Dynamic Systems Biology Modeling and Simulation

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.

Table of Contents:
1. Biosystem Modeling and Simulation: Nomenclature and Philosophy2. Math Models of Systems: Biomodeling 1013. Computer Simulation Methods4. Structural Biomodeling from Theory & Data: Compartmentalizations5. Structural Biomodeling from Theory & Data: Sizing, Distinguishing & Simplifying Multicompartmental Models6. Nonlinear Mass Action & Biochemical Kinetic Interaction Modeling7. Cellular Systems Biology Modeling: Deterministic & Stochastic8. Physiologically Based, Whole-Organism Kinetics & Noncompartmental Modeling9. Biosystem Stability & Oscillations10. Structural Identifiability11. Parameter Sensitivity Methods12. Parameter Estimation & Numerical Identifiability13. Parameter Estimation Methods II: Facilitating, Simplifying & Working With Data14. Biocontrol System Modeling, Simulation, and Analysis15. Data-Driven Modeling and Alternative Hypothesis Testing16. Experiment Design and Optimization17. Model Reduction and Network Inference in Dynamic Systems Biology

About the Author :
“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. He introduced the notions of interval and quasi-identifiablity of unidentifiable dynamic system models, and his lab has developed symbolic algorithmic approaches and new internet software (web app COMBOS) for computing identifiable parameter combinations. These are the aggregate parts of otherwise unidentifiable models that can be quantified – with broad application in model reduction (simplification) and experiment design. His long-term contributions to quantitative understanding of thyroid hormone production and metabolism in mammals and fishes have recently been crystallized into web app THYROSIM – for internet-based research and teaching about thyroid hormone dynamics in humans. Last but not least, Professor Joe is a passionate straight-ahead jazz saxophone player (alto and tenor), an alternate career begun in the 1950s in NYC at Stuyvesant High School – temporarily suspended when he started undergrad school, and resumed again in middle-age. He recently added flute to his practice schedule and he and his band – Acoustically Speaking –can be found occasionally gigging in Los Angeles or Honolulu haunts.

Review :
"I am just in awe of your ability to start with simple ideas and use them to explain sophisticated concepts and methodologies in modeling biochemical and cellular systems (Chapters 6 and 7). This is a great new contribution to the textbook offerings in systems biology." Alex Hoffmann, Director of the San Diego Center for Systems Biology and the UCSD Graduate Program in Bioinformatics and Systems Biology "I found Chapter 1 to be a marvel of heavy-lifting, done so smoothly there was no detectable sweat. Heavy-lifting because you laid out the big load of essential vocabulary and concepts a reader has to have to enter the world of biomodeling confidently. In that chapter you generously acknowledge some us who tried to accomplish this earlier but, compared to your Chapter 1, we were clumsy and boring. For me, now, Chapter 1 was a "page-turner" to be enjoyed straight through. You have the gift of a master athlete who does impossible performances and makes them seem easy. "Your Chapter 9 - on oscillations and stability - is a true jewel. I have a shelf full of books etc on nonlinear mechanics and system analyses and modeling, but nothing to match the clarity and deep understanding you offer the reader. You are a great explainer and teacher." F. Eugene Yates, Emeritus Professor of Medicine, Chemical Engineering and Ralph and Marjorie Crump Professor of Biomedical Engineering, UCLA "Chapter 4 covers many aspects of the notion of compartmentalization in the structural modeling of biomedical and biological models - both linear and nonlinear. Developments are biophysically motivated throughout; and compartments are taken to represent entities with the same dynamic characteristics (dynamic signatures). A very positive feature of this text is the numerous worked examples in the text, which greatly help readers follow the material. At the end of the chapter, there are further well thought out analytical and simulation exercises that will help readers check that they have understood what has been presented. "Chapter 5 looks at many important aspects of multicompartmental modeling, examining in more detail how output data limit what can be learnt about model structure, even when such data are perfect. Among the many features explained are how to establish the size and complexity of a model; how to select between several candidate models; and whether it is possible to simplify a model. All of this is done with respect to the dynamic signatures in the model. As in Chapter 4, readers are helped to understand the often challenging material by means of numerous worked examples in the text, and there are further examples given at the end." Professor Keith Godfrey, University of Warwick, Coventry, U.K.