Biomedical subjects

Linda Petzold

Publications and source records attributed to Linda Petzold.

3 recordsLinked to original sources

PubMed2006

A Monte Carlo simulation study of lipid bilayer formation on hydrophilic substrates from vesicle solutions.

A general lattice Monte Carlo model is used for simulating the formation of supported lipid bilayers (SLBs) from vesicle solutions. The model, based on a previously published paper, consists of adsorption, decomposition, and lateral diffusion steps, and is derived from fundamental physical interactions and mass transport principles. The Monte Carlo simulation results are fit to experimental data at different vesicle bulk concentrations. A sensitivity analysis reveals that the process strongly depends on the bulk concentration C(0), adsorption rate constant K, and all vesicle radii parameters. A measure of "quality of coverage" is proposed. By this measure, the quality of the formed bilayers is found to increase with vesicle bulk concentration.

Adsorption↗

PubMed2005

Sensitivity analysis of discrete stochastic systems.

Sensitivity analysis quantifies the dependence of system behavior on the parameters that affect the process dynamics. Classical sensitivity analysis, however, does not directly apply to discrete stochastic dynamical systems, which have recently gained popularity because of its relevance in the simulation of biological processes. In this work, sensitivity analysis for discrete stochastic processes is developed based on density function (distribution) sensitivity, using an analog of the classical sensitivity and the Fisher Information Matrix. There exist many circumstances, such as in systems with multistability, in which the stochastic effects become nontrivial and classical sensitivity analysis on the deterministic representation of a system cannot adequately capture the true system behavior. The proposed analysis is applied to a bistable chemical system--the Schlögl model, and to a synthetic genetic toggle-switch model. Comparisons between the stochastic and deterministic analyses show the significance of explicit consideration of the probabilistic nature in the sensitivity analysis for this class of processes.

Algorithms↗

PubMed2004

Efficient formulation of the stochastic simulation algorithm for chemically reacting systems.

In this paper we examine the different formulations of Gillespie's stochastic simulation algorithm (SSA) [D. Gillespie, J. Phys. Chem. 81, 2340 (1977)] with respect to computational efficiency, and propose an optimization to improve the efficiency of the direct method. Based on careful timing studies and an analysis of the time-consuming operations, we conclude that for most practical problems the optimized direct method is the most efficient formulation of SSA. This is in contrast to the widely held belief that Gibson and Bruck's next reaction method [M. Gibson and J. Bruck, J. Phys. Chem. A 104, 1876 (2000)] is the best way to implement the SSA for large systems. Our analysis explains the source of the discrepancy.

Journal Article↗