Reachability in Biochemical Dynamical Systems by Quantitative Discrete Approximation (extended abstract)

In this paper, a novel computational technique for finite discrete approximation of continuous dynamical systems suitable for a significant class of biochemical dynamical systems is introduced. The method is parameterized in order to affect the imposed level of approximation provided that with increasing parameter value the approximation converges to the original continuous system. By employing this approximation technique, we present algorithms solving the reachability problem for biochemical dynamical systems. The presented method and algorithms are evaluated on several exemplary biological models and on a real case study.Comment: In Proceedings CompMod 2011, arXiv:1109.104

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

The glass is half full <i>and</i> half empty:A population-representative twin study testing if optimism and pessimism are distinct systems

Optimism and pessimism are associated with important outcomes including health and depression. Yet it is unclear if these apparent polar opposites form a single dimension or reflect two distinct systems. The extent to which personality accounts for differences in optimism/pessimism is also controversial. Here, we addressed these questions in a genetically informative sample of 852 pairs of twins. Distinct genetic influences on optimism and pessimism were found. Significant family-level environment effects also emerged, accounting for much of the negative relationship between optimism and pessimism, as well as a link to neuroticism. A general positive genetics factor exerted significant links among both personality and life-orientation traits. Both optimism bias and pessimism also showed genetic variance distinct from all effects of personality, and from each other

Singing the same tune? International continuities and discontinuities in how police talk about using force

This article focuses on a research project conducted in six jurisdictions: England, The Netherlands, Germany, Australia, Venezuela, and Brazil. These societies are very different ethnically, socially, politically, economically, historically and have wildly different levels of crime. Their policing arrangements also differ significantly: how they are organised; how their officers are equipped and trained; what routine operating procedures they employ; whether they are armed; and much else besides. Most relevant for this research, they represent policing systems with wildly different levels of police shootings, Police in the two Latin American countries represented here have a justified reputation for the frequency with which they shoot people, whereas at the other extreme the police in England do not routinely carry firearms and rarely shoot anyone. To probe whether these differences are reflected in the way that officers talk about the use of force, police officers in these different jurisdictions were invited to discuss in focus groups a scenario in which police are thwarted in their attempt to arrest two youths (one of whom is a known local criminal) by the youths driving off with the police in pursuit, and concludes with the youths crashing their car and escaping in apparent possession of a gun, It might be expected that focus groups would prove starkly different, and indeed they were, but not in the way that might be expected. There was little difference in affirmation of normative and legal standards regarding the use of force. It was in how officers in different jurisdictions envisaged the circumstances in which the scenario took place that led Latin American officers to anticipate that they would shoot the suspects, whereas officers in the other jurisdictions had little expectation that they would open fire in the conditions as they imagined them to be

Deinococcus geothermalis: The Pool of Extreme Radiation Resistance Genes Shrinks

Bacteria of the genus Deinococcus are extremely resistant to ionizing radiation (IR), ultraviolet light (UV) and desiccation. The mesophile Deinococcus radiodurans was the first member of this group whose genome was completely sequenced. Analysis of the genome sequence of D. radiodurans, however, failed to identify unique DNA repair systems. To further delineate the genes underlying the resistance phenotypes, we report the whole-genome sequence of a second Deinococcus species, the thermophile Deinococcus geothermalis, which at its optimal growth temperature is as resistant to IR, UV and desiccation as D. radiodurans, and a comparative analysis of the two Deinococcus genomes. Many D. radiodurans genes previously implicated in resistance, but for which no sensitive phenotype was observed upon disruption, are absent in D. geothermalis. In contrast, most D. radiodurans genes whose mutants displayed a radiation-sensitive phenotype in D. radiodurans are conserved in D. geothermalis. Supporting the existence of a Deinococcus radiation response regulon, a common palindromic DNA motif was identified in a conserved set of genes associated with resistance, and a dedicated transcriptional regulator was predicted. We present the case that these two species evolved essentially the same diverse set of gene families, and that the extreme stress-resistance phenotypes of the Deinococcus lineage emerged progressively by amassing cell-cleaning systems from different sources, but not by acquisition of novel DNA repair systems. Our reconstruction of the genomic evolution of the Deinococcus-Thermus phylum indicates that the corresponding set of enzymes proliferated mainly in the common ancestor of Deinococcus. Results of the comparative analysis weaken the arguments for a role of higher-order chromosome alignment structures in resistance; more clearly define and substantially revise downward the number of uncharacterized genes that might participate in DNA repair and contribute to resistance; and strengthen the case for a role in survival of systems involved in manganese and iron homeostasis

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems