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This work considers the problem of calculating an interval-valued state estimate for a nonlinear system subject to bounded inputs and measurement errors. Such state estimators are often called interval observers. Interval observers can be constructed using methods from reachability theory. Recent advances in the construction of interval enclosures of reachable sets for nonlinear systems inspire the present work. These advances can incorporate constraints on the states to produce tighter interval enclosures. When applied to the state estimation problem, bounded-error measurements may be used as state constraints in these new theories. The result is a method that is easily implementable and which generally produces better, tighter interval state estimates. Furthermore, a novel linear programming-based method is proposed for calculating the observer gain, which must be tuned in practice. In contrast with previous approaches, this method does not rely on special system structure. The new approaches are demonstrated with numerical examples., Comment: 15 pages, 2 figures

The use of approximate solution techniques for the Chemical Master Equation is common practice for the analysis of stochastic chemical systems. Despite their widespread use, however, many such techniques rely on unverifiable assumptions and only few provide mechanisms to control the approximation error quantitatively. Addressing this gap, Dowdy and Barton [The Journal of Chemical Physics, 149(7), 074103 (2018)] proposed a method for the computation of guaranteed bounds on the moment trajectories associated with stochastic chemical systems described by the Chemical Master Equation, thereby providing a general framework for error quantification. Here, we present an extension of this method. The key contribution is a new hierarchy of convex necessary moment conditions crucially reflecting the temporal causality and other regularity conditions that are inherent to the moment trajectories associated with stochastic processes described by the Chemical Master Equation. Analogous to the original method, these conditions generate a hierarchy of semidefinite programs that furnishes monotonically improving bounds on the trajectories of the moments and related statistics. Compared to its predecessor, the presented hierarchy produces bounds that are at least as tight and it often enables the computation of dramatically tighter bounds as it enjoys superior scaling properties and the generated semidefinite programs are highly structured. We analyze the properties of the presented hierarchy, discuss some aspects of its practical implementation and demonstrate its merits with several examples., Comment: corrected typos and added implementation details

Applying the method of moments to the chemical master equation (CME) appearing in stochastic chemical kinetics often leads to the so-called closure problem. Recently, several authors showed that this problem can be partially overcome using moment-based semidefinite programs (SDPs). In particular, they showed that moment-based SDPs can be used to calculate rigorous bounds on various descriptions of the stochastic chemical kinetic system's stationary distribution(s) -- for example, mean molecular counts, variances in these counts, and so on. In this paper, we show that these ideas can be extended to the corresponding dynamic problem, calculating time-varying bounds on the same descriptions.

Flux balance analysis has proven an effective tool for analyzing metabolic networks. In flux balance analysis, reaction rates and optimal pathways are ascertained by solving a linear program, in which the growth rate is maximized subject to mass-balance constraints. A variety of cell functions in response to environmental stimuli can be quantified using flux balance analysis by parameterizing the linear program with respect to extracellular conditions. However, for most large, genome-scale metabolic networks of practical interest, the resulting parametric problem has multiple and highly degenerate optimal solutions, which are computationally challenging to handle. An improved multi-parametric programming algorithm based on active-set methods is introduced in this paper to overcome these computational difficulties. Degeneracy and multiplicity are handled, respectively, by introducing generalized inverses and auxiliary objective functions into the formulation of the optimality conditions. These improvements are especially effective for metabolic networks because their stoichiometry matrices are generally sparse; thus, fast and efficient algorithms from sparse linear algebra can be leveraged to compute generalized inverses and null-space bases. We illustrate the application of our algorithm to flux balance analysis of metabolic networks by studying a reduced metabolic model of Corynebacterium glutamicum and a genome-scale model of Escherichia coli. We then demonstrate how the critical regions resulting from these studies can be associated with optimal metabolic modes and discuss the physical relevance of optimal pathways arising from various auxiliary objective functions. Achieving more than five-fold improvement in computational speed over existing multi-parametric programming tools, the proposed algorithm proves promising in handling genome-scale metabolic models., Comment: Accepted in J. Optim. Theory Appl. First draft was submitted on August 4th, 2017

An optimal control strategy is developed to construct nanostructures of desired geometry along line segments by means of directed self-assembly of charged particles. Such a control strategy determines the electric potentials of a set of electrodes located at fixed points in the line segment. The particles move under the electric forces generated by these electrodes and by the interactions between the particles themselves to form a desired pattern eventually. Due to technology limitations, the particle positions cannot be measured during the course of control, so that the control is open-loop in nature. Such an open-loop control optimally changes the electrode potentials in time in order to create a desired pattern with the highest probability, despite the inherent uncertainty in the initial positions and the dynamical behaviors of the particles. Two models are proposed to describe the uncertain dynamics of the particles: a continuous model relying on a set of nonlinear stochastic differential equations, and a discrete Ising model consisting of a large dimensional continuous-time Markov chain. While the first model is more mathematically tractable, the second one more precisely describes particles at the nanometer scale. The control design procedure begins with the continuous model and identifies the structure of its stable equilibria, which is used later to propose a piecewise constant structure for the control and to demonstrate that the optimal value of each piece is independently obtained from a certain static optimization problem. It is shown next that the design procedure can be applied to the discrete model with only minor modifications. A numerical example of control design is presented.