Statistical Model Checking for Stochastic Hybrid Systems

This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315

Finding a partner in the ocean: molecular and evolutionary bases of the response to sexual cues in a planktonic diatom

Microalgae play a major role as primary producers in aquatic ecosystems. Cell signalling regulates their interactions with the environment and other organisms, yet this process in phytoplankton is poorly defined. Using the marine planktonic diatom Pseudo-nitzschia multistriata, we investigated the cell response to cues released during sexual reproduction, an event that demands strong regulatory mechanisms and impacts on population dynamics. We sequenced the genome of P. multistriata and performed phylogenomic and transcriptomic analyses, which allowed the definition of gene gains and losses, horizontal gene transfers, conservation and evolutionary rate of sex-related genes. We also identified a small number of conserved noncoding elements. Sexual reproduction impacted on cell cycle progression and induced an asymmetric response of the opposite mating types. G protein-coupled receptors and cyclic guanosine monophosphate (cGMP) are implicated in the response to sexual cues, which overall entails a modulation of cell cycle, meiosis-related and nutrient transporter genes, suggesting a fine control of nutrient uptake even under nutrient-replete conditions. The controllable life cycle and the genome sequence of P. multistriata allow the reconstruction of changes occurring in diatoms in a key phase of their life cycle, providing hints on the evolution and putative function of their genes and empowering studies on sexual reproduction

Finding a partner in the ocean: molecular and evolutionary bases of the response to sexual cues in a planktonic diatom

Microalgae play a major role as primary producers in aquatic ecosystems. Cell signalling regulates their interactions with the environment and other organisms, yet this process in phytoplankton is poorly defined. Using the marine planktonic diatom Pseudo-nitzschia multistriata, we investigated the cell response to cues released during sexual reproduction, an event that demands strong regulatory mechanisms and impacts on population dynamics. We sequenced the genome of P. multistriata and performed phylogenomic and transcriptomic analyses, which allowed the definition of gene gains and losses, horizontal gene transfers, conservation and evolutionary rate of sex-related genes. We also identified a small number of conserved noncoding elements. Sexual reproduction impacted on cell cycle progression and induced an asymmetric response of the opposite mating types. G protein-coupled receptors and cyclic guanosine monophosphate (cGMP) are implicated in the response to sexual cues, which overall entails a modulation of cell cycle, meiosis-related and nutrient transporter genes, suggesting a fine control of nutrient uptake even under nutrient-replete conditions. The controllable life cycle and the genome sequence of P. multistriata allow the reconstruction of changes occurring in diatoms in a key phase of their life cycle, providing hints on the evolution and putative function of their genes and empowering studies on sexual reproduction

Subtle Alterations in PCNA-Partner Interactions Severely Impair DNA Replication and Repair

Dynamic switching of PCNA-partner interactions is essential for normal DNA replication and repair in yeast

Emergence of Noise-Induced Oscillations in the Central Circadian Pacemaker

Computational modeling and experimentation explain how intercellular coupling and intracellular noise can generate oscillations in a mammalian neuronal network even in the absence of cell-autonomous oscillators

Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise

Quantitative genetic analysis has long been used to study how natural variation of genotype can influence an organism's phenotype. While most studies have focused on genetic determinants of phenotypic average, it is rapidly becoming understood that stochastic noise is genetically determined. However, it is not known how many traits display genetic control of stochastic noise nor how broadly these stochastic loci are distributed within the genome. Understanding these questions is critical to our understanding of quantitative traits and how they relate to the underlying causal loci, especially since stochastic noise may be directly influenced by underlying changes in the wiring of regulatory networks. We identified QTLs controlling natural variation in stochastic noise of glucosinolates, plant defense metabolites, as well as QTLs for stochastic noise of related transcripts. These loci included stochastic noise QTLs unique for either transcript or metabolite variation. Validation of these loci showed that genetic polymorphism within the regulatory network alters stochastic noise independent of effects on corresponding average levels. We examined this phenomenon more globally, using transcriptomic datasets, and found that the Arabidopsis transcriptome exhibits significant, heritable differences in stochastic noise. Further analysis allowed us to identify QTLs that control genomic stochastic noise. Some genomic QTL were in common with those altering average transcript abundance, while others were unique to stochastic noise. Using a single isogenic population, we confirmed that natural variation at ELF3 alters stochastic noise in the circadian clock and metabolism. Since polymorphisms controlling stochastic noise in genomic phenotypes exist within wild germplasm for naturally selected phenotypes, this suggests that analysis of Arabidopsis evolution should account for genetic control of stochastic variance and average phenotypes. It remains to be determined if natural genetic variation controlling stochasticity is equally distributed across the genomes of other multi-cellular eukaryotes