The properties of mechanical systems of rigid bodies subject to unilateral constraints are investigated. In particular, properties of interest for the digital simulation of the motion of such systems are studied. The constraints give rise to discontinuities in the solution. Under general assumptions on the system a unique solution is constructed using the linear complementarity theory of mathematical programming. A numerical method for solution of these problems and generalizations of the constraints studied in this paper are briefly discussed.

Stochastic chemical systems with diffusion are modeled with a reactiondiffusion master equation. On a macroscopic level, the governing equation is a reaction-diffusion equation for the averages of the chemical species. On a mesoscopic level, the master equation for a well stirred chemical system is combined with Brownian motion in space to obtain the reactiondiffusion master equation. The space is covered by an unstructured mesh and the diffusion coefficients on the mesoscale are obtained from a finite element discretization of the Laplace operator on the macroscale. The resulting method is a flexible hybrid algorithm in that the diffusion can be handled either on the meso-or on the macroscale level. The accuracy and the efficiency of the method are illustrated in three numerical examples inspired by molecular biology.

Die Lösungen des Coulombschen Reibungsproblems für starre Körper in zwei Dimensionen werden analysiert. Das bestimmende System von gewöhnlichen Differentialgleichungen und Ungleichungen wird aufgestellt. Beispiele werden vorgelegt, die einige ungewünschte Eigenschaften dieses speziellen Reibungsgesetzes nachweisen. Hinreichende Bedingungen für Existenz und Eindeutigkeit werden mit Hilfe der Theorie der linearen Komplementarität hergeleitet.

The chemical master equation is solved by a hybrid method coupling a macroscopic, deterministic description with a mesoscopic, stochastic model. The molecular species are divided into one subset where the expected values of the number of molecules are computed and one subset with species with a stochastic variation in the number of molecules. The macroscopic equations resemble the reaction rate equations and the probability distribution for the stochastic variables satisfy a master equation. The probability distribution is obtained by the Stochastic Simulation Algorithm due to Gillespie. The equations are coupled via a summation over the mesoscale variables. This summation is approximated by Monte Carlo and Quasi Monte Carlo methods. The error in the approximations is analyzed. The hybrid method is applied to three chemical systems from molecular cell biology.

A numerical method is given for the solution of a system of ordinary differential equations and algebraic, unilateral constraints. The equations govern the motion of a mechanical system of. rigid bodies, where contacts between the bodies are created and disappear in the time interval of interest. The ordinary differential equations are discretized by linear multistep methods. In order to satisfy the constraints, a quadratic programming problem is solved at each time step. The fact that the variation of the objective function is small from step to step is utilized to save computing time. A discrete friction model, based on Coulomb's law of friction and suitable for efficient computation, is proposed for planar problems where dry friction cannot be neglected. The normal forces and the friction forces are the optimal solution to a quadratic programming problem. The methods are tested on four model problems. A data structure and possible generalizations are discussed.

We present a new simulation algorithm that allows for dynamic switching between a mesoscopic and a microscopic modeling framework for stochastic reaction-diffusion kinetics. The more expensive and more accurate microscopic model is used only for those species and in those regions in space where there is reason to believe that a microscopic model is needed to capture the dynamics correctly. The microscopic algorithm is extended to simulation on curved surfaces in order to model reaction and diffusion on membranes. The accuracy of the method on and near a spherical membrane is analyzed and evaluated in a numerical experiment. Two biologically motivated examples are simulated in which the need for microscopic simulation of parts of the system arises for different reasons. First, we apply the method to a model of the phosphorylation reactions in a MAPK signaling cascade where microscale methods are necessary to resolve fast rebinding events. Then a model is considered for transport of a species over a membrane coupled to reactions in the bulk. The new algorithm attains an accuracy similar to a full microscopic simulation by handling critical interactions on the microscale, but at a significantly reduced cost by using the mesoscale framework for most parts of the biological model.

The master equation of chemical reactions is solved by first approximating it by the Fokker-Planck equation. Then this equation is discretized in the state space and time by a finite volume method. The difference between the solution of the master equation and the discretized FokkerPlanck equation is analyzed. The solution of the Fokker-Planck equation is compared to the solution of the master equation obtained with Gillespie's Stochastic Simulation Algorithm (SSA) for problems of interest in the regulation of cell processes. The time dependent and steady state solutions are computed and for equal accuracy in the solutions, the Fokker-Planck approach is more efficient than SSA for low dimensional problems and high accuracy.

We propose an adaptive hybrid method suitable for stochastic simulation of diffusion dominated reaction-diffusion processes. For such systems, simulation of the diffusion requires the predominant part of the computing time. In order to reduce the computational work, the diffusion in parts of the domain is treated macroscopically, in other parts with the tau-leap method and in the remaining parts with Gillespie's stochastic simulation algorithm (SSA) as implemented in the next subvolume method (NSM). The chemical reactions are handled by SSA everywhere in the computational domain. A trajectory of the process is advanced in time by an operator splitting technique and the time steps are chosen adaptively. The spatial adaptation is based on estimates of the errors in the tau-leap method and the macroscopic diffusion. The accuracy and efficiency of the method are demonstrated in examples from molecular biology where the domain is discretized by unstructured meshes.