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

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

A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.

As modeling becomes a more widespread practice in the life sciences and biomedical sciences, researchers need reliable tools to calibrate models against ever more complex and detailed data. Here we present an approximate Bayesian computation (ABC) framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches. We outline the underlying rationale, discuss the computational and practical issues and provide detailed guidance as to how the important tasks of parameter inference and model selection can be performed in practice. Unlike other available packages, ABC-SysBio is highly suited for investigating, in particular, the challenging problem of fitting stochastic models to data. In order to demonstrate the use of ABC-SysBio, in this protocol we postulate the existence of an imaginary reaction network composed of seven interrelated biological reactions (involving a specific mRNA, the protein it encodes and a post-translationally modified version of the protein), a network that is defined by two files containing 'observed' data that we provide as supplementary information. In the first part of the PROCEDURE, ABC-SysBio is used to infer the parameters of this system, whereas in the second part we use ABC-SysBio's relevant functionality to discriminate between two different reaction network models, one of them being the 'true' one. Although computationally expensive, the additional insights gained in the Bayesian formalism more than make up for this cost, especially in complex problems

Toward community standards in the quest for orthologs

The identification of orthologs—genes pairs descended from a common ancestor through speciation, rather than duplication—has emerged as an essential component of many bioinformatics applications, ranging from the annotation of new genomes to experimental target prioritization. Yet, the development and application of orthology inference methods is hampered by the lack of consensus on source proteomes, file formats and benchmarks. The second ‘Quest for Orthologs' meeting brought together stakeholders from various communities to address these challenges. We report on achievements and outcomes of this meeting, focusing on topics of particular relevance to the research community at large. The Quest for Orthologs consortium is an open community that welcomes contributions from all researchers interested in orthology research and applications. Contact: [email protected]