Acid production in dental plaque after exposure to probiotic bacteria

<p>Abstract</p> <p>Background</p> <p>The increasing interest in probiotic lactobacilli in health maintenance has raised the question of potential risks. One possible side effect could be an increased acidogenicity in dental plaque. The aim of this study was to investigate the effect of probiotic lactobacilli on plaque lactic acid (LA) production <it>in vitro</it> and <it>in vivo</it>.</p> <p>Methods</p> <p>In the first part (A), suspensions of two lactobacilli strains (<it>L. reuteri</it> DSM 17938<it>, L. plantarum</it> 299v) were added to suspensions of supragingival dental plaque collected from healthy young adults (n=25). LA production after fermentation with either xylitol or fructose was analyzed. In the second part (B), subjects (n=18) were given lozenges with probiotic lactobacilli (<it>L. reuteri</it> DSM 17938 and ATCC PTA 5289) or placebo for two weeks in a double-blinded, randomized cross-over trial. The concentration of LA in supragingival plaque samples was determined at baseline and after 2 weeks. Salivary counts of mutans streptococci (MS) and lactobacilli were estimated with chair-side methods.</p> <p>Results</p> <p>Plaque suspensions with <it>L. reuteri</it> DSM 17938 produced significantly less LA compared with <it>L. plantarum</it> 299v or controls (p<0.05). Fructose gave higher LA concentrations than xylitol. In part B, there were no significant differences in LA production between baseline and follow up in any of the groups and no differences between test and placebo were displayed. The salivary MS counts were not significantly altered during the intervention but the lactobacilli counts increased significantly in the test group (p<0.05).</p> <p>Conclusion</p> <p>Lactic acid production in suspensions of plaque and probiotic lactobacilli was strain-dependant and the present study provides no evidence of an increase in plaque acidity by the supply of selected probiotic lactobacilli when challenged by fructose or xylitol. The study protocol was approved by The Danish National Committee on Biomedical Research Ethics (protocol no H-2-2010-112).</p> <p>Trial registration</p> <p>NCT01700712</p

Systematic calibration of a cell signaling network model

Background: Mathematical modeling is being applied to increasingly complex biological systems and datasets; however, the process of analyzing and calibrating against experimental data is often challenging and a rate limiting step in model development. To address this problem, we developed a systematic methodology for calibrating quantitative models of dynamic biological processes and illustrate its utility by validating a model of TRAIL (Tumor necrosis factor Related Apoptosis-Inducing Ligand)-induced cell death. Results: We propose a serial framework integrating analysis and calibration modules and we compare various methods for global sensitivity analysis and global parameter estimation. First, adequacy of the network structure is checked by global sensitivity analysis to changes in concentrations of molecular species, validating that the model can reproduce qualitative features of the system behavior derived from experiments or literature surveys. Second, rate parameters are ranked by importance using gradient-based and variance-based sensitivity indices, and we systematically determine the optimal number of parameters to include in model calibration. Third, deterministic, stochastic and hybrid algorithms for global optimization are applied to estimate the values of the most important parameters by fitting to time series data. We compare the performance of these three optimization algorithms. Conclusions: Our proposed framework covers the entire process from validating a proto-model to establishing a realistic model for in silico experiments and thereby provides a generalized workflow for the construction of predictive models of complex network systems

Optimizing radial basis functions by D.C. programming and its use in direct search for global derivative-free optimization

In this paper we address the global optimization of functions subject to bound and linear constraints without using derivatives of the objective function. We investigate the use of derivative-free models based on radial basis functions (RBFs) in the search step of direct-search methods of directional type. We also study the application of algorithms based on difference of convex (d.c.) functions programming to solve the resulting subproblems which consist of the minimization of the RBF models subject to simple bounds on the variables. Extensive numerical results are reported with a test set of bound and linearly constrained problems.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/MAT/64838/2006, PTDC/MAT/098214/200

Scalable inference of ordinary differential equation models of biochemical processes.

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability