%{search_type} search results

We proposed to apply a chemical cross-linking, mass spectrometry and modeling method called MS3D to the structure determination of the rhodopsin-transducin membrane protein complex (RTC). Herein we describe experimental progress made to adapt the MS3D approach for characterizing membrane protein systems, and computational progress in experimental design, data analysis and protein structure modeling. Over the past three years, we have developed tailored experimental methods for all steps in the MS3D method for rhodopsin, including protein purification, a functional assay, cross-linking, proteolysis and mass spectrometry. In support of the experimental effort. we have out a data analysis pipeline in place that automatically selects the monoisotopic peaks in a mass spectrometric spectrum, assigns them and stores the results in a database. Theoretical calculations using 24 experimentally-derived distance constraints have resulted in a backbone-level model of the activated form of rhodopsin, which is a critical first step towards building a model of the RTC. Cross-linked rhodopsin-transducin complexes have been isolated via gel electrophoresis and further mass spectrometric characterization of the cross-links is underway.

• 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)

• 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)

This work includes proceedings from the Tenth International Conference on Artificial Life, marking two decades of interdisciplinary research in this growing scientific community. "Artificial Life" is an interdisciplinary effort to investigate the fundamental properties of living systems through the simulation and synthesis of life-like processes in artificial media. The field brings a powerful set of tools to the study of how high-level behavior can arise in systems governed by simple rules of interaction. This tenth volume marks two decades of research in this interdisciplinary scientific community, a period marked by vast advances in the life sciences. The field has contributed fundamentally to our understanding of life itself through computer models, and has led to novel solutions to complex real-world problems - from disease prevention to stock market prediction - across high technology and human society. The proceedings of the biennial A-life conference - which has grown over the years from a small workshop in Santa Fe to a major international meeting - reflect the increasing importance of the work to all areas of contemporary science.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the First International Workshop on Formal Methods in Systems Biology, FMSB 2008, held in Cambridge, UK, in June, 2008. The 9 revised full papers presented were carefully reviewed and selected from the workshop lectures that all were invited contributions. The purpose of this meeting was to identify techniques for the specification, development and verification of biological models. It also focused on the design of tools to execute and analyze biological models that can significantly advance our understanding of biological systems.
(source: Nielsen Book Data)

In this wide-ranging survey, Claus Emmeche outlines many of the challenges and controversies involved in the dynamic and curious science of artificial life. He describes the work being done by an international network of biologists, computer scientists and physicists who are using computers to study life as it could be, or as it might evolve under conditions different from those on Earth. Many artificial-life researchers believe that they can create new life in the computer by simulating the processes observed in traditional, biological life-forms. The flight of a flock of birds, for example, can be reproduced faithfully and in all its complexity by a relatively simple computer program that is designed to generate electronic "boids". Are these "boids" then alive? The central problem, Emmeche notes, lies in defining the salient differences between biological life and computer simulations of its processes. His study touches on every aspect of this complex and rapidly developing discipline, including its connections to artificial intelligence, chaos theory, computational theory, and studies of emergence.
(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)

• 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)

Working mostly alone, almost single-handedly writing 250,000 lines of computer code, Steve Grand produced Creatures[registered], a revolutionary computer game that allows players to create living beings complete with brains, genes, and hormonal systems - creatures that live and breathe and breed in real time on an ordinary desktop computer. Enormously successful, the game inevitably raises the question: What is artificial life? And in this book - a chance for the devoted fan and the curious onlooker to see the world from the perspective of an original philosopher-engineer and intellectual maverick - Steve Grand proposes an answer.
(source: Nielsen Book Data)

• 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)

This text is a "tour d'horizon" of who is developing what artificial life around the world today. The author has interviewed the leading researchers and developers of artificial life and has some scary predictions for the directions in which they take us. Telephone traffic is now so large that it can't be run by a single program, so BT are experimenting with various species of small "ant" programs which are autonomous and can breed and evolve better offspring by trial, error, and natural selection. Meanwhile harmless artificial life forms are already loose on the Internet; computer viruses and even robots are now able to evolve randomly like their biological counterparts. Protein-based computers are on the agenda: a team in Japan aim to build an organic brain as clever as a puppy. The convergence of technology with biology has big implications. Artificial life today is evolving beyond even its designers' control.
(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)