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• Title: Introduction to Systems BiologyTable of ContentsI. Introductio
• n1. Scientific Challenges in Systems Biology- Hiroaki Kitan
• o2. Bringing Genomes to Life: The Use of Genome-Scale in silico Models- Ines Thiele and Bernhard O. Palsso
• n3. From Gene Expression to Metabolic Fluxes- Ana Paula Oliveira, Michael C. Jewett and Jens NielsenII. Experimental Techniques for Systems Biolog
• y4. Handling and Interpreting Gene Groups- Nils Bluthgen, Szymon M. Kielbasa, and Dieter Beul
• e5. The Dynamic Transcriptome of Mice- Yuki Hasegawa and Yoshihide Hayashizak
• i6. Dissecting Transcriptional Control Networks- Vijayalakshmi H. Nagaraj and Anirvan M. Sengupt
• a7. Reconstruction and Structural Analysis of Metabolic and RegulatoryNetworks- Hong-wu Ma, Marcio Rosa Da Silva, Ji-Bin Sun, Bharani Kumar, andAn-Ping Zen
• g8. Cross-Species Comparison Using Expression Data- Gaelle Lelandais and Stephane Le Cro
• m9. Methods for Protein-Protein Interaction Analysis- Keiji Kito and Takashi It
• o10. Genome-Scale Assessment of Phenotypic Changes during AdaptiveEvolution- Stephen S. Fon
• g11. Location Proteomics- Ting Zhao, Shann-Ching Chen, and Robert F. MurphyIII. Theoretical and Modeling Technique
• s12. Reconstructing Transcriptional Networks using Gene ExpressionProfiling and Bayesian State Space Models- Matthew J. Beal, Juan Li, Zoubin Ghahramani, and David L. Wil
• d13. Modeling Spatiotemporal Dynamics of Multicellular Signaling- Hao Zhu and Pawan K Dha
• r14. Kinetics of Dimension Restricted Conditions- Noriko Hiroi and Akira Funahash
• i15. Mechanisms Generating Ultrasensitivity, Bistability andOscillations in Signal Transduction- Nils Bluthgen, Stefan Legewie, Hanspeter Herzel, and Boris Kholodenk
• o16. Employing Systems Biology to Quantify Receptor Tyrosine KinaseSignaling in Time and Space- Boris N. Kholodenk
• o17. Dynamic Instabilities within Living Neutrophils- Howard R. Petty, Roberto Romero, Lars F. Olsen, and Ursula Kumme
• r18. Efficiency, Robustness and Stochasticity of Gene RegulatoryNetworks in Systems Biology: E Switch as a Working Example- Xiaomei Zhu, Lan Yin, Leroy Hood, David Galas, and Ping A
• o19. Applications, Representation and Management of Signaling PathwayInformation: Introduction to the SigPath Project- Eliza Chan and Fabien CampagneIV. Methods and Software Platforms for Systems Biolog
• y20. SBML Models and MathSBML- Bruce E. Shapiro, Andrew Finney, Michael Hucka, Benjamin Bornstein, Akira Funahashi, Akiya Jouraku, Sarah M. Keating, Nicolas Le Novere, Joanne Matthews, and Maria J. Schilstr
• a21. CellDesigner: A Graphical Biological Network Editor and WorkbenchInterfacing Simulator- Akira Funahashi, Mineo Morohashi, Yukiko Matsuoka, Akiya Jouraku, andHiroaki Kitan
• o22. DBRF-MEGN Method: An Algorithm for Inferring Gene RegulatoryNetworks from Large-Scale Gene Expression Profiles- Koji Kyoda and Shuichi Onam
• i23. Systematic Determination of Biological Network Topology:Non-Integral Connectivity Method (NICM)- Kumar Selvarajoo and Masa Tsuchiy
• a24. Storing, Searching and Disseminating Experimental Proteomics Data- Norman W. Paton, Andrew R. Jones, Chris Garwood, Kevin Garwood, andStephen Olive
• r25. Representing and Analyzing Biochemical Networks using BioMaze- Yves Deville, Christian Lemer, and Shoshana Wodak.
• (source: Nielsen Book Data)

Introduction to Systems Biology is an introductory text for undergraduate and graduate students who are interested in comprehensive biological systems. The authors provide a broad overview of the field using key examples and typical approaches to experimental design. The volume begins with an introduction to systems biology and then details experimental omics tools. Other sections introduce the reader to challenging computational approaches to help understand biological dynamic systems. The final sections of the volume provide ideas for theoretical and modeling optimization in systemic biological researches, presenting most algorithms as implementations, including an up-to-date full range of bioinformatic programs and available successful applications. Informative and cutting-edge, this volume presents a clear and intuitive illustration of the biological systemic approaches and introduces ideal computational methods for research. Introduction to Systems Biology is an indispensable resource, providing a first glimpse into the state-of-the-art in systems biology.
(source: Nielsen Book Data)

• 1. Introduction
• Part I. Complexity: 2. Constitutive complexity
• 3. Dynamic complexity
• 4. Evolved diversity
• Part II. Pluralism: 5. Laws
• 6. Pluralism or disunity.
• (source: Nielsen Book Data)

This fine collection of essays by a leading philosopher of science presents a defence of integrative pluralism as the best description for the complexity of scientific inquiry today. The tendency of some scientists to unify science by reducing all theories to a few fundamental laws of the most basic particles that populate our universe is ill-suited to the biological sciences, which study multi-component, multi-level, evolved complex systems. This integrative pluralism is the most efficient way to understand the different and complex processes - historical and interactive - that generate biological phenomena. This book will be of interest to students and professionals in the philosophy of science.
(source: Nielsen Book Data)

• 1. Introduction
• Part I. Complexity: 2. Constitutive complexity
• 3. Dynamic complexity
• 4. Evolved diversity
• Part II. Pluralism: 5. Laws
• 6. Pluralism or disunity.
• (source: Nielsen Book Data)

This fine collection of essays by a leading philosopher of science presents a defence of integrative pluralism as the best description for the complexity of scientific inquiry today. The tendency of some scientists to unify science by reducing all theories to a few fundamental laws of the most basic particles that populate our universe is ill-suited to the biological sciences, which study multi-component, multi-level, evolved complex systems. This integrative pluralism is the most efficient way to understand the different and complex processes - historical and interactive - that generate biological phenomena. This book will be of interest to students and professionals in the philosophy of science.
(source: Nielsen Book Data)

• 1. Algorithmic systems biology. 1.1. Converging sciences. 1.2. The approach. 1.3. Structure of the book. 1.4. Summary. 1.5. Further reading
• 2. Setting the context. 2.1. The structure of the cell. 2.2. DNA, RNA and genes. 2.3. Proteins. 2.4. Metabolites. 2.5. Cellular processes. 2.6. Experimental methods. 2.7. Summary. 2.8. Further reading
• 3. Systems and models. 3.1. Systems. 3.2. Model. 3.3. Summary. 3.4. Further reading
• 4. Static modeling technologies. 4.1. Preliminary assessment. 4.2. Linear regression. 4.3. Dimensionality reduction methods. 4.4. Clustering. 4.5. Gene set analysis. 4.6. Analysis of biological networks. 4.7. Summary. 4.8. Further reading
• 5. Dynamic modeling technologies. 5.1. Equation-based approaches. 5.2. Rewriting systems. 5.3. Network-based approaches. 5.4. Automata-based approaches. 5.5. Relationship between continuous and stochastic models. 5.6. Diagrammatic modeling. 5.7. Summary. 5.8. Further reading
• 6. Language-based modeling. 6.1. Process calculi. 6.2. Third generation: from calculi to modeling languages. 6.3. Self-assembly. 6.4. An evolutionary framework. 6.6. Summary. 6.7. Further reading
• 7. Dynamic modeling process. 7.1. Setting the objectives and the acceptance criteria. 7.2. Building the knowledge base. 7.3. From the knowledge base to a model schema. 7.4. From the model schema to a concrete model. 7.5. Model calibration, evaluation and refinement. 7.6. Summary. 7.7. Further reading
• 8. Simulation. 8.1. Model execution. 8.2. Random number generation. 8.3. Stochastic simulation algorithms. 8.4. Summary. 8.5. Further reading
• 9. Perspectives and conclusions.

Modeling is fast becoming fundamental to understanding the processes that define biological systems.
(source: Nielsen Book Data)

• Foreword xiii
• Luis Farinas Del Cerro Chapter 1 Symbolic Representation and Inference or Regulatory Network Structures 1 Nataly Maimari, Krysia Broda, Antonis Kakas, Rob Krams and Alessandra Russo Chapter 2 Reasoning on the Response of Logical Signaling Networks with ASP 49 Torsten Schaub, Anne Siegek and Santiago Videla Chapter 3 A Logical Model for Molecular Interaction Maps 93 Robert DeMolombe, Luis Farinas Del Cerro and Naji Obeid Chapter 4 Analyzing Large Network Dynamics with Process Hitting 125 Loic Pauleve, Courtney Chancellor, Maxime Folschette, Morgan Magnin and Olivier Roux Chapter 5 ASP for Construction and Validation of Regulatory Biological Networks Alexandre Rocca, Nicolas Mobilia, Eric Fanchon, Tony Ribeiro, Laurent Trilling and Katsumi Inoue Chapter 6 Simulation-Based Reasoning about Biological Pathways Using Petri Nets and ASP 207 Saadat Anwar, Chitta Barbal and Katsumi Inoue Chapter 7 Formal Methods Applied to Gene Networks Modeling 245 Gilles Bernot, Jean-Paul Comet and El Houssine Snaussi Chapter 8 Temporal Logic Modeling of Dynamical Behaviors: First-Order Patterns and Solvers 291 Francois Fages and Pauline Traynard Chapter 9 Analyzing SBGN-AF Networks Using Normal Logic Programs 325 Adrien Rougny, Christine Froidevaux, Yoshitaka Yamamoto and Katsumi Inoue Chapter 10 Machine Learning of Biological Networks Using Abductive ILP 363 Alireza Tamassoni, Diahuan Lin, Hiroaki Watanabe, Jianzhong Chen and Stephen Muggleton List of Authors 403 Index 407.
• (source: Nielsen Book Data)

• Biological Systems and Dynamics In the Beginning The Hemodynamic System Cheyne-Stokes Respiration
• Population Dynamics of a Single Species Fibonacci, Malthus and Nicholsons Blowflies Fixed Points and Stability of a One-Dimensional First Order Difference Equation The Cobweb Diagram An Example: Hormone Secretion Higher Dimensional Maps Period Doubling Bifurcation in Infant Respiration
• Observability of Dynamic Variables Bioelectric Phenomena Measurement ECG, EEG, EMG, EOG and All That Measuring Movement Measuring Temperature Measuring Oxygen Concentration Biomedical Imaging The Importance of Measurement
• Biomedical Signal Processing Segmentation Error Measure and Automatic Analysis of EEGs ECG Signal Processing Vector Cardiography Embedology and State Space Representation Fractals, Chaos and Nonlinear Dynamics Prediction
• Computational Neurophysiology The Cell Action Potentials and Ion Channels Ficks Law, Ohms Law and the Einstein Relation Cellular Equilibrium: Nernst and Goldman Equivalent Circuits Dendrites
• Mathematical Neurodynamics Hodgkin, Huxley and the Squid Giant Axon FitzHugh-Nagumo Model Fixed Points and Stability of a One-Dimensional Differential Equation Nullclines and Phase Plane Pitchfork and Hopf Bifurcations in Two Dimensions Excitability
• Population Dynamics Predator-Prey Interactions Fixed Points and Stability of Two-Dimensional Differential Equations Disease Models: SIS, SIR and SEIR SARS in Hong Kong
• Action, Reaction and Diffusion Black Death and Spatial Disease Transmission Reaction-Diffusion Cardiac Dynamics
• Autonomous Agents Flocking Celluloid Penguins and Roosting Starlings Evaluating Crowd Simulations
• Complex Networks Human Networks: Growing Complex Networks Small World Networks of Spread of SARS Global Spread of Avian Influenza Complex Disease Transmission and Immunisation Complex Networks Constructed from Musical Composition Interaction of Grazing Herbivores Neuronal Networks and Complex Networks
• Conclusion Models Are a Reflection of Reality
• References
• A Summary appears at the end of each chapter.
• (source: Nielsen Book Data)

From the spontaneous rapid firing of cortical neurons to the spatial diffusion of disease epidemics, biological systems exhibit rich dynamic behaviour over a vast range of time and space scales. Unifying many of these diverse phenomena, Dynamics of Biological Systems provides the computational and mathematical platform from which to understand the underlying processes of the phenomena. Through an extensive tour of various biological systems, the text introduces computational methods for simulating spatial diffusion processes in excitable media, such as the human heart, as well as mathematical tools for dealing with systems of nonlinear ordinary and partial differential equations, such as neuronal activation and disease diffusion. The mathematical models and computer simulations offer insight into the dynamics of temporal and spatial biological systems, including cardiac pacemakers, artificial electrical defibrillation, pandemics, pattern formation, flocking behaviour, the interaction of autonomous agents, and hierarchical and structured network topologies. Tools from complex systems and complex networks are also presented for dealing with real phenomenological systems. With exercises and projects in each chapter, this classroom-tested text shows students how to apply a variety of mathematical and computational techniques to model and analyze the temporal and spatial phenomena of biological systems. MATLAB(R) implementations of algorithms and case studies are available on the author's website.
(source: Nielsen Book Data)

• Introduction Systems Biology, Biological Knowledge, and Kinetic Modelling Dependence of enzyme reaction rate on the substrate concentration What are the model limitations? Or, in other words, what can be modeled? Cellular Networks Reconstruction and Static Modelling Pathway reconstruction The high-quality network reconstruction: description of the process Visual notations: three categories Edinburgh Pathway Editor (EPE) Introduction Feature summary of EPE A flexible visual representation Conclusion Construction and Verification of Kinetic Models Introduction Basic principles of kinetic model construction Basic principles of kinetic model verification Study of dynamic and regulatory properties of the kinetic model Introduction to DBSolve Creation and analysis of the models using DBSolve: functional description Enzyme Kinetics Modelling Introduction Basic principles of modeling individual enzymes and transporters "Hyperbolic" enzymes Allosteric enzymes Transporters Kinetic Models of Biochemical Pathways Modelling of the mitochondrial Krebs cycle Modelling of the Escherichia coli branched-chain amino acid biosynthesis Modelling of Mitochondrial Energy Metabolism Oxidative phosphorylation and superoxide production in mitochondria Development of kinetic models Description of individual processes of the model Model predictions Application of the Kinetic Modelling Approach to Problems in Biotechnology and Biomedicine Study of the mechanisms of salicylate-hepatotoxic effect Multiple target identification analysis for antituberculosis drug discovery Application of the kinetic model of E. coli branched-chain amino acid biosynthesis to optimize production of isoleucine and valine Conclusion and Discussion References Index.
• (source: Nielsen Book Data)

With more and more interest in how components of biological systems interact, it is important to understand the various aspects of systems biology. "Kinetic Modelling in Systems Biology" focuses on one of the main pillars in the future development of systems biology. It explores both the methods and applications of kinetic modeling in this emerging field. The book introduces the basic biological cellular network concepts in the context of cellular functioning, explains the main aspects of the Edinburgh Pathway Editor (EPE) software package, and discusses the process of constructing and verifying kinetic models. It presents the features, user interface, and examples of DBSolve as well as the principles of modeling individual enzymes and transporters.The authors describe how to construct kinetic models of intracellular systems on the basis of models of individual enzymes. They also illustrate how to apply the principles of kinetic modeling to collect all available information on the energy metabolism of whole organelles, construct a kinetic model, and predict the response of the organelle to changes in external conditions. The final chapter focuses on applications of kinetic modeling in biotechnology and biomedicine. Encouraging readers to think about future challenges, this book will help them understand the kinetic modeling approach and how to apply it to solve real-life problems.Extensively used throughout the text for pathway visualization and illustration, the EPE software is available on the accompanying CD-ROM. The CD also includes pathway diagrams in several graphical formats, DBSolve installation with examples, and all models from the book with dynamic visualization of simulation results, allowing readers to perform in silico simulations and use the models as templates for further applications.
(source: Nielsen Book Data)

• I. Enabling Information and Integration Technologies for Systems Biology II. Foundations of Biochemical Network Analysis and Modeling III. Computer Simulations of Dynamic Networks IV. Multi-Scale Representations of Cells and Emerging Phenotypes.
• (source: Nielsen Book Data)

"Systems Biology" is concerned with the quantitative study of complex biosystems at the molecular, cellular, tissue, and systems scales. Its focus is on the function of the system as a whole, rather than on individual parts. This exciting new arena applies mathematical modeling and engineering methods to the study of biological systems. This book is the first of its kind to focus on the newly emerging field of systems biology with an emphasis on computational approaches. The work covers new concepts, methods for information storage, mining and knowledge extraction, reverse engineering of gene and metabolic networks, as well as modelling and simulation of multi-cellular systems. Central themes include strategies for predicting biological properties and methods for elucidating structure-function relationships.
(source: Nielsen Book Data)

• Preface.- Principles.- Models of Systems.- The Modeling Process.- Qualitative Model Formulation.- Quantitative Model Formulation: I.- Quantitative Model Formulation: II.- Numerical Techniques.- Parameter Estimation.- Model Validation.- Model Analysis.- Stochastic Models.- Applications.- Photosynthesis and Plant Growth.- Hormonal Control in Mammals.- Populations and Individuals.- Chemostats.- Diseases.- Spatial Patterns and processes.- Scaling Models.- Chaos in Biology.- Cellular Automata and Recursive Growth.- Evolutionary Computation.- Bibliography.- Index.
• (source: Nielsen Book Data)

This is the second edition of a textbook currently published by Springer for a course in mathematical modeling and computer simulation for biologists at the advanced undergraduate and introductory graduate level. The audience for this edition is similar to that of the previous one: advanced level courses in computational biology, as well as researchers retooling themselves. This new edition includes a CD-ROM with real examples of models as teaching tools.
(source: Nielsen Book Data)

• Application of engineering science principles to biological systems
• Development of a general environmental control model
• Microbial systems
• Modeling the composting process
• Basics of plant growth
• Crop growth modeling
• Basics of animal growth
• Animal growth modeling.

• Introduction: What is Modeling?
• The Steps in Building a Model. The Difference Between Building a Model and Using a Model. Why Model Biological Systems?
• Modeling Software: Review of Software. WinSAAM. Concepts and Tools of Modeling: Building Models in Sections. Techniques and Tools to Facilitate Model Development. Strategies for Modeling Biological Systems: Experimental Design and Data Collection. Starting Modeling and Developing a Model. Rejecting Hypotheses and Accepting a Model. Model Summarization. Multiple Studies Analysis. Information in the Model. Errors in Compartmental Modeling. Testing Robustness: Sensitivity, Identifiability, and Stability. Evaluating and Using Published Models: Why Use a Published Model?
• Reviewing and Summarizing Published Models. The Model Translation Process. Verification and Validation. Using the Model. A Library of Models. Subject Index. Appendices.
• (source: Nielsen Book Data)

"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. It contains practical descriptions of how to analyze kinetic data. It provides examples of how to develop and use models. It describes several software packages including SAAM/CONSAM. It includes software with working models.
(source: Nielsen Book Data)

• Introduction * Physical and Biochemical Models * Genetics Models * Models of Organism * Single Population Models * Multiple Population Models * Catastrophe and Self-Organization * Conclusion: Building a Modeling Community * Appendix.
• (source: Nielsen Book Data)

Models help us understand the nonlinear dynamics of real-world processes by using the computer to mimic the actual forces that are known or assumed to result in a system's behavior. The complexity of biological systems, from cells to ecosystems, are particularly well suited to this method because the feedback processes governing them have made it difficult to identify natural laws describing their behavior. This book does not require a substantial background in mathematics or computer science and encourages all students and scholars to actively incorporate modeling into their education and research. Using STELLA and MADONNA software, "Modeling Dynamic Biological Systems" applies methods of computer simulation to real-world phenomena in a wide range of topics from laboratory biology and ecology. The book includes sections on physical and biochemical processes, genetics, single- and multiple-population dynamics, and catastrophe and self-organization. STELLA and MADONNA run-time software as well as computer files of sample models accompany the book on a CD-ROM. The software is compatible with both Macintosh- and Windows-based computers. The availability of powerful, intuitive software for developing and running simulation models of real-world phenomena promises a revolution in studying, teaching, and thinking about complex problems that range from the functioning of a cell to the functioning of an ecosystem to the functioning of a national economy. The books in the series "Modeling Dynamic Systems" will promote the spread of 'systems thinking' by integrating state-of-the-art modeling techniques with the theories and concepts of specific disciplines or interdisciplinary topics. The unifying theme of the series is the ease, power, and transparency of model-building.
(source: Nielsen Book Data)

This textbook introduces senior undergraduates and graduate students to the art and craft of creating and analyzing computer simulation models of biological systems. The field of biological modeling is huge, and the book is appropriately selective, nevertheless discusses a wide range of examples. The book assumes a minimum of mathematical sophistication on the part of the readers, and mathematical manipulations will not overwhelm them. The book assumes only some knowledge of single-variable linear regression and some basic calculus.

• Part One: Principles: Models of systems
• The modeling process
• Qualitative model formulation
• Quantitative model formulation
• Simulation Paradigms
• Numerical techniques
• Parameter estimation
• Model validation
• Model analysis
• Stochastic models
• Part Two: Applications: Photsynthesis and plant growth
• Hormonal control in mammals
• Populations and individuals
• Chemostats
• Spatial patterns and processes
• Scaling models
• Chaos in biology
• Cellular automata and growth
• Evolutionary computation
• Complex adaptive systems.
• (source: Nielsen Book Data)

Designed for advanced undergraduate and graduate students, this textbook describes the essentials on how to create and analyze mathematical and computer simulation models. It offers a comprehensive understanding of the underlying principles as well as details and equations applicable to a wide variety of biological systems and disciplines. The text contains two major sections, principles and applications. The first section discusses the principles of biological systems with a thorough description of the modeling activities of formulation, implementation, validation, and analysis. The section of the text, covering applications, takes a closer look at the fundamental equations and problems from various biological disciplines, such as biochemistry, plant and animal physiology, and ecology.
(source: Nielsen Book Data)