A finite state projection algorithm for the stationary solution of the chemical master equation

The chemical master equation (CME) is frequently used in systems biology to quantify the effects of stochastic fluctuations that arise due to biomolecular species with low copy numbers. The CME is a system of ordinary differential equations that describes the evolution of probability density for each population vector in the state-space of the stochastic reaction dynamics. For many examples of interest, this state-space is infinite, making it difficult to obtain exact solutions of the CME. To deal with this problem, the Finite State Projection (FSP) algorithm was developed by Munsky and Khammash (Jour. Chem. Phys. 2006), to provide approximate solutions to the CME by truncating the state-space. The FSP works well for finite time-periods but it cannot be used for estimating the stationary solutions of CMEs, which are often of interest in systems biology. The aim of this paper is to develop a version of FSP which we refer to as the stationary FSP (sFSP) that allows one to obtain accurate approximations of the stationary solutions of a CME by solving a finite linear-algebraic system that yields the stationary distribution of a continuous-time Markov chain over the truncated state-space. We derive bounds for the approximation error incurred by sFSP and we establish that under certain stability conditions, these errors can be made arbitrarily small by appropriately expanding the truncated state-space. We provide several examples to illustrate our sFSP method and demonstrate its efficiency in estimating the stationary distributions. In particular, we show that using a quantised tensor train (QTT) implementation of our sFSP method, problems admitting more than 100 million states can be efficiently solved.Comment: 8 figure

A finite state projection algorithm for the stationary solution of the chemical master equation

The chemical master equation (CME) is frequently used in systems biology to quantify the effects of stochastic fluctuations that arise due to biomolecular species with low copy numbers. The CME is a system of ordinary differential equations that describes the evolution of probability density for each population vector in the state-space of the stochastic reaction dynamics. For many examples of interest, this state-space is infinite, making it difficult to obtain exact solutions of the CME. To deal with this problem, the Finite State Projection (FSP) algorithm was developed by Munsky and Khammash (Jour. Chem. Phys. 2006), to provide approximate solutions to the CME by truncating the state-space. The FSP works well for finite time-periods but it cannot be used for estimating the stationary solutions of CMEs, which are often of interest in systems biology. The aim of this paper is to develop a version of FSP which we refer to as the stationary FSP (sFSP) that allows one to obtain accurate approximations of the stationary solutions of a CME by solving a finite linear-algebraic system that yields the stationary distribution of a continuous-time Markov chain over the truncated state-space. We derive bounds for the approximation error incurred by sFSP and we establish that under certain stability conditions, these errors can be made arbitrarily small by appropriately expanding the truncated state-space. We provide several examples to illustrate our sFSP method and demonstrate its efficiency in estimating the stationary distributions. In particular, we show that using a quantised tensor train (QTT) implementation of our sFSP method, problems admitting more than 100 million states can be efficiently solved.Comment: 8 figure

Submodularity of Energy Related Controllability Metrics

The quantification of controllability and observability has recently received new interest in the context of large, complex networks of dynamical systems. A fundamental but computationally difficult problem is the placement or selection of actuators and sensors that optimize real-valued controllability and observability metrics of the network. We show that several classes of energy related metrics associated with the controllability Gramian in linear dynamical systems have a strong structural property, called submodularity. This property allows for an approximation guarantee by using a simple greedy heuristic for their maximization. The results are illustrated for randomly generated systems and for placement of power electronic actuators in a model of the European power grid.Comment: 7 pages, 2 figures; submitted to the 2014 IEEE Conference on Decision and Contro

A literature survey of low-rank tensor approximation techniques

During the last years, low-rank tensor approximation has been established as a new tool in scientific computing to address large-scale linear and multilinear algebra problems, which would be intractable by classical techniques. This survey attempts to give a literature overview of current developments in this area, with an emphasis on function-related tensors

Computational Tools for Large-Scale Linear Systems

While the theoretical analysis of linear dynamical systems with finite state-spaces is a mature topic, in situations where the underlying model has a large number of dimensions, modelers must turn to computational tools to better visualize and analyze the dynamic behavior of interest. In these situations, we are confronted with the Curse of Dimensionality: computational and storage complexity grows exponentially in the number of dimensions. This doctoral project focuses on two main classes of large-scale linear systems which arise in system biology. The Chemical Master Equation (CME) is a Fokker-Planck equation which describes the evolution of the probability mass function of a countable state space Markov process. Each state of the CME is labelled with an ordered S-tuple corresponding to one configuration of a well-mixed chemical system, where S is the number of distinct chemical species of interest. Even in cases where one only considers a projection of the CME to a finite subset of the states, one still must contend with the Curse of Dimensionality: the computational complexity grows exponentially in the number of chemical species. This dissertation describes a computational methodology for efficient solution of the CME which, in the best cases, will scale linearly in the number of chemical species. The second main class of high-dimensional problems requiring computational tools are coupled linear reaction-diffusion equations. For this class of models, we focus primarily on the computation of certain high-dimensional matrices which describe in a quantitative sense the input-to-state and state-to-output relationships. We describe algorithms for extracting useful information stored in these matrices and use this information to efficiently compute both reduced order models and open-loop control laws for steering the full system. A key feature of this approach is that the method is completely simulation or experiment free, in fact, in our numerical experiments, the computation of a reduced model or open-loop control law is an order of magnitude faster on a laptop than simulation of the full system on a 32 core node of a high-performance cluster. In both projects, the enabling computational technology is the recently proposed Tensor Train (TT) structured low-parametric representation of high-dimensional data. The TT-format effectively exploits low-rank structure of the "unfolding matrices" for compression and computational efficiency. Formally, the computational complexity of basic TT arithmetics scale linearly in the number of dimensions, potentially circumventing the curse of dimensionality. To demonstrate the effectiveness of this approach, we performed numerous numerical experiments whose results are reported here

Geometric methods on low-rank matrix and tensor manifolds

In this chapter we present numerical methods for low-rank matrix and tensor problems that explicitly make use of the geometry of rank constrained matrix and tensor spaces. We focus on two types of problems: The first are optimization problems, like matrix and tensor completion, solving linear systems and eigenvalue problems. Such problems can be solved by numerical optimization for manifolds, called Riemannian optimization methods. We will explain the basic elements of differential geometry in order to apply such methods efficiently to rank constrained matrix and tensor spaces. The second type of problem is ordinary differential equations, defined on matrix and tensor spaces. We show how their solution can be approximated by the dynamical low-rank principle, and discuss several numerical integrators that rely in an essential way on geometric properties that are characteristic to sets of low rank matrices and tensors

Second International Workshop on Harmonic Oscillators

The Second International Workshop on Harmonic Oscillators was held at the Hotel Hacienda Cocoyoc from March 23 to 25, 1994. The Workshop gathered 67 participants; there were 10 invited lecturers, 30 plenary oral presentations, 15 posters, and plenty of discussion divided into the five sessions of this volume. The Organizing Committee was asked by the chairman of several Mexican funding agencies what exactly was meant by harmonic oscillators, and for what purpose the new research could be useful. Harmonic oscillators - as we explained - is a code name for a family of mathematical models based on the theory of Lie algebras and groups, with applications in a growing range of physical theories and technologies: molecular, atomic, nuclear and particle physics; quantum optics and communication theory

Nonlinear Waves in Bose-Einstein Condensates: Physical Relevance and Mathematical Techniques

The aim of the present review is to introduce the reader to some of the physical notions and of the mathematical methods that are relevant to the study of nonlinear waves in Bose-Einstein Condensates (BECs). Upon introducing the general framework, we discuss the prototypical models that are relevant to this setting for different dimensions and different potentials confining the atoms. We analyze some of the model properties and explore their typical wave solutions (plane wave solutions, bright, dark, gap solitons, as well as vortices). We then offer a collection of mathematical methods that can be used to understand the existence, stability and dynamics of nonlinear waves in such BECs, either directly or starting from different types of limits (e.g., the linear or the nonlinear limit, or the discrete limit of the corresponding equation). Finally, we consider some special topics involving more recent developments, and experimental setups in which there is still considerable need for developing mathematical as well as computational tools.Comment: 69 pages, 10 figures, to appear in Nonlinearity, 2008. V2: new references added, fixed typo