770 research outputs found
Solving the chemical master equation using sliding windows
<p>Abstract</p> <p>Background</p> <p>The chemical master equation (CME) is a system of ordinary differential equations that describes the evolution of a network of chemical reactions as a stochastic process. Its solution yields the probability density vector of the system at each point in time. Solving the CME numerically is in many cases computationally expensive or even infeasible as the number of reachable states can be very large or infinite. We introduce the sliding window method, which computes an approximate solution of the CME by performing a sequence of local analysis steps. In each step, only a manageable subset of states is considered, representing a "window" into the state space. In subsequent steps, the window follows the direction in which the probability mass moves, until the time period of interest has elapsed. We construct the window based on a deterministic approximation of the future behavior of the system by estimating upper and lower bounds on the populations of the chemical species.</p> <p>Results</p> <p>In order to show the effectiveness of our approach, we apply it to several examples previously described in the literature. The experimental results show that the proposed method speeds up the analysis considerably, compared to a global analysis, while still providing high accuracy.</p> <p>Conclusions</p> <p>The sliding window method is a novel approach to address the performance problems of numerical algorithms for the solution of the chemical master equation. The method efficiently approximates the probability distributions at the time points of interest for a variety of chemically reacting systems, including systems for which no upper bound on the population sizes of the chemical species is known a priori.</p
Model reduction of controlled Fokker--Planck and Liouville-von Neumann equations
Model reduction methods for bilinear control systems are compared by means of
practical examples of Liouville-von Neumann and Fokker--Planck type. Methods
based on balancing generalized system Gramians and on minimizing an H2-type
cost functional are considered. The focus is on the numerical implementation
and a thorough comparison of the methods. Structure and stability preservation
are investigated, and the competitiveness of the approaches is shown for
practically relevant, large-scale examples
Numerical Integration of the Master Equation in Some Models of Stochastic Epidemiology
The processes by which disease spreads in a population of individuals are inherently stochastic. The master equation has proven to be a useful tool for modeling such processes. Unfortunately, solving the master equation analytically is possible only in limited cases (e.g., when the model is linear), and thus numerical procedures or approximation methods must be employed. Available approximation methods, such as the system size expansion method of van Kampen, may fail to provide reliable solutions, whereas current numerical approaches can induce appreciable computational cost. In this paper, we propose a new numerical technique for solving the master equation. Our method is based on a more informative stochastic process than the population process commonly used in the literature. By exploiting the structure of the master equation governing this process, we develop a novel technique for calculating the exact solution of the master equation – up to a desired precision – in certain models of stochastic epidemiology. We demonstrate the potential of our method by solving the master equation associated with the stochastic SIR epidemic model. MATLAB software that implements the methods discussed in this paper is freely available as Supporting Information S1
A correlated-polaron electronic propagator: open electronic dynamics beyond the Born-Oppenheimer approximation
In this work we develop a theory of correlated many-electron dynamics dressed
by the presence of a finite-temperature harmonic bath. The theory is based on
the ab-initio Hamiltonian, and thus well-defined apart from any
phenomenological choice of collective basis states or electronic coupling
model. The equation-of-motion includes some bath effects non-perturbatively,
and can be used to simulate line- shapes beyond the Markovian approximation and
open electronic dynamics which are subjects of renewed recent interest. Energy
conversion and transport depend critically on the ratio of electron-electron
coupling to bath-electron coupling, which is a fitted parameter if a
phenomenological basis of many-electron states is used to develop an electronic
equation of motion. Since the present work doesn't appeal to any such basis, it
avoids this ambiguity. The new theory produces a level of detail beyond the
adiabatic Born-Oppenheimer states, but with cost scaling like the
Born-Oppenheimer approach. While developing this model we have also applied the
time-convolutionless perturbation theory to correlated molecular excitations
for the first time. Resonant response properties are given by the formalism
without phenomenological parameters. Example propagations with a developmental
code are given demonstrating the treatment of electron-correlation in
absorption spectra, vibronic structure, and decay in an open system.Comment: 25 pages 7 figure
Markovian Dynamics on Complex Reaction Networks
Complex networks, comprised of individual elements that interact with each
other through reaction channels, are ubiquitous across many scientific and
engineering disciplines. Examples include biochemical, pharmacokinetic,
epidemiological, ecological, social, neural, and multi-agent networks. A common
approach to modeling such networks is by a master equation that governs the
dynamic evolution of the joint probability mass function of the underling
population process and naturally leads to Markovian dynamics for such process.
Due however to the nonlinear nature of most reactions, the computation and
analysis of the resulting stochastic population dynamics is a difficult task.
This review article provides a coherent and comprehensive coverage of recently
developed approaches and methods to tackle this problem. After reviewing a
general framework for modeling Markovian reaction networks and giving specific
examples, the authors present numerical and computational techniques capable of
evaluating or approximating the solution of the master equation, discuss a
recently developed approach for studying the stationary behavior of Markovian
reaction networks using a potential energy landscape perspective, and provide
an introduction to the emerging theory of thermodynamic analysis of such
networks. Three representative problems of opinion formation, transcription
regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see
http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm
Coarse Grained Computations for a Micellar System
We establish, through coarse-grained computation, a connection between
traditional, continuum numerical algorithms (initial value problems as well as
fixed point algorithms) and atomistic simulations of the Larson model of
micelle formation. The procedure hinges on the (expected) evolution of a few
slow, coarse-grained mesoscopic observables of the MC simulation, and on
(computational) time scale separation between these and the remaining "slaved",
fast variables. Short bursts of appropriately initialized atomistic simulation
are used to estimate the (coarse-grained, deterministic) local dynamics of the
evolution of the observables. These estimates are then in turn used to
accelerate the evolution to computational stationarity through traditional
continuum algorithms (forward Euler integration, Newton-Raphson fixed point
computation). This "equation-free" framework, bypassing the derivation of
explicit, closed equations for the observables (e.g. equations of state) may
provide a computational bridge between direct atomistic / stochastic simulation
and the analysis of its macroscopic, system-level consequences
- …