the novel state generation and its relevance
to biological cell differentiation are briefly discussed.Comment: 8 pages. Phys. Rev. E. in pres
Magic number 7 2 in networks of threshold dynamics
Information processing by random feed-forward networks consisting of units
with sigmoidal input-output response is studied by focusing on the dependence
of its outputs on the number of parallel paths M. It is found that the system
leads to a combination of on/off outputs when , while for , chaotic dynamics arises, resulting in a continuous distribution of
outputs. This universality of the critical number is explained by
combinatorial explosion, i.e., dominance of factorial over exponential
increase. Relevance of the result to the psychological magic number
is briefly discussed.Comment: 6 pages, 5 figure
A variational approach to the stochastic aspects of cellular signal transduction
Cellular signaling networks have evolved to cope with intrinsic fluctuations,
coming from the small numbers of constituents, and the environmental noise.
Stochastic chemical kinetics equations govern the way biochemical networks
process noisy signals. The essential difficulty associated with the master
equation approach to solving the stochastic chemical kinetics problem is the
enormous number of ordinary differential equations involved. In this work, we
show how to achieve tremendous reduction in the dimensionality of specific
reaction cascade dynamics by solving variationally an equivalent quantum field
theoretic formulation of stochastic chemical kinetics. The present formulation
avoids cumbersome commutator computations in the derivation of evolution
equations, making more transparent the physical significance of the variational
method. We propose novel time-dependent basis functions which work well over a
wide range of rate parameters. We apply the new basis functions to describe
stochastic signaling in several enzymatic cascades and compare the results so
obtained with those from alternative solution techniques. The variational
ansatz gives probability distributions that agree well with the exact ones,
even when fluctuations are large and discreteness and nonlinearity are
important. A numerical implementation of our technique is many orders of
magnitude more efficient computationally compared with the traditional Monte
Carlo simulation algorithms or the Langevin simulations.Comment: 15 pages, 11 figure
Correction to: Differentiation of RPE cells from integration-free iPS cells and their cell biological characterization.
The original article [1] contains an error in the legend of Fig 5 whereby the descriptions for panels 5d and 5e are incorrect; as such, the corrected legend can be viewed below with its respective figure images
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