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© 2020

Identifiability and Regression Analysis of Biological Systems Models

Statistical and Mathematical Foundations and R Scripts

Book

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-x
  2. Paola Lecca
    Pages 1-18
  3. Paola Lecca
    Pages 19-35
  4. Paola Lecca
    Pages 37-48
  5. Paola Lecca
    Pages 49-62
  6. Paola Lecca
    Pages 63-79
  7. Back Matter
    Pages 81-82

About this book

Introduction

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.

Keywords

Model identifiability Regression analysis Stiff dynamics Non-linear dynamics Parameter inference Self-starting models Network inference Factor analysis Latent class models

Authors and affiliations

  1. 1.Faculty of Computer ScienceFree University of Bozen-BolzanoBozen-BolzanoItaly

About the authors

Paola Lecca is an Assistant Professor at the Faculty of Computer Science, Free University of Bolzano, where she conducts theoretical and applied research on graph theory, dynamical network modelling and analysis, statistical inference, and parallel computing. Prof. Lecca is a member of the Smart Data Factory, the Laboratory for Technology Transfer, and the Laboratory for Advanced Computing and Systems at the Free University of Bolzano, which focus on transforming theoretical studies on data management, mathematical analysis, algebra and statistics into technologies for various fields of application.

Moreover, Prof. Lecca contributes to the development of high-performance software for complex dynamical network simulation and knowledge inference as a Senior Member of the Association for Computing Machinery, New York, USA. She also serves on the Advisory Board of the AIR Institute, Spain, which actively works to promote innovation in computer science, artificial intelligence and information and communication technologies. The author of over one hundred publications including books and journal and conference papers on computational biology, bioinformatics, and biophysics, she also serves as an editor and reviewer for high Impact Factor journals in these areas.

Bibliographic information

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