11,080 research outputs found
On controlling stochastic sensitivity of oscillatory systems
For a nonlinear oscillatory stochastic system, we study the control problem for the variance of random trajectories around a deterministic cycle. To describe the range of random trajectories, we use the method of stochastic sensitivity functions. We consider the problem of designing a given stochastic sensitivity function, discuss problems of controllability and reachability. Complete stochastic controllability is only possible when the control's dimension coincides with the system's dimension. Otherwise, the design problem becomes ill-posed. To solve it, we propose a regularization method that lets us produce a given stochastic sensitivity function with any given precision. The efficiency of the proposed approach is demonstrated with the example of controlling stochastic oscillations in a brusselator model. © 2013 Pleiades Publishing, Ltd
Noise control and utility: From regulatory network to spatial patterning
Stochasticity (or noise) at cellular and molecular levels has been observed
extensively as a universal feature for living systems. However, how living
systems deal with noise while performing desirable biological functions remains
a major mystery. Regulatory network configurations, such as their topology and
timescale, are shown to be critical in attenuating noise, and noise is also
found to facilitate cell fate decision. Here we review major recent findings on
noise attenuation through regulatory control, the benefit of noise via
noise-induced cellular plasticity during developmental patterning, and
summarize key principles underlying noise control
Stochastic neural network models for gene regulatory networks
Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale gene expression data sets is still in the very early developmental stage. In this paper we present some stochastic models by introducing stochastic processes into neural network models that can describe intermediate regulation for large scale gene networks. Poisson random variables are used to represent chance events in the processes of synthesis and degradation. For expression data with normalized concentrations, exponential or normal random variables are used to realize fluctuations. Using a network with three genes, we show how to use stochastic simulations for studying robustness and stability properties of gene expression patterns under the influence of noise, and how to use stochastic models to predict statistical distributions of expression levels in population of cells. The discussion suggest that stochastic neural network models can give better description of gene regulatory networks and provide criteria for measuring the reasonableness o mathematical models
Quantum calcium-ion interactions with EEG
Previous papers have developed a statistical mechanics of neocortical
interactions (SMNI) fit to short-term memory and EEG data. Adaptive Simulated
Annealing (ASA) has been developed to perform fits to such nonlinear stochastic
systems. An N-dimensional path-integral algorithm for quantum systems,
qPATHINT, has been developed from classical PATHINT. Both fold short-time
propagators (distributions or wave functions) over long times. Previous papers
applied qPATHINT to two systems, in neocortical interactions and financial
options. \textbf{Objective}: In this paper the quantum path-integral for
Calcium ions is used to derive a closed-form analytic solution at arbitrary
time that is used to calculate interactions with classical-physics SMNI
interactions among scales. Using fits of this SMNI model to EEG data, including
these effects, will help determine if this is a reasonable approach.
\textbf{Method}: Methods of mathematical-physics for optimization and for path
integrals in classical and quantum spaces are used for this project. Studies
using supercomputer resources tested various dimensions for their scaling
limits. In this paper the quantum path-integral is used to derive a closed-form
analytic solution at arbitrary time that is used to calculate interactions with
classical-physics SMNI interactions among scales. \textbf{Results}: The
mathematical-physics and computer parts of the study are successful, in that
there is modest improvement of cost/objective functions used to fit EEG data
using these models. \textbf{Conclusion}: This project points to directions for
more detailed calculations using more EEG data and qPATHINT at each time slice
to propagate quantum calcium waves, synchronized with PATHINT propagation of
classical SMNI.Comment: published in Sc
Nonlinear Protein Degradation and the Function of Genetic Circuits
The functions of most genetic circuits require sufficient degrees of
cooperativity in the circuit components. While mechanisms of cooperativity have
been studied most extensively in the context of transcriptional initiation
control, cooperativity from other processes involved in the operation of the
circuits can also play important roles. In this study, we examine a simple
kinetic source of cooperativity stemming from the nonlinear degradation of
multimeric proteins. Ample experimental evidence suggests that protein subunits
can degrade less rapidly when associated in multimeric complexes, an effect we
refer to as cooperative stability. For dimeric transcription factors, this
effect leads to a concentration-dependence in the degradation rate because
monomers, which are predominant at low concentrations, will be more rapidly
degraded. Thus cooperative stability can effectively widen the accessible range
of protein levels in vivo. Through theoretical analysis of two exemplary
genetic circuits in bacteria, we show that such an increased range is important
for the robust operation of genetic circuits as well as their evolvability. Our
calculations demonstrate that a few-fold difference between the degradation
rate of monomers and dimers can already enhance the function of these circuits
substantially. These results suggest that cooperative stability needs to be
considered explicitly and characterized quantitatively in any systematic
experimental or theoretical study of gene circuits.Comment: 42 pages, 10 figure
Mean Field Control for Efficient Mixing of Energy Loads
We pose an engineering challenge of controlling an Ensemble of Energy Devices
via coordinated, implementation-light and randomized on/off switching as a
problem in Non-Equilibrium Statistical Mechanics. We show that Mean Field
Control} with nonlinear feedback on the cumulative consumption, assumed
available to the aggregator via direct physical measurements of the energy
flow, allows the ensemble to recover from its use in the Demand Response
regime, i.e. transition to a statistical steady state, significantly faster
than in the case of the fixed feedback. Moreover when the nonlinearity is
sufficiently strong, one observes the phenomenon of "super-relaxation" -- where
the total instantaneous energy consumption of the ensemble transitions to the
steady state much faster than the underlying probability distribution of the
devices over their state space, while also leaving almost no devices outside of
the comfort zone.Comment: 7 pages, 5 figure
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