Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

Approximate Bayesian computation methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper we discuss and apply an approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC gives information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.Comment: 26 pages, 9 figure

From qualitative data to quantitative models: analysis of the phage shock protein stress response in Escherichia coli

Background Bacteria have evolved a rich set of mechanisms for sensing and adapting to adverse conditions in their environment. These are crucial for their survival, which requires them to react to extracellular stresses such as heat shock, ethanol treatment or phage infection. Here we focus on studying the phage shock protein (Psp) stress response in Escherichia coli induced by a phage infection or other damage to the bacterial membrane. This system has not yet been theoretically modelled or analysed in silico. Results We develop a model of the Psp response system, and illustrate how such models can be constructed and analyzed in light of available sparse and qualitative information in order to generate novel biological hypotheses about their dynamical behaviour. We analyze this model using tools from Petri-net theory and study its dynamical range that is consistent with currently available knowledge by conditioning model parameters on the available data in an approximate Bayesian computation (ABC) framework. Within this ABC approach we analyze stochastic and deterministic dynamics. This analysis allows us to identify different types of behaviour and these mechanistic insights can in turn be used to design new, more detailed and time-resolved experiments. Conclusions We have developed the first mechanistic model of the Psp response in E. coli. This model allows us to predict the possible qualitative stochastic and deterministic dynamic behaviours of key molecular players in the stress response. Our inferential approach can be applied to stress response and signalling systems more generally: in the ABC framework we can condition mathematical models on qualitative data in order to delimit e.g. parameter ranges or the qualitative system dynamics in light of available end-point or qualitative information.Medical Research Council (Great Britain)Biotechnology and Biological Sciences Research Council (Great Britain)Wellcome Trust (London, England)Royal Society (Great Britain) (Wolfson Research Merit Award

Model selection in systems biology depends on experimental design.

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis

Teaching physiology: blood pressure and heart rate changes in simulated diving

Background and Purpose: Physiology exercise employing simulated diving is used in our curriculum to integrate knowledge in cardio-respiratory physiology. Aim was to improve model used in physiology exercise by employing continuous recordings of arterial pressure and heart rate. Materials and Methods: Total of 55 medical and dental students volunteered for the exercise. They were instrumented with photoplethysmographic blood pressure and heart rate device, as well as with pulse oxymetry. Continuous measurement of variables was undertaken while students performed apneas or breathed through snorkel in air or in cold water, or temperature change was applied to their forehead. Results: Employment of continuous recordings enabled detailed insight into changes in selected cardiovascular parameters during 30 seconds breathholding. Time course of the changes showed marked biphasic response. When face was submerged in cold water during apnea, arterial pressure initially decreased and heart rate increased. At the end of breath-hold, arterial pressure increased and heart rate decreased, respectively. Corresponding changes were less pronounced when breath-hold was performed without face immersion. Conclusion: Improved protocol in laboratory exercise enabled us to show two distinct phases in changes of cardiovascular variables which are characteristic of diving reflex. We showed students how modern technology can improve their studies in near future and encouraged and motivate them to participate actively in exercise

ABC-SysBio—approximate Bayesian computation in Python with GPU support

Motivation: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions

Simulation-based model selection for dynamical systems in systems and population biology

Computer simulations have become an important tool across the biomedical sciences and beyond. For many important problems several different models or hypotheses exist and choosing which one best describes reality or observed data is not straightforward. We therefore require suitable statistical tools that allow us to choose rationally between different mechanistic models of e.g. signal transduction or gene regulation networks. This is particularly challenging in systems biology where only a small number of molecular species can be assayed at any given time and all measurements are subject to measurement uncertainty. Here we develop such a model selection framework based on approximate Bayesian computation and employing sequential Monte Carlo sampling. We show that our approach can be applied across a wide range of biological scenarios, and we illustrate its use on real data describing influenza dynamics and the JAK-STAT signalling pathway. Bayesian model selection strikes a balance between the complexity of the simulation models and their ability to describe observed data. The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable.Comment: This article is in press in Bioinformatics, 2009. Advance Access is available on Bioinformatics webpag

Construction of biodentaplast upper partial removable protection of biodentaplast by the bredent system (ThermoPress 400)

Необходимостта от подобрена биопоносимост, функция и естетика налага използването на термопластични материали в зъбопротезирането. Разликата между акрилните пластмаси и термопластичните материали се свежда до това, че термоматериалите са полимеризирали при фабрични условия и ние ги получаваме под формата на гранулат. Поради тази причина те не съдържат остатъчен мономер. Частичната снемаема протеза не се фиксира неподвижно към естествените зъби на пациента. Което означава, че пациентът може сам да сваля и слага протезата при нужда. Този тип протези лежат върху лигавицата и се свързват с естествените зъби с помощта на разнообразни задръжно-опорни елементи. Целта на настоящата статия е да се представят технологичните етапи на изработване на горна частична протеза от материала Biodentaplast по системата на Bredent (thermopress 400) върху случай от реалната практика. Разгледан клиничен случай, отнасящ се до пациент от мъжки пол на 56 г., който започва лечение през м. май 2017 г. Частичното обеззъбяване е трети клас по Кенеди (дистално двустранно ограничен дефект). Biodentaplast е термопластичен материал на основата на полиоксиметилена с висока степен на кристалност и има универсално приложение в зъботехниката за изработване на безметални конструк ции. Високотехнологичният материал се отличава с твърдост, еластичност, биопоносимост и лекота. Някои от направените изводи са, че термопластичните протези са приложими при пациенти, които имат противопоказания за изпиляване на зъбите (епилепсия, остри ставни заболявания и др.). Проведено анкетно проучване сред пациенти показва, че 100% от анкетираните предпочитат гъвкавите протези пред конвенционалните РММА пластмаси. Биологичната поносимост е голяма поради липсата на остатъчен мономер и метал.The need for improved bioscience, function and aesthetics requires the use of thermoplastic materials in dental prostheses. The difference between acrylic plastics and thermoplastic materials is that the thermo-materials are polymerized under factory conditions and we get them in the form of granulates. For this reason, they do not contain a residual monomer. Partial removable prostheses are not fixed rigidly to the natural teeth of a patient. This means that the patient can remove and put the prosthesis himself, if necessary. This type of prosthesis rests on the mucous membrane and connects with the natural teeth with a variety of support elements. The purpose of this article is to present the technological steps of making an upper partial prosthesis from Biodentaplast material on the Bredent system (thermopress 400) on a real-life case. We present a case study involving a male patient, aged 56, who started in May, 2017. His partial teeth were third class by Kennedy (distal bilateral narrowing defect). Biodentaplast is a thermoplastic material based on polyoxymethylene with a high degree of crystallinity and is universally used in dentistry for the manufacture of non-metallic structures. The high-tech material is characterized by its hardness, elasticity, bio-portability and lightness. Some of the conclusions drawn are that thermoplastic dentures are useful in patients who have contraindications for dental scarring (epilepsy, acute joint diseases, etc.). A survey conducted among patients shows that 100% of respondents prefer flexible dentures to conventional PMMA plastics. Biological tolerance is high due to the lack of residual monomer and metal

Ectopia cordis thoracique sporadique: description clinique d’un cas

Nous décrivons un cas d'ectopia cordis, une malformation cardiaque congénitale extrêmement rare dans laquelle le coeur est partiellement ou complètement situé en dehors des limites de la cage thoracique. Dans le cas que nous décrivons, elle est thoracique et isolée. Ce cas a été diagnostiqué en salle de naissance au Katanga, au sud de la République Démocratique du Congo. Il s'agit du premier cas documenté chez un nouveau-né Congolais. Pan African Medical Journal 2012; 13:6