Over the last few years, the power law distribution has been used as the data generating mechanism in many disparate fields. However, at times the techniques used to fit the power law distribution have been inappropriate. This paper describes the poweRlaw R package, which makes fitting power laws and other heavy-tailed distributions straightforward. This package contains R functions for fitting, comparing and visualizing heavy tailed distributions. Overall, it provides a principled approach to power law fitting.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Although stochastic population models have proved to be a powerful tool in the study of process generating mechanisms across a wide range of disciplines, all too often the associated mathematical development involves nonlinear mathematics, which immediately raises difficult and challenging analytic problems that need to be solved if useful progress is to be made. One approximation that is often employed to estimate the moments of a stochastic process is moment closure. This approximation essentially truncates the moment equations of the stochastic process. A general expression for the marginal- and joint-moment equations for a large class of stochastic population models is presented. The generalisation of the moment equations allows this approximation to be applied easily to a wide range of models. Software is available from http://pysbml.googlecode.com/ to implement the techniques presented here.

OBJECTIVES: Availability of donor lungs suitable for transplant falls short of current demand and contributes to waiting list mortality. Ex vivo lung perfusion (EVLP) offers the opportunity to objectively assess and recondition organs unsuitable for immediate transplant. Identifying robust biomarkers that can stratify donor lungs during EVLP to use or non-use or for specific interventions could further improve its clinical impact. METHODS: In this pilot study, 16 consecutive donor lungs unsuitable for immediate transplant were assessed by EVLP. Key inflammatory mediators and tissue injury markers were measured in serial perfusate samples collected hourly and in bronchoalveolar lavage fluid (BALF) collected before and after EVLP. Levels were compared between donor lungs that met criteria for transplant and those that did not. RESULTS: Seven of the 16 donor lungs (44%) improved during EVLP and were transplanted with uniformly good outcomes. Tissue and vascular injury markers lactate dehydrogenase, HMGB-1 and Syndecan-1 were significantly lower in perfusate from transplanted lungs. A model combining IL-1β and IL-8 concentrations in perfusate could predict final EVLP outcome after 2 h assessment. In addition, perfusate IL-1β concentrations showed an inverse correlation to recipient oxygenation 24 h post-transplant. CONCLUSIONS: This study confirms the feasibility of using inflammation and tissue injury markers in perfusate and BALF to identify donor lungs most likely to improve for successful transplant during clinical EVLP. These results support examining this issue in a larger study.

BackgroundExtended criteria donor lungs deemed unsuitable for immediate transplantation can be reconditioned using ex vivo lung perfusion (EVLP). Objective identification of which donor lungs can be successfully reconditioned and will function well post-operatively has not been established. This study assessed the predictive value of markers of inflammation and tissue injury in donor lungs undergoing EVLP as part of the DEVELOP-UK study.MethodsLongitudinal samples of perfusate, bronchoalveolar lavage, and tissue from 42 human donor lungs undergoing clinical EVLP assessments were analyzed for markers of inflammation and tissue injury. Levels were compared according to EVLP success and post-transplant outcomes. Neutrophil adhesion to human pulmonary microvascular endothelial cells (HPMECs) conditioned with perfusates from EVLP assessments was investigated on a microfluidic platform.ResultsThe most effective markers to differentiate between in-hospital survival and non-survival post-transplant were perfusate interleukin (IL)-1β (area under the curve = 1.00, p = 0.002) and tumor necrosis factor-α (area under the curve = 0.95, p = 0.006) after 30 minutes of EVLP. IL-1β levels in perfusate correlated with upregulation of intracellular adhesion molecule-1 in donor lung vasculature (R2 = 0.68, p < 0.001) and to a lesser degree upregulation of intracellular adhesion molecule-1 (R2 = 0.30, p = 0.001) and E-selectin (R2 = 0.29, p = 0.001) in conditioned HPMECs and neutrophil adhesion to conditioned HPMECs (R2 = 0.33, p < 0.001). Neutralization of IL-1β in perfusate effectively inhibited neutrophil adhesion to conditioned HPMECs (91% reduction, p = 0.002).ConclusionsDonor lungs develop a detectable and discriminatory pro-inflammatory signature in perfusate during EVLP. Blocking the IL-1β pathway during EVLP may reduce endothelial activation and subsequent neutrophil adhesion on reperfusion; this requires further investigation in vivo.