Dynamic Systems Biology Modeling and Simuation consolidates and unifies classical and contemporary multiscale methodologies for mathematical modeling and computer simulation of dynamic biological systems – from molecular/cellular, organ-system, on up to population levels. The book pedagogy is developed as a well-annotated, systematic tutorial – with clearly spelled-out and unified nomenclature – derived from the author’s own modeling efforts, publications and teaching over half a century. Ambiguities in some concepts and tools are clarified and others are rendered more accessible and practical. The latter include novel qualitative theory and methodologies for recognizing dynamical signatures in data using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for quantification feasibility. The level is basic-to-intermediate, with much emphasis on biomodeling from real biodata, for use in real applications.
- Introductory coverage of core mathematical concepts such as linear and nonlinear differential and difference equations, Laplace transforms, linear algebra, probability, statistics and stochastics topics; PLUS …….
- The pertinent biology, biochemistry, biophysics or pharmacology for modeling are provided, to support understanding the amalgam of “math modeling” with life sciences.
- Strong emphasis on quantifying as well as building and analyzing biomodels: includes methodology and computational tools for parameter identifiability and sensitivity analysis; parameter estimation from real data; model distinguishability and simplification; and practical bioexperiment design and optimization.
- Companion website provides solutions and program code for examples and exercises using Matlab, Simulink, VisSim, SimBiology, SAAMII, AMIGO, Copasi and SBML-coded models.
- A full set of PowerPoint slides are available from the author for teaching from his textbook. He uses them to teach a 10 week quarter upper division course at UCLA, which meets twice a week, so there are 20 lectures. They can easily be augmented or stretched for a 15 week semester course.
Importantly, the slides are editable, so they can be readily adapted to a lecturer’s personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content.
The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: email@example.com
upper-division undergraduate, graduate level, and research level students systems biology, computational biology, biomathematics, biomedical engineering (bioengineering), pharmacology and areas using contemporary dynamical biosystem modeling and simulation methodology.
- Preface to the First Edition
- Pedagogical Struggles
- Crystallizing and Focusing – My Way
- How to Use this Book in the Classroom
- Chapter 1. Biosystem Modeling & Simulation: Nomenclature & Philosophy
- Modeling Definitions
- Modeling Essential System Features
- Primary Focus: Dynamic (Dynamical) System Models
- Measurement Models & Dynamic System Models Combined: Important!
- Top-Down & Bottom-Up Modeling
- Source & Sink Submodels: One Paradigm for Biomodeling with Subsystem Components
- Systems, Integration, Computation & Scale in Biology
- Overview of the Modeling Process & Biomodeling Goals
- Looking Ahead: A Top-Down Model of the Chapters
- Chapter 2. Math Models of Systems: Biomodeling 101
- Some Basics & a Little Philosophy
- Algebraic or Differential Equation Models
- Differential & Difference Equation Models
- Different Kinds of Differential & Difference Equation Models
- Linear & Nonlinear Mathematical Models
- Piecewise-Linearized Models: Mild/Soft Nonlinearities
- Solution of Ordinary Differential (ODE) & Difference Equation (DE) Models
- Special Input Forcing Functions (Signals) & Their Model Responses: Steps & Impulses
- State Variable Models of Continuous-Time Systems
- Linear Time-Invariant (TI) Discrete-Time Difference Equations (DEs) & Their Solution
- Linearity & Superposition
- Laplace Transform Solution of ODEs
- Transfer Functions of Linear TI ODE Models
- More on System Stability
- Looking Ahead
- Chapter 3. Computer Simulation Methods
- No. of pages:
- © Academic Press 2014
- 7th November 2013
- Academic Press
- eBook ISBN:
- Hardcover ISBN:
“Professor Joe” – as he is called by his students – is a Distinguished Professor of Computer Science and Medicine and Chair of the Computational & Systems Biology Interdepartmental Program at UCLA – an undergraduate research-oriented program he nurtured and honed over several decades. As an active full-time member of the UCLA faculty for nearly half a century, he also developed and led innovative graduate PhD programs, including Computational Systems Biology in Computer Science, and Biosystem Science and Engineering in Biomedical Engineering. He has mentored students from these programs since 1968, as Director of the UCLA Biocybernetics Laboratory, and was awarded the prestigious UCLA Distinguished Teaching Award and Eby Award for Creative Teaching in 2003, and the Lockeed-Martin Award for Teaching Excellence in 2004. Professor Joe also is a Fellow of the Biomedical Engineering Society. Visiting professorships included stints at universities in Canada, Italy, Sweden and the UK and he was a Senior Fulbright-Hays Scholar in Italy in 1979. Professor Joe has been very active in the publishing world. As an editor, he founded and was Editor-in-Chief of the Modeling Methodology Forum – a department in seven of the American Journals of Physiology – from 1984 thru 1991. As a writer, he authored or coauthored both editions of Feedback and Control Systems (Schaum- McGraw-Hill 1967 and 1990), more than 200 research articles, and recently published his opus textbook: Dynamic Systems Biology Modeling and Simulation (Academic Press/Elsevier November 2013 and February 2014). Much of his research has been based on integrating experimental neuroendocrine and metabolism studies in mammals and fishes with data-driven mathematical modeling methodology – strongly motivated by his experiences in “wet-lab”. His seminal contributions to modeling theory and practice are in structural identifiability (parameter ambiguity) analysis, driven by experimental encumbrances.
Distinguished Professor Computer Science, Medicine & Biomedical Engineering Chair, Computational & Systems Biology Interdepartmental Program UCLA Los Angeles CA
"This very satisfying book has multiple strengths. The text has marvelous clarity, as do the mathematical demonstrations. All are synoptic, while simultaneously explaining the underlying, fine details. The useful organization is enhanced by superb graphics. Although the author has many technical capabilities, with both range and depth, below I’ll give just one illustrative example of the excellent result. Major themes of modern computation and modeling, as applied to biology, include issues of nonlinearities, chaotic dynamics, emergent properties, and instabilities. For example, consider the problems attendant on complex dynamic systems with multiple scales of time and space so typical of living systems. The scientific literature in this domain is rich and immense. When I looked into DiStefano’s book for entries dealing with these topics, I found as early as Chapter One a heading: ”Multiscale Modeling”. Elsewhere were other treatments of these aspects of complexity and modeling difficulties such as the famous problem of “stiff ODEs”, here brilliantly examined and explained, with remedies. The many authoritative tutorials by DiStefano amazed me for so effectively distilling the technical essences. They confirm that DiStefano is a great teacher and guide through various profound, classical difficulties. This book is a masterwork."--F. Eugene Yates
"This book provides a systematic review of the concepts of mathematical modeling in various fields. With its simple language, varied practical examples, quick references, appendixes, and clear basic concepts, it provides a thorough explanation of the subject. The well-organized chapters, along with the use of different notations and typescripts, make it a user-friendly book." Rating: 5 Stars--Doody.com, March 7, 2014
"DiStefano presents this interdisciplinary text merging mathematics, modeling, systems science, and biology. The first chapter introduces th