A Graphical and Computational Modelling Platform for Biological Pathways

A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net–based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed

Peak inspiratory flow measured at different inhaler resistances in patients with asthma

BACKGROUND: Patients' peak inspiratory flow rate (PIFR) may help clinicians select an inhaler device. OBJECTIVE: To determine the proportion of patients with asthma who could generate correct PIFRs at different inhaler resistance settings. METHODS: During a UK asthma review service, patients' PIFR was checked at resistance settings matching their current preventer inhaler device, at R5 (high resistance dry powder inhaler (DPI)) and at R0 (low resistance, pressurised metered dose inhaler (pMDI)). Correct PIFR ('pass') was defined for R5 as 30-90 L/min and for R0 as 20-60 L/min. A logistic regression model examined the independent predictors of incorrect PIFR ('fail') at R5 and R0. Asthma severity was assessed retrospectively from treatment level. RESULTS: A total of 994 adults (female 64.3%) were included, of whom 90.4% currently used a preventer inhaler (71.5% pMDI). PIFR pass rates were: 93.7% at R5 compared with 70.5% at R0 (p60 L/min), and 20% of patients currently using pMDI failed for this reason. Independent risk factors for failing R5 were: female gender, older age group and current preventer pMDI; and for failing R0 included: male gender, younger age group, current preventer DPI and mild versus severe asthma. CONCLUSIONS: This study demonstrates that most patients with asthma can achieve adequate inspiratory flow to activate high resistance DPIs, whereas approximately a third of patients breathe in too fast to achieve recommended inspiratory flows for correct pMDI use, including one fifth of patients who currently use a pMDI preventer