Biomedical subjects

Allan R Willms

Publications and source records attributed to Allan R Willms.

3 recordsLinked to original sources

PubMed2006

Static recipient cells as reservoirs of antibiotic resistance during antibiotic therapy.

How does taking the full course of antibiotics prevent antibiotic resistant bacteria establishing in patients? We address this question by testing the possibility that horizontal/lateral gene transfer (HGT) is critical for the accumulation of the antibiotic-resistance phenotype while bacteria are under antibiotic stress. Most antibiotics prevent bacterial reproduction, some by preventing de novo gene expression. Nevertheless, in some cases and at some concentrations, the effects of most antibiotics on gene expression may not be irreversible. If the stress is removed before the bacteria are cleared from the patients by normal turnover, gene expression restarts, converting the residual population to phenotypic resistance. Using mathematical models we investigate how static recipients of resistance genes carried by plasmids accumulate resistance genes, and how specifically an environment cycling between presence and absence of the antibiotic uniquely favors the evolution of horizontally mobile resistance genes. We found that the presence of static recipients can substantially increase the persistence of the plasmid and that this effect is most pronounced when the cost of carriage of the plasmid decreases the cell's growth rate by as much as a half or more. In addition, plasmid persistence can be enhanced even when conjugation rates are as low as half the rate required for the plasmid to persist as a parasite on its own.

Anti-Bacterial Agents↗

PubMed2006

An efficient dynamic system for real-time robot-path planning.

This paper presents a simple yet efficient dynamic-programming (DP) shortest path algorithm for real-time collision-free robot-path planning applicable to situations in which targets and barriers are permitted to move. The algorithm works in real time and requires no prior knowledge of target or barrier movements. In the case that the barriers are stationary, this paper proves that this algorithm always results in the robot catching the target, provided it moves at a greater speed than the target, and the dynamic-system update frequency is sufficiently large. Like most robot-path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points from which it receives information in real time. The information stored at each point is a current estimate of the distance to the nearest target and the neighbor from which this distance was determined. Updating the distance estimate at each grid point is done using only the information gathered from the point's neighbors, that is, each point can be considered an independent processor, and the order in which grid points are updated is not determined based on global knowledge of the current distances at each point or the previous history of each point. The robot path is determined in real time completely from the information at the robot's current grid-point location. The computational effort to update each point is minimal, allowing for rapid propagation of the distance information outward along the grid from the target locations. In the static situation, where both the targets and the barriers do not move, this algorithm is a DP solution to the shortest path problem, but is restricted by lack of global knowledge. In this case, this paper proves that the dynamic system converges in a small number of iterations to a state where the minimal distance to a target is recorded at each grid point and shows that this robot-path-planning algorithm can be made to always choose an optimal path. The effectiveness of this algorithm is demonstrated through a number of simulations.

Algorithms↗

PubMed2002

NEUROFIT: software for fitting Hodgkin-Huxley models to voltage-clamp data.

I introduce publicly available software for accurate fitting of Hodgkin-Huxley models to voltage-clamp data. I describe the model and non-linear fitting procedure employed by the software and compare its results with the usual method of fitting such models using potassium A-current data from a pyloric dilator cell of the lobster Panulirus interruptus and sodium current data from an electrocyte cell of the electric fish Sternopygus macrurus. The set of parameter values for the model determined by this software yield current traces that are substantially closer to the observed data than those determined from the usual fitting method. This improvement is due to the fact that the software fits all of the parameters simultaneously utilizing all of the data rather than fitting steady-state and time constant parameters disjointly using peak currents and portions of the rising and falling phases. I analyze the convergence properties of the software's fitting algorithm using simulated data showing that accurate parameter values are obtained for most of the parameters using any reasonable initial values. The software also incorporates a linear pre-estimation procedure to help in determining reasonable initial values for the full non-linear algorithm. I illustrate and discuss some of the inadequacies of voltage-clamp data.

Algorithms↗