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Identifiability and Regression Analysis of Biological Systems Models

Author: Paola Lecca

Publisher:

Published: 2020

Total Pages: 90

ISBN-13: 9783030412562

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This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.

Book Synopsis Identifiability and Regression Analysis of Biological Systems Models by : Paola Lecca

Download or read book Identifiability and Regression Analysis of Biological Systems Models written by Paola Lecca and published by . This book was released on 2020 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.

Identifiability and Regression Analysis of Biological Systems Models

Author: Paola Lecca

Publisher: Springer Nature

Published: 2020-03-05

Total Pages: 90

ISBN-13: 3030412555

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Book Synopsis Identifiability and Regression Analysis of Biological Systems Models by : Paola Lecca

Download or read book Identifiability and Regression Analysis of Biological Systems Models written by Paola Lecca and published by Springer Nature. This book was released on 2020-03-05 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.

Parameter Identification Techniques for Systems Biology Models

Author: Choujun Zhan

Publisher: LAP Lambert Academic Publishing

Published: 2012-06

Total Pages: 168

ISBN-13: 9783846533659

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Mathematical models for revealing the dynamics and interaction properties inside biological systems play an important role in computational systems biology. This work is motivated by the current difficulties in identifying practical bio-system models. In the field of systems biology, available data are often noisy, sparse and expensive to collect. Therefore, system identification is a challenging problem. A major task of identifying a model, as described in terms of non-linear ordinary differential equations (ODEs) with indeterminate parameters, can be formulated into an optimization problem. It is a reverse engineering exercise to reconstruct the system model via various numerical tools like constraint-mixed-optimization algorithms and approximations. Due to sensitivity issues, in many cases, even the simulated output data, as generated by the identified model with a set of estimated parameters, fit very well with the measured data, it is still important to infer how well these model parameters being determined; which is essential for the investigation of model construction. For this reason, the identifiability issues, which is an important practical issue, is also treated.

Book Synopsis Parameter Identification Techniques for Systems Biology Models by : Choujun Zhan

Download or read book Parameter Identification Techniques for Systems Biology Models written by Choujun Zhan and published by LAP Lambert Academic Publishing. This book was released on 2012-06 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical models for revealing the dynamics and interaction properties inside biological systems play an important role in computational systems biology. This work is motivated by the current difficulties in identifying practical bio-system models. In the field of systems biology, available data are often noisy, sparse and expensive to collect. Therefore, system identification is a challenging problem. A major task of identifying a model, as described in terms of non-linear ordinary differential equations (ODEs) with indeterminate parameters, can be formulated into an optimization problem. It is a reverse engineering exercise to reconstruct the system model via various numerical tools like constraint-mixed-optimization algorithms and approximations. Due to sensitivity issues, in many cases, even the simulated output data, as generated by the identified model with a set of estimated parameters, fit very well with the measured data, it is still important to infer how well these model parameters being determined; which is essential for the investigation of model construction. For this reason, the identifiability issues, which is an important practical issue, is also treated.

2019-20 MATRIX Annals

Author: Jan de Gier

Publisher: Springer Nature

Published: 2021-02-10

Total Pages: 798

ISBN-13: 3030624978

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MATRIX is Australia’s international and residential mathematical research institute. It facilitates new collaborations and mathematical advances through intensive residential research programs, each 1-4 weeks in duration. This book is a scientific record of the ten programs held at MATRIX in 2019 and the two programs held in January 2020: · Topology of Manifolds: Interactions Between High and Low Dimensions · Australian-German Workshop on Differential Geometry in the Large · Aperiodic Order meets Number Theory · Ergodic Theory, Diophantine Approximation and Related Topics · Influencing Public Health Policy with Data-informed Mathematical Models of Infectious Diseases · International Workshop on Spatial Statistics · Mathematics of Physiological Rhythms · Conservation Laws, Interfaces and Mixing · Structural Graph Theory Downunder · Tropical Geometry and Mirror Symmetry · Early Career Researchers Workshop on Geometric Analysis and PDEs · Harmonic Analysis and Dispersive PDEs: Problems and Progress The articles are grouped into peer-reviewed contributions and other contributions. The peer-reviewed articles present original results or reviews on a topic related to the MATRIX program; the remaining contributions are predominantly lecture notes or short articles based on talks or activities at MATRIX.

Book Synopsis 2019-20 MATRIX Annals by : Jan de Gier

Download or read book 2019-20 MATRIX Annals written by Jan de Gier and published by Springer Nature. This book was released on 2021-02-10 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATRIX is Australia’s international and residential mathematical research institute. It facilitates new collaborations and mathematical advances through intensive residential research programs, each 1-4 weeks in duration. This book is a scientific record of the ten programs held at MATRIX in 2019 and the two programs held in January 2020: · Topology of Manifolds: Interactions Between High and Low Dimensions · Australian-German Workshop on Differential Geometry in the Large · Aperiodic Order meets Number Theory · Ergodic Theory, Diophantine Approximation and Related Topics · Influencing Public Health Policy with Data-informed Mathematical Models of Infectious Diseases · International Workshop on Spatial Statistics · Mathematics of Physiological Rhythms · Conservation Laws, Interfaces and Mixing · Structural Graph Theory Downunder · Tropical Geometry and Mirror Symmetry · Early Career Researchers Workshop on Geometric Analysis and PDEs · Harmonic Analysis and Dispersive PDEs: Problems and Progress The articles are grouped into peer-reviewed contributions and other contributions. The peer-reviewed articles present original results or reviews on a topic related to the MATRIX program; the remaining contributions are predominantly lecture notes or short articles based on talks or activities at MATRIX.

Parameter Identifiability of Biochemical Reaction Networks in Systems Biology

Author: Dara Geffen

Publisher:

Published: 2008

Total Pages: 152

ISBN-13:

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In systems biology, models often contain a large number of unknown or only roughly known parameters that must be estimated through the fitting of data. This work examines the question of whether or not these parameters can in fact be estimated from available measurements. Structural or a priori identifiability of unknown parameters in biochemical reaction networks is considered. Such systems consist of continuous time, nonlinear differential equations. Several methods for analyzing identifiability of such systems exist, most of which restate the question as one of observability by expanding the state space to include parameters. However, these existing methods were not developed with biological systems in mind, so do not necessarily address the specific challenges posed by this type of problem. In this work, such methods are considered for the analysis of a representative biological system, the NF-kappaB signal transduction pathway. It is shown that existing observability-based strategies, which rely on finding an analytical solution, require significant simplifications to be applicable to systems biology problems that are seldom feasible. The analytical nature of the solution imposes restrictions on the size and complexity of systems that these methods can handle. This conflicts with the fact that most currently studied systems biology models are rather large networks containing many states and parameters. In this thesis, a new simulation based method using an empirical observability Gramian for determining identifiability is proposed. Computational and numerical sensitivity issues for this method are considered. An algorithm, based on this method, is developed and demonstrated on a simple biological example of microbial growth with Michaelis-Menten kinetics. The new method is applied to the motivating NF-kappaB example to show its suitability for use in systems biology.

Book Synopsis Parameter Identifiability of Biochemical Reaction Networks in Systems Biology by : Dara Geffen

Download or read book Parameter Identifiability of Biochemical Reaction Networks in Systems Biology written by Dara Geffen and published by . This book was released on 2008 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: In systems biology, models often contain a large number of unknown or only roughly known parameters that must be estimated through the fitting of data. This work examines the question of whether or not these parameters can in fact be estimated from available measurements. Structural or a priori identifiability of unknown parameters in biochemical reaction networks is considered. Such systems consist of continuous time, nonlinear differential equations. Several methods for analyzing identifiability of such systems exist, most of which restate the question as one of observability by expanding the state space to include parameters. However, these existing methods were not developed with biological systems in mind, so do not necessarily address the specific challenges posed by this type of problem. In this work, such methods are considered for the analysis of a representative biological system, the NF-kappaB signal transduction pathway. It is shown that existing observability-based strategies, which rely on finding an analytical solution, require significant simplifications to be applicable to systems biology problems that are seldom feasible. The analytical nature of the solution imposes restrictions on the size and complexity of systems that these methods can handle. This conflicts with the fact that most currently studied systems biology models are rather large networks containing many states and parameters. In this thesis, a new simulation based method using an empirical observability Gramian for determining identifiability is proposed. Computational and numerical sensitivity issues for this method are considered. An algorithm, based on this method, is developed and demonstrated on a simple biological example of microbial growth with Michaelis-Menten kinetics. The new method is applied to the motivating NF-kappaB example to show its suitability for use in systems biology.

Analysis of Biological Systems

Author: Corrado Priami

Publisher:

Published: 2015

Total Pages: 414

ISBN-13: 9781783266876

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Modeling is fast becoming fundamental to understanding the processes that define biological systems. High-throughput technologies are producing increasing quantities of data that require an ever-expanding toolset for their effective analysis and interpretation. Analysis of high-throughput data in the context of a molecular interaction network is particularly informative as it has the potential to reveal the most relevant network modules with respect to a phenotype or biological process of interest. Analysis of Biological Systems collects classical material on analysis, modeling and simulation, thereby acting as a unique point of reference. The joint application of statistical techniques to extract knowledge from big data and map it into mechanistic models is a current challenge of the field, and the reader will learn how to build and use models even if they have no computing or math background. An in-depth analysis of the currently available technologies, and a comparison between them, is also included. Unlike other reference books, this in-depth analysis is extended even to the field of language-based modeling. The overall result is an indispensable, self-contained and systematic approach to a rapidly expanding field of science.

Book Synopsis Analysis of Biological Systems by : Corrado Priami

Download or read book Analysis of Biological Systems written by Corrado Priami and published by . This book was released on 2015 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling is fast becoming fundamental to understanding the processes that define biological systems. High-throughput technologies are producing increasing quantities of data that require an ever-expanding toolset for their effective analysis and interpretation. Analysis of high-throughput data in the context of a molecular interaction network is particularly informative as it has the potential to reveal the most relevant network modules with respect to a phenotype or biological process of interest. Analysis of Biological Systems collects classical material on analysis, modeling and simulation, thereby acting as a unique point of reference. The joint application of statistical techniques to extract knowledge from big data and map it into mechanistic models is a current challenge of the field, and the reader will learn how to build and use models even if they have no computing or math background. An in-depth analysis of the currently available technologies, and a comparison between them, is also included. Unlike other reference books, this in-depth analysis is extended even to the field of language-based modeling. The overall result is an indispensable, self-contained and systematic approach to a rapidly expanding field of science.

Large Scale Model Identification in Systems Biology

Author: Kapil G. Gadkar

Publisher:

Published: 2005

Total Pages: 358

ISBN-13:

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Book Synopsis Large Scale Model Identification in Systems Biology by : Kapil G. Gadkar

Download or read book Large Scale Model Identification in Systems Biology written by Kapil G. Gadkar and published by . This book was released on 2005 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Identifiability and Observability in Epidemiological Models

Author: Nik Cunniffe

Publisher: Springer Nature

Published:

Total Pages: 115

ISBN-13: 9819725399

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Book Synopsis Identifiability and Observability in Epidemiological Models by : Nik Cunniffe

Download or read book Identifiability and Observability in Epidemiological Models written by Nik Cunniffe and published by Springer Nature. This book was released on with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Investigating Biological Systems Using Modeling

Author: Meryl E. Wastney

Publisher: Academic Press

Published: 1999

Total Pages: 401

ISBN-13: 0127367403

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Investigating Biological Systems Using Modeling describes how to apply software to analyze and interpret data from biological systems. It is written for students and investigators in lay person's terms, and will be a useful reference book and textbook on mathematical modeling in the design and interpretation of kinetic studies of biological systems. It describes the mathematical techniques of modeling and kinetic theory, and focuses on practical examples of analyzing data. The book also uses examples from the fields of physiology, biochemistry, nutrition, agriculture, pharmacology, and medicine. Contains practical descriptions of how to analyze kinetic data Provides examples of how to develop and use models Describes several software packages including SAAM/CONSAM Includes software with working models

Book Synopsis Investigating Biological Systems Using Modeling by : Meryl E. Wastney

Download or read book Investigating Biological Systems Using Modeling written by Meryl E. Wastney and published by Academic Press. This book was released on 1999 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigating Biological Systems Using Modeling describes how to apply software to analyze and interpret data from biological systems. It is written for students and investigators in lay person's terms, and will be a useful reference book and textbook on mathematical modeling in the design and interpretation of kinetic studies of biological systems. It describes the mathematical techniques of modeling and kinetic theory, and focuses on practical examples of analyzing data. The book also uses examples from the fields of physiology, biochemistry, nutrition, agriculture, pharmacology, and medicine. Contains practical descriptions of how to analyze kinetic data Provides examples of how to develop and use models Describes several software packages including SAAM/CONSAM Includes software with working models

Statistical Methods in Biology

Author: S.J. Welham

Publisher: CRC Press

Published: 2014-08-22

Total Pages: 606

ISBN-13: 1439808783

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Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural sciences to which they are being applied, drawing on relevant examples from the authors’ experience. Taking a practical and intuitive approach, the book only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat® statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R. By the time you reach the end of the book (and online material) you will have gained: A clear appreciation of the importance of a statistical approach to the design of your experiments, A sound understanding of the statistical methods used to analyse data obtained from designed experiments and of the regression approaches used to construct simple models to describe the observed response as a function of explanatory variables, Sufficient knowledge of how to use one or more statistical packages to analyse data using the approaches described, and most importantly, An appreciation of how to interpret the results of these statistical analyses in the context of the biological or agricultural science within which you are working. The book concludes with a guide to practical design and data analysis. It gives you the understanding to better interact with consultant statisticians and to identify statistical approaches to add value to your scientific research.

Book Synopsis Statistical Methods in Biology by : S.J. Welham

Download or read book Statistical Methods in Biology written by S.J. Welham and published by CRC Press. This book was released on 2014-08-22 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written in simple language with relevant examples, Statistical Methods in Biology: Design and Analysis of Experiments and Regression is a practical and illustrative guide to the design of experiments and data analysis in the biological and agricultural sciences. The book presents statistical ideas in the context of biological and agricultural sciences to which they are being applied, drawing on relevant examples from the authors’ experience. Taking a practical and intuitive approach, the book only uses mathematical formulae to formalize the methods where necessary and appropriate. The text features extended discussions of examples that include real data sets arising from research. The authors analyze data in detail to illustrate the use of basic formulae for simple examples while using the GenStat® statistical package for more complex examples. Each chapter offers instructions on how to obtain the example analyses in GenStat and R. By the time you reach the end of the book (and online material) you will have gained: A clear appreciation of the importance of a statistical approach to the design of your experiments, A sound understanding of the statistical methods used to analyse data obtained from designed experiments and of the regression approaches used to construct simple models to describe the observed response as a function of explanatory variables, Sufficient knowledge of how to use one or more statistical packages to analyse data using the approaches described, and most importantly, An appreciation of how to interpret the results of these statistical analyses in the context of the biological or agricultural science within which you are working. The book concludes with a guide to practical design and data analysis. It gives you the understanding to better interact with consultant statisticians and to identify statistical approaches to add value to your scientific research.