Complementary approaches to understanding the plant circadian clock

This is the final version of the article. Available from the Open Publishing Association via the DOI in this record.Proceedings - Third Workshop 'From Biology To Concurrency and back', Paphos, Cyprus, 27 March 2010Circadian clocks are oscillatory genetic networks that help organisms adapt to the 24-hour day/night cycle. The clock of the green alga Ostreococcus tauri is the simplest plant clock discovered so far. Its many advantages as an experimental system facilitate the testing of computational predictions. We present a model of the Ostreococcus clock in the stochastic process algebra Bio-PEPA and exploit its mapping to different analysis techniques, such as ordinary differential equations, stochastic simulation algorithms and model-checking. The small number of molecules reported for this system tests the limits of the continuous approximation underlying differential equations. We investigate the difference between continuous-deterministic and discrete-stochastic approaches. Stochastic simulation and model-checking allow us to formulate new hypotheses on the system behaviour, such as the presence of self-sustained oscillations in single cells under constant light conditions. We investigate how to model the timing of dawn and dusk in the context of model-checking, which we use to compute how the probability distributions of key biochemical species change over time. These show that the relative variation in expression level is smallest at the time of peak expression, making peak time an optimal experimental phase marker. Building on these analyses, we use approaches from evolutionary systems biology to investigate how changes in the rate of mRNA degradation impacts the phase of a key protein likely to affect fitness. We explore how robust this circadian clock is towards such potential mutational changes in its underlying biochemistry. Our work shows that multiple approaches lead to a more complete understanding of the clock.The authors thank Gerben van Ooijen for TopCount data and Jane Hillston and Andrew Millar for their helpful comments. The Centre for Systems Biology at Edinburgh is a Centre for Integrative Systems Biology (CISB) funded by BBSRC and EPSRC, ref. BB/D019621/1. CT is supported by The International Human Frontier Science Program Organization

Narrative-based computational modelling of the Gp130/JAK/STAT signalling pathway.

BACKGROUND: Appropriately formulated quantitative computational models can support researchers in understanding the dynamic behaviour of biological pathways and support hypothesis formulation and selection by "in silico" experimentation. An obstacle to widespread adoption of this approach is the requirement to formulate a biological pathway as machine executable computer code. We have recently proposed a novel, biologically intuitive, narrative-style modelling language for biologists to formulate the pathway which is then automatically translated into an executable format and is, thus, usable for analysis via existing simulation techniques. RESULTS: Here we use a high-level narrative language in designing a computational model of the gp130/JAK/STAT signalling pathway and show that the model reproduces the dynamic behaviour of the pathway derived by biological observation. We then "experiment" on the model by simulation and sensitivity analysis to define those parameters which dominate the dynamic behaviour of the pathway. The model predicts that nuclear compartmentalisation and phosphorylation status of STAT are key determinants of the pathway and that alternative mechanisms of signal attenuation exert their influence on different timescales. CONCLUSION: The described narrative model of the gp130/JAK/STAT pathway represents an interesting case study showing how, by using this approach, researchers can model biological systems without explicitly dealing with formal notations and mathematical expressions (typically used for biochemical modelling), nevertheless being able to obtain simulation and analysis results. We present the model and the sensitivity analysis results we have obtained, that allow us to identify the parameters which are most sensitive to perturbations. The results, which are shown to be in agreement with existing mathematical models of the gp130/JAK/STAT pathway, serve us as a form of validation of the model and of the approach itself

Le competenze infermieristiche avanzate nel trattamento dello stroke in fase acuta in Italia. Strategia per l’identificazione (I parte)

Introduction: the goal of this study was to describe advanced nursing competences indicators for identification strategy in the stroke care in Italy and develop a survey tool. Problem: the new structure of the NHS and the needs expressed by patients increasingly require an advancement of the skills of health professionals. To this end the authors have identified and described a method for the detection of advanced nursing skills. Starting from the theoretical structure of AB Hamric have been identified analyzed and compared documents of a professionalizing and clinical from which have identified some categories. For each indicator have been identified one or more items and has been developed ad hoc questionnaire. At the end this was validated. Discussion: the methodology for the identification of the indicators has been efficacy in achieving the objectives. The strategy used in the study is reproducible, since traced to a theoretical model, and contextualized to any clinical setting, where there are secondary sources of evidencebased. It can also be adapted to post basic training course of a single reality. Conclusions: advanced clinical knowledge and skills, frequently without a formal recognition because of the complexity and instability of the patient, are used in the stroke care. Itís hoped to use the tool to verify the effectiveness and then play back the path in other clinical setting

Antimicrobial use and microbiological testing in district general hospital ICUs of the Veneto region of north-east Italy

International - predominantly American - studies undertaken in the ICUs of teaching centres show that inadequate antibiotic therapy increases mortality and length of stay. We sought to ascertain whether this also pertains to smaller ICUs in the Veneto region of north-east Italy. To the best of our knowledge, this is the first such survey in the Veneto area or in Italy as a whole. A retrospective, observational study was performed across five general-hospital ICUs to examine appropriateness of microbiological sampling, empirical antibiotic adequacy, and outcomes. Among 911 patients (mean age, 65.8 years ± 16.2 SD; median ICU stay, 17.0 days [IQR, 8.0–29.0]), 757 (83.1 %) were given empirical antibiotics. Treatment adequacy could be fully assessed in only 212 patients (28.0 %), who received empirical treatment and who had a relevant clinical sample collected at the initiation of this antibiotic (T0). Many other patients only had delayed microbiological investigation of their infections between day 1 and day 10 of therapy. Mortality was significantly higher among the 34.9 % of patients receiving inadequate treatment (48.6 % vs 18.80 %; p < 0.001). Only 32.5 % of combination regimens comprised a broad-spectrum Gram-negative β-lactam plus an anti-MRSA agent, and many combinations were irrational. Inadequate treatment was frequent and was strongly associated with mortality; moreover, there was delayed microbiological investigation of many infections, precluding appropriate treatment modification and de-escalation. Improvements in these aspects and in antibiotic stewardship are being sought

A High-Level Language for Rule-Based Modelling

Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages

Structural, Metabolic, and Functional Brain Abnormalities as a Result of Prenatal Exposure to Drugs of Abuse: Evidence from Neuroimaging

Prenatal exposure to alcohol and stimulants negatively affects the developing trajectory of the central nervous system in many ways. Recent advances in neuroimaging methods have allowed researchers to study the structural, metabolic, and functional abnormalities resulting from prenatal exposure to drugs of abuse in living human subjects. Here we review the neuroimaging literature of prenatal exposure to alcohol, cocaine, and methamphetamine. Neuroimaging studies of prenatal alcohol exposure have reported differences in the structure and metabolism of many brain systems, including in frontal, parietal, and temporal regions, in the cerebellum and basal ganglia, as well as in the white matter tracts that connect these brain regions. Functional imaging studies have identified significant differences in brain activation related to various cognitive domains as a result of prenatal alcohol exposure. The published literature of prenatal exposure to cocaine and methamphetamine is much smaller, but evidence is beginning to emerge suggesting that exposure to stimulant drugs in utero may be particularly toxic to dopamine-rich basal ganglia regions. Although the interpretation of such findings is somewhat limited by the problem of polysubstance abuse and by the difficulty of obtaining precise exposure histories in retrospective studies, such investigations provide important insights into the effects of drugs of abuse on the structure, function, and metabolism of the developing human brain. These insights may ultimately help clinicians develop better diagnostic tools and devise appropriate therapeutic interventions to improve the condition of children with prenatal exposure to drugs of abuse

Stochastic Simulation of Biomolecular Networks in Dynamic Environments

This is the final version of the article. Available from Public Library of Science via the DOI in this record.Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate-using decision-making by a large population of quorum sensing bacteria-that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.MV acknowledges support under an MRC Biomedical Informatics Fellowship. PT acknowledges support by the Royal Commission for the Exhibition of 1851. RG acknowledges support from the Leverhulme Trust (RPG-2013-171). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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