Author: Joel Barry Swartz
Publisher:
ISBN:
Category : Biological models
Languages : en
Pages : 590

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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Andrei Kramer
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832541950
Category : Computers
Languages : en
Pages : 164

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Markov Chain Monte Carlo (MCMC) methods are sampling based techniques, which use random numbers to approximate deterministic but unknown values. They can be used to obtain expected values, estimate parameters or to simply inspect the properties of a non-standard, high dimensional probability distribution. Bayesian analysis of model parameters provides the mathematical foundation for parameter estimation using such probabilistic sampling. The strengths of these stochastic methods are their robustness and relative simplicity even for nonlinear problems with dozens of parameters as well as a built-in uncertainty analysis. Because Bayesian model analysis necessarily involves the notion of prior knowledge, the estimation of unidentifiable parameters can be regularised (by priors) in a straight forward way. This work draws the focus on typical cases in systems biology: relative data, nonlinear ordinary differential equation models and few data points. It also investigates the consequences of parameter estimation from steady state data; consequences such as performance benefits. In biology the data is almost exclusively relative, the raw measurements (e.g. western blot intensities) are normalised by control experiments or a reference value within a series and require the model to do the same when comparing its output to the data. Several sampling algorithms are compared in terms of effective sampling speed and necessary adaptations to relative and steady state data are explained.

Author: Adam Clinton Baughman
Publisher:
ISBN:
Category :
Languages : en
Pages : 96

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Author: Quentin Vanhaelen
Publisher: Humana
ISBN: 9781071617694
Category : Science
Languages : en
Pages : 0

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This detailed book provides an overview of various classes of computational techniques, including machine learning techniques, commonly used for evaluating kinetic parameters of biological systems. Focusing on three distinct situations, the volume covers the prediction of the kinetics of enzymatic reactions, the prediction of the kinetics of protein-protein or protein-ligand interactions (binding rates, dissociation rates, binding affinities), and the prediction of relatively large set of kinetic rates of reactions usually found in quantitative models of large biological networks. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of expert implementation advice that leads to successful results. Authoritative and practical, Computational Methods for Estimating the Kinetic Parameters of Biological Systems will be of great interest for researchers working through the challenge of identifying the best type of algorithm and who would like to use or develop a computational method for the estimation of kinetic parameters.

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Viruses pose a threat to human health. Understanding how viruses work helps us develop vaccines and antivirals. Experimental techniques are now advanced enough to provide quantitative data regarding viral infection. Using this data, we can develop mathematical models to describe viral infection processes. Two such modeling paradigms are deterministic and stochastic reaction systems. Useful mathematical models require accurate estimates of model parameters from data. In this dissertation, I present parameter estimation methods for deterministic and stochastic reaction models, focusing on the stochastic models. I present two new classes of parameter estimation methods for stochastic chemical kinetic models, namely, importance sampling and approximate direct methods. Using examples from systems biology, I demonstrate the use of these newly developed methods and compare them with literature methods with favorable results. Guidelines on experimental and model design and directions for further research are presented in the end.

Author: J. Perkins
Publisher: Createspace Independent Publishing Platform
ISBN: 9781983526428
Category :
Languages : en
Pages : 438

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SimBiology provides an app and programmatic tools to model, simulate, and analyze dynamic systems, focusing on pharmacokinetic/pharmacodynamic (PK/PD) and systems biology applications. It provides a block diagram editor for building models, or you can create models programmatically using the MATLAB language. SimBiology includes a library of common PK models, which you can customize and integrate with mechanistic systems biology models. A variety of model exploration techniques let you identify optimal dosing schedules and putative drug targets in cellular pathways. SimBiology uses ordinary differential equations (ODEs) and stochastic solvers to simulate the time course profile of drug exposure, drug efficacy, and enzyme and metabolite levels. You can investigate system dynamics and guide experimentation using parameter sweeps and sensitivity analysis. You can also use single subject or population data to estimate model parameters. The fundamental content of this book is the following: -App for PK/PD and mechanistic systems biology modeling -Ordinary differential equations (ODEs) and stochastic solvers -Library of PK models -Parameter estimation techniques for single-subject and population data, including nonlinear mixed-effects models -Sensitivity analysis and parameter sweeps for investigating parameter effects on system dynamics -Diagnostic plots for individual and population fits -Methods for creating and optimizing dosing schedules

Author: William Robert Smith
Publisher:
ISBN:
Category : Biological systems
Languages : en
Pages : 180

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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 129

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Kalman filter; constrained square-root unscented kalman filter; identifiability analysis; kinetic model; profile likelihood; Sensitivity analysis; informative prior

Author: Sarvesh Dwivedi
Publisher:
ISBN:
Category :
Languages : en
Pages :

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