Bayesian estimation of Pseudomonas aeruginosa viscoelastic properties based on creep responses of wild type, rugose, and mucoid variant biofilms.

Pseudomonas aeruginosa biofilms are relevant for a variety of disease settings, including pulmonary infections in people with cystic fibrosis. Biofilms are initiated by individual bacteria that undergo a phenotypic switch and produce an extracellular polymeric slime (EPS). However, the viscoelastic characteristics of biofilms at different stages of formation and the contributions of different EPS constituents have not been fully explored. For this purpose, we develop and parameterize a mathematical model to study the rheological behavior of three biofilms - P. aeruginosa wild type PAO1, isogenic rugose small colony variant (RSCV), and mucoid variant biofilms against a range of experimental data. Using Bayesian inference to estimate these viscoelastic properties, we quantify the rheological characteristics of the biofilm EPS. We employ a Monte Carlo Markov Chain algorithm to estimate these properties of P. aeruginosa variant biofilms in comparison to those of wild type. This information helps us understand the rheological behavior of biofilms at different stages of their development. The mechanical properties of wild type biofilms change significantly over time and are more sensitive to small changes in their composition than the other two mutants.

Short-Term Antiretroviral Treatment Recommendations Based on Sensitivity Analysis of a Mathematical Model for HIV Infection of CD4âºΤ Cells.

HIV infection is one of the most difficult infections to control and manage. The most recent recommendations to control this infection vary according to the guidelines used (US, European, WHO) and are not patient-specific. Unfortunately, no two individuals respond to infection and treatment quite the same way. The purpose of this paper is to make use of the uncertainty and sensitivity analysis to investigate possible short-term treatment options that are patient-specific. We are able to identify the most significant parameters that are responsible for ART outcome and to formulate some insights into the ART success.

Sensitivity Analysis of a Pharmacokinetic Model of Vaginal Anti-HIV Microbicide Drug Delivery.

Uncertainties in parameter values in microbicide pharmacokinetics (PK) models confound the models' use in understanding the determinants of drug delivery and in designing and interpreting dosing and sampling in PK studies. A global sensitivity analysis (Sobol' indices) was performed for a compartmental model of the pharmacokinetics of gel delivery of tenofovir to the vaginal mucosa. The model's parameter space was explored to quantify model output sensitivities to parameters characterizing properties for the gel-drug product (volume, drug transport, initial loading) and host environment (thicknesses of the mucosal epithelium and stroma and the role of ambient vaginal fluid in diluting gel). Greatest sensitivities overall were to the initial drug concentration in gel, gel-epithelium partition coefficient for drug, and rate constant for gel dilution by vaginal fluid. Sensitivities for 3 PK measures of drug concentration values were somewhat different than those for the kinetic PK measure. Sensitivities in the stromal compartment (where tenofovir acts against host cells) and a simulated biopsy also depended on thicknesses of epithelium and stroma. This methodology and results here contribute an approach to help interpret uncertainties in measures of vaginal microbicide gel properties and their host environment. In turn, this will inform rational gel design and optimization.

Optimal unified combination rule in application of Dempster-Shafer theory to lung cancer radiotherapy dose response outcome analysis.

Our previous study demonstrated the application of the Dempster-Shafer theory of evidence to dose/volume/outcome data analysis. Specifically, it provided Yager's rule to fuse data from different institutions pertaining to radiotherapy pneumonitis versus mean lung dose. The present work is a follow-on study that employs the optimal unified combination rule, which optimizes data similarity among independent sources. Specifically, we construct belief and plausibility functions on the lung cancer radiotherapy dose outcome datasets, and then apply the optimal unified combination rule to obtain combined belief and plausibility, which bound the probabilities of pneumonitis incidence. To estimate the incidence of pneumonitis at any value of mean lung dose, we use the Lyman-Kutcher-Burman (LKB) model to fit the combined belief and plausibility curves. The results show that the optimal unified combination rule yields a narrower uncertainty range (as represented by the belief-plausibility range) than Yager's rule, which is also theoretically proven.

Global sensitivity analysis used to interpret biological experimental results.

Modeling host/pathogen interactions provides insight into immune defects that allow bacteria to overwhelm the host, mechanisms that allow vaccine strategies to be successful, and illusive interactions between immune components that govern the immune response to a challenge. However, even simplified models require a fairly high dimensional parameter space to be explored. Here we use global sensitivity analysis for parameters in a simple model for biofilm infections in mice. The results indicate which parameters are insignificant and are 'frozen' to yield a reduced model. The reduced model replicates the full model with high accuracy, using approximately half of the parameter space. We used the sensitivity to investigate the results of the combined biological and mathematical experiments for osteomyelitis. We are able to identify parts of the compartmentalized immune system that were responsible for each of the experimental outcomes. This model is one example for a technique that can be used generally.