Statistical Optimization of Biological Systems

Author: Tapobrata Panda
Publisher: CRC Press
ISBN: 1466587091
Format: PDF, Docs
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A number of books written by statisticians address the mathematical optimization of biological systems, but do not directly address statistical optimization. Statistical Optimization of Biological Systems covers the optimization of bioprocess systems in its entirety, devoting much-needed attention to the experimental optimization of biological systems using statistical techniques. Employing real-life bioprocess optimization problems and their solutions as examples, this book: Describes experimental design from identifying process variables to selecting a screening design, applying response surface methodology, and conducting regression modeling Demonstrates the statistical analysis and optimization of different experimental designs, the results of which are used to establish important variables and optimum settings Details the optimization techniques employed to determine optimum levels of the process variables for both single- and multiple-response systems Discusses important experimental designs, such as evolutionary operation programs and Taguchi’s designs Delineates the concept of hybrid experimental design using the essence of a genetic algorithm Statistical Optimization of Biological Systems examines the complex nature of biological systems, the need for optimization, and the rationale of statistical and non-statistical optimization methods. More importantly, the book explains how to successfully apply mathematical and statistical techniques to the optimization of biological systems.

Optimization

Author: Kenneth Lange
Publisher: Springer Science & Business Media
ISBN: 1461458382
Format: PDF
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Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike a balance between presentation of mathematical theory and development of numerical algorithms. Building on students’ skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications. In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions.

Sustainable Biological Systems for Agriculture

Author: Megh R. Goyal
Publisher: CRC Press
ISBN: 135167658X
Format: PDF, Docs
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Sustainable Biological Systems for Agriculture: Emerging Issues in Nanotechnology, Biofertilizers, Wastewater, and Farm Machines explores and introduces the use of nanotechnology, biofertilizers, and design of farm machines in agriculture. The contributions are from India, Africa and the USA; the chapters emphasize sustainable solutions for the enhancement of agriculture processes. The volume provides a wealth of information on new and emerging issues in this interdisciplinary field. The book is divided into several sections: Potential Applications of Nanotechnology in Biological Systems Emerging Issues, Challenges and Specific Examples of Nanotechnology for Sustainable Biological Systems Potential of Nano- and Bio- fertilizers in Sustainable Agriculture Emerging Focus Areas in Biological Systems Performance of Farm Machines for Sustainable Agriculture The information provided here will be valuable to government agricultural professionals, scientists, researchers, farmers, and faculty and students all over the world.

Handbook of Statistical Systems Biology

Author: Michael Stumpf
Publisher: John Wiley & Sons
ISBN: 1119952042
Format: PDF, Mobi
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Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.

Making Sense of Complexity

Author: National Research Council
Publisher: National Academies Press
ISBN: 9780309169615
Format: PDF, Docs
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On April 26-28, 2001, the Board on Mathematical Sciences and Their Applications (BMSA) and the Board on Life Sciences of the National Research Council cosponsored a workshop on the dynamical modeling of complex biomedical systems. The workshop's goal was to identify some open research questions in the mathematical sciences whose solution would contribute to important unsolved problems in three general areas of the biomedical sciences: disease states, cellular processes, and neuroscience. The workshop drew a diverse group of over 80 researchers, who engaged in lively discussions. To convey the workshop's excitement more broadly, and to help more mathematical scientists become familiar with these very fertile interface areas, the BMSA appointed one of its members, George Casella, of the University of Florida, as rapporteur. He developed this summary with the help of two colleagues from his university, Rongling Wu and Sam S. Wu, assisted by Scott Weidman, BMSA director. This summary represents the viewpoint of its authors only and should not be taken as a consensus report of the BMSA or of the National Research Council.

Computational Neuroscience

Author: Eric L. Schwartz
Publisher: MIT Press
ISBN: 9780262691642
Format: PDF, Mobi
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The thirty original contributions in this book provide a working definition of"computational neuroscience" as the area in which problems lie simultaneously within computerscience and neuroscience. They review this emerging field in historical and philosophical overviewsand in stimulating summaries of recent results. Leading researchers address the structure of thebrain and the computational problems associated with describing and understanding this structure atthe synaptic, neural, map, and system levels.The overview chapters discuss the early days of thefield, provide a philosophical analysis of the problems associated with confusion between brainmetaphor and brain theory, and take up the scope and structure of computationalneuroscience.Synaptic-level structure is addressed in chapters that relate the properties ofdendritic branches, spines, and synapses to the biophysics of computation and provide a connectionbetween real neuron architectures and neural network simulations.The network-level chapters take upthe preattentive perception of 3-D forms, oscillation in neural networks, the neurobiologicalsignificance of new learning models, and the analysis of neural assemblies and local learningrides.Map-level structure is explored in chapters on the bat echolocation system, cat orientationmaps, primate stereo vision cortical cognitive maps, dynamic remapping in primate visual cortex, andcomputer-aided reconstruction of topographic and columnar maps in primates.The system-level chaptersfocus on the oculomotor system VLSI models of early vision, schemas for high-level vision,goal-directed movements, modular learning, effects of applied electric current fields on corticalneural activity neuropsychological studies of brain and mind, and an information-theoretic view ofanalog representation in striate cortex.Eric L. Schwartz is Professor of Brain Research and ResearchProfessor of Computer Science, Courant Institute of Mathematical Sciences, New York UniversityMedical Center. Computational Neuroscience is included in the System Development FoundationBenchmark Series.

Optimization Techniques in Statistics

Author: Jagdish S. Rustagi
Publisher: Elsevier
ISBN: 1483295710
Format: PDF
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Statistics help guide us to optimal decisions under uncertainty. A large variety of statistical problems are essentially solutions to optimization problems. The mathematical techniques of optimization are fundamentalto statistical theory and practice. In this book, Jagdish Rustagi provides full-spectrum coverage of these methods, ranging from classical optimization and Lagrange multipliers, to numerical techniques using gradients or direct search, to linear, nonlinear, and dynamic programming using the Kuhn-Tucker conditions or the Pontryagin maximal principle. Variational methods and optimization in function spaces are also discussed, as are stochastic optimization in simulation, including annealing methods. The text features numerous applications, including: Finding maximum likelihood estimates Markov decision processes Programming methods used to optimize monitoring of patients in hospitals Derivation of the Neyman-Pearson lemma The search for optimal designs Simulation of a steel mill Suitable as both a reference and a text, this book will be of interest to advanced undergraduate or beginning graduate students in statistics, operations research, management and engineering sciences, and related fields. Most of the material can be covered in one semester by students with a basic background in probability and statistics. Key Features * Covers optimization from traditional methods to recent developments such as Karmarkars algorithm and simulated annealing * Develops a wide range of statistical techniques in the unified context of optimization * Discusses applications such as optimizing monitoring of patients and simulating steel mill operations * Treats numerical methods and applications Includes exercises and references for each chapter * Covers topics such as linear, nonlinear, and dynamic programming, variational methods, and stochastic optimization

Formal Modeling Actors Open Systems Biological Systems

Author: Gul Agha
Publisher: Springer
ISBN: 3642249337
Format: PDF, ePub, Docs
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This Festschrift volume, published in honor of Carolyn Talcott on the occasion of her 70th birthday, contains a collection of papers presented at a symposium held in Menlo Park, California, USA, in November 2011. Carolyn Talcott is a leading researcher and mentor of international renown among computer scientists. She has made key contributions to a number of areas of computer science including: semantics and verification of progamming languages; foundations of actor-based systems; middleware, meta-architectures, and systems; Maude and rewriting logic; and computational biology. The 21 papers presented are organized in topical sections named: Essays on Carolyn Talcott; actors and programming languages; cyberphysical systems; middleware and meta-architectures; formal methods and reasoning tools; and computational biology.

Infobiotics

Author: Vincenzo Manca
Publisher: Springer Science & Business Media
ISBN: 3642362230
Format: PDF, Kindle
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The book presents topics in discrete biomathematics. Mathematics has been widely used in modeling biological phenomena. However, the molecular and discrete nature of basic life processes suggests that their logic follow principles that are intrinsically based on discrete and informational mechanisms. The ultimate reason of polymers, as key element of life, is directly based on the computational power of strings, and the intrinsic necessity of metabolism is related to the mathematical notion of multiset. The switch of the two roots of bioinformatics suggests a change of perspective. In bioinformatics, the biologists ask computer scientists to assist them in processing biological data. Conversely, in infobiotics mathematicians and computer scientists investigate principles and theories yielding new interpretation keys of biological phenomena. Life is too important to be investigated by biologists alone, and though computers are essential to process data from biological laboratories, many fundamental questions about life can be appropriately answered by a perspicacious intervention of mathematicians, computer scientists, and physicists, who will complement the work of chemists, biochemists, biologists, and medical investigators. The volume is organized in seven chapters. The first part is devoted to research topics (Discrete information and life, Strings and genomes, Algorithms and Biorhythms, Life Strategies), the second one to mathematical backgrounds (Numbers and Measures, Languages and Grammars, Combinations and Chances).

Data Mining A Heuristic Approach

Author: Abbass, Hussein A.
Publisher: IGI Global
ISBN: 9781591400110
Format: PDF, Mobi
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Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.