Targeted experiment design using the posterior predictive distribution

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Parameter adaptations during phenotype transitions in progressive diseases

<p>Abstract</p> <p>Background</p> <p>The study of phenotype transitions is important to understand progressive diseases, e.g., diabetes mellitus, metabolic syndrome, and cardiovascular diseases. A challenge remains to explain phenotype transitions in terms of adaptations in molecular components and interactions in underlying biological systems.</p> <p>Results</p> <p>Here, mathematical modeling is used to describe the different phenotypes by integrating experimental data on metabolic pools and fluxes. Subsequently, trajectories of parameter adaptations are identified that are essential for the phenotypical changes. These changes in parameters reflect progressive adaptations at the transcriptome and proteome level, which occur at larger timescales. The approach was employed to study the metabolic processes underlying liver X receptor induced hepatic steatosis. Model analysis predicts which molecular processes adapt in time after pharmacological activation of the liver X receptor. Our results show that hepatic triglyceride fluxes are increased and triglycerides are especially stored in cytosolic fractions, rather than in endoplasmic reticulum fractions. Furthermore, the model reveals several possible scenarios for adaptations in cholesterol metabolism. According to the analysis, the additional quantification of one cholesterol flux is sufficient to exclude many of these hypotheses.</p> <p>Conclusions</p> <p>We propose a generic computational approach to analyze biological systems evolving through various phenotypes and to predict which molecular processes are responsible for the transition. For the case of liver X receptor induced hepatic steatosis the novel approach yields information about the redistribution of fluxes and pools of triglycerides and cholesterols that was not directly apparent from the experimental data. Model analysis provides guidance which specific molecular processes to study in more detail to obtain further understanding of the underlying biological system.</p

ISHCCO Qualification Framework For Construction Safety Coordinators

ISHCCO (International Safety and Health Construction Coordinators Organization - www.ishcco.org) represents an European umbrella association of the national professional associations of Health and Safety Construction Coordinators (HSCC). One of the statutory aims of ISHCCO is to promote excellence in education, training and professional development in the countries of the national members. Since ISHCCO was founded in 2003 it has been working on the development of such a catalogue of criteria for the promotion and acceptance of qualification framework for HSCC. The ISHCCO qualification framework (IQF) developed enables benchmarking based on technical standards and European legislation complying with international and national criteria. IQF, like the European Qualification Framework, has three dimensions for the competences of HSCC: knowledge, skills and attitudes. What is described is the process followed to define these competences, the application of IQF for levels 5, 6 and 7 of EQF (technician, bachelor and master) and the connection with the European Directive 92/57 about temporary or mobile construction sites. The types of projects considered in IQF include requirements for simple projects, medium building construction and civil engineering projects and highly specialized construction projects or major projects. The target groups in construction are experts, institutions, professional associations, chambers of commerce, construction sector companies, authorities and building owner/clients. IQF can be used to define learning outcomes of HSCC training courses and respective contents and assessment

An integrated strategy for prediction uncertainty analysis

Motivation: To further our understanding of the mechanisms underlying biochemical pathways mathematical modelling is used. Since many parameter values are unknown they need to be estimated using experimental observations. The complexity of models necessary to describe biological pathways in combination with the limited amount of quantitative data results in large parameter uncertainty which propagates into model predictions. Therefore prediction uncertainty analysis is an important topic that needs to be addressed in Systems Biology modelling

A Bayesian approach to targeted experiment design

Motivation: Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity

Geodesic Monte Carlo on embedded manifolds

Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows in the Hamilton–Jacobi representation. This paper takes the differential geometric basis of Markov chain Monte Carlo further by considering methods to simulate from probability distributions that themselves are defined on a manifold, with common examples being classes of distributions describing directional statistics. Proposal mechanisms are developed based on the geodesic flows over the manifolds of support for the distributions, and illustrative examples are provided for the hypersphere and Stiefel manifold of orthonormal matrices

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib.

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines

A systems toxicology paracetamol overdose framework: accounting for high-risk individuals

The most commonly prescribed painkiller worldwide, paracetamol (acetaminophen, APAP) is also the predominant cause of acute liver failure (ALF), and therefore paracetamol-induced liver toxicity remains an important clinical problem. The standard clinical treatment framework for paracetamol overdose currently allows for antidote therapy decisions to be made based on a nomogram treatment line. This treatment threshold is lowered for patients adjudged to be highly susceptible to liver injury due to risk factors such as anorexia nervosa or bulimia. Additionally, both the original and adjusted clinical frameworks are highly dependent on knowledge from the patient regarding time since ingestion and initial dose amount, both of which are often highly unpredictable. We have recently developed a pre-clinical framework for predicting time since ingestion, initial dose amount and subsequent probability of liver injury based on novel biomarker concentrations. Here, we use identifiability analysis as a tool to increase confidence in our model parameter estimates and extend the framework to make predictions for both healthy and high-risk populations. Through pharmacokinetic-pharmacodynamic model refinement, we identify thresholds that determine whether necrosis or apoptosis is the dominant form of cell death, which can be essential for effective ALF interventions. Using a single blood test, rather than the multiple tests required in the current clinical frameworks, our model provides overdose identification information applicable for healthy and high-risk individuals as well as quantitative measures of estimated liver injury probability