Accelerated Model Checking of Parametric Markov Chains

Parametric Markov chains occur quite naturally in various applications: they can be used for a conservative analysis of probabilistic systems (no matter how the parameter is chosen, the system works to specification); they can be used to find optimal settings for a parameter; they can be used to visualise the influence of system parameters; and they can be used to make it easy to adjust the analysis for the case that parameters change. Unfortunately, these advancements come at a cost: parametric model checking is---or rather was---often slow. To make the analysis of parametric Markov models scale, we need three ingredients: clever algorithms, the right data structure, and good engineering. Clever algorithms are often the main (or sole) selling point; and we face the trouble that this paper focuses on -- the latter ingredients to efficient model checking. Consequently, our easiest claim to fame is in the speed-up we have often realised when comparing to the state of the art

Evolutionary Sequence Modeling for Discovery of Peptide Hormones

There are currently a large number of “orphan” G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development

Bioaccumulation and Toxicity of Organic Chemicals in Terrestrial Invertebrates

Terrestrial invertebrates are key components in ecosystems, with crucial roles in soil structure, functioning, and ecosystem services. The present chapter covers how terrestrial invertebrates are impacted by organic chemicals, focusing on up-to-date information regarding bioavailability, exposure routes and general concepts on bioaccumulation, toxicity, and existing models. Terrestrial invertebrates are exposed to organic chemicals through different routes, which are dependent on both the organismal traits and nature of exposure, including chemical properties and media characteristics. Bioaccumulation and toxicity data for several groups of organic chemicals are presented and discussed, attempting to cover plant protection products (herbicides, insecticides, fungicides, and molluscicides), veterinary and human pharmaceuticals, polycyclic aromatic compounds, polychlorinated biphenyls, flame retardants, and personal care products. Chemical mixtures are also discussed bearing in mind that chemicals appear simultaneously in the environment. The biomagnification of organic chemicals is considered in light of the consumption of terrestrial invertebrates as novel feed and food sources. This chapter highlights how science has contributed with data from the last 5 years, providing evidence on bioavailability, bioaccumulation, and toxicity derived from exposure to organic chemicals, including insights into the main challenges and shortcomings to extrapolate results to real exposure scenarios