•We present here the first IonTorrent PGM profiling of the human saliva microbiome.•The saliva microbiome complexity increases after short-term probiotic intake.•Streptococcus, Actinomyces and Rothia ...characterize the post-probiotic microbiome.•The computational pipelines need to be adapted to the IonTorrent characteristics.•We suggest IonTorrent PGM as an overall good compromise for 16S rRNA sequencing.
Microbial communities populating several human body habitats are important determinants of human health. Cultivation-free community-wide approaches like bacterial 16S rRNA sequencing recently revolutionized the study of such human-associated microbial diversity, and the continuously decreasing cost/throughput ratio of current sequencing platforms is further enhancing the availability and effectiveness of microbiome research. The IonTorrent PGM platform is among the latest available commercial high-throughput sequencing tools, but it is just starting to be used for 16S rRNA surveys with only episodic assessments of its performance. We present here the first IonTorrent profiling of the human saliva microbiome collected from 12 healthy individuals. In this cohort, a subset of the volunteers was asked to assume a probiotic product, in order to investigate its impact on the composition and the structure of the saliva microbiome. Analysis of the generated dataset suggests the suitability of the IonTorrent platform for 16S rRNA surveys, even though some platform-specific choices are required to optimize the consistency of the obtained bacterial profiles. Interestingly, we found a marked and statistically significant increase of the overall bacterial diversity in the saliva of individuals who received the probiotic product compared to the control group, suggesting a short-term effect of probiotic product administration on oral microbiome composition.
Important achievements in traditional biology have deepened the knowledge about living systems leading to an extensive identification of parts-list of the cell as well as of the interactions among ...biochemical species responsible for cell's regulation. Such an expanding knowledge also introduces new issues. For example, the increasing comprehension of the interdependencies between pathways (pathways cross-talk) has resulted, on one hand, in the growth of informational complexity, on the other, in a strong lack of information coherence. The overall grand challenge remains unchanged: to be able to assemble the knowledge of every "piece" of a system in order to figure out the behavior of the whole (integrative approach). In light of these considerations, high performance computing plays a fundamental role in the context of in-silico biology. Stochastic simulation is a renowned analysis tool, which, although widely used, is subject to stringent computational requirements, in particular when dealing with heterogeneous and high dimensional systems. Here, we introduce and discuss a methodology aimed at alleviating the burden of simulating complex biological networks. Such a method, which springs from graph theory, is based on the principle of fragmenting the computational space of a simulation trace and delegating the computation of fragments to a number of parallel processes.
We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, ...for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.
Cells life follows a cycling behaviour which starts at cell birth and leads to cell division through a number of distinct phases. The transitions through the various cell cycle phases are controlled ...by a complex network of signalling pathways. Many cell cycle transitions are irreversible: once they are started they must reach completion. In this study we investigate the existence of conditions which lead to cases when irreversibility may be broken. Specifically, we characterise the elements of the cell cycle signalling network that are responsible for the irreversibility and we determine conditions for which the irreversible transitions may become reversible. We illustrate our results through a formal approach in which stochastic simulation analysis and model checking verification are combined. Through probabilistic model checking we provide a quantitative measure for the probability of irreversibility in the “Start” transition of the cell cycle.
We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, ...for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.
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