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Modeling and Control of Complex Systems
Comprehension of complex systems comes from an understanding of not only the behavior of constituent elements but how they act together to form the behavior of the whole. However, given the multidisciplinary nature of complex systems, the scattering of information across different areas creates a chaotic situation for those trying to understand pos
Introduction to Genomic Signal Processing with Control
Studying large sets of genes and their collective function requires tools that can easily handle huge amounts of information. Recent research indicates that engineering approaches for prediction, signal processing, and control are well suited for studying multivariate interactions. A tutorial guide to the current engineering research in genomics, Introduction to Genomic Signal Processing with Control provides a state-of-the-art account of the use of control theory to obtain intervention strategies for gene regulatory networks. The book builds up the necessary molecular biology background with a basic review of organic chemistry and an introduction of DNA, RNA, and proteins, followed by a des...
Epistemology of the Cell
"Honorable mention – Biomedicine and Neuroscience, 2011 Prose Awards" An examination of how the cell should be described in order to effectively process biological data "The fruitful pursuit of biological knowledge requires one to take Einstein's admonition [on science without epistemology] as a practical demand for scientific research, to recognize Waddington's characterization of the subject matter of biology, and to embrace Wiener's conception of the form of biological knowledge in response to its subject matter. It is from this vantage point that we consider the epistemology of the cell." —from the Preface In the era of high biological data throughput, biomedical engineers need a mor...
Genomic Signal Processing
Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous mathematical definitions and propositions for the main elements of GSP and by paying attention to the validi...
Introduction to Tissue Engineering
A comprehensive reference and teaching aid on tissue engineering—covering everything from the basics of regenerative medicine to more advanced and forward thinking topics such as the artificial liver, bladder, and trachea Regenerative medicine/tissue engineering is the process of replacing or regenerating human cells, tissues, or organs to restore or establish normal function. It is an incredibly progressive field of medicine that may, in the near future, help with the shortage of life-saving organs available through donation for transplantation. Introduction to Tissue Engineering: Applications and Challenges makes tissue engineering more accessible to undergraduate and graduate students a...
Computational and Statistical Approaches to Genomics
Computational and Statistical Genomics aims to help researchers deal with current genomic challenges. Topics covered include: overviews of the role of supercomputers in genomics research, the existing challenges and directions in image processing for microarray technology, and web-based tools for microarray data analysis; approaches to the global modeling and analysis of gene regulatory networks and transcriptional control, using methods, theories, and tools from signal processing, machine learning, information theory, and control theory; state-of-the-art tools in Boolean function theory, time-frequency analysis, pattern recognition, and unsupervised learning, applied to cancer classification, identification of biologically active sites, and visualization of gene expression data; crucial issues associated with statistical analysis of microarray data, statistics and stochastic analysis of gene expression levels in a single cell, statistically sound design of microarray studies and experiments; and biological and medical implications of genomics research.
Models and Algorithms for Biomolecules and Molecular Networks
By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms. Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms Sampling techniques for estimating evolutionary rates and generating molecular structures Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations End-of-chapter exercises
Biomedical Signal Analysis
Biomedical Signal Analysis Comprehensive resource covering recent developments, applications of current interest, and advanced techniques for biomedical signal analysis Biomedical Signal Analysis provides extensive insight into digital signal processing techniques for filtering, identification, characterization, classification, and analysis of biomedical signals with the aim of computer-aided diagnosis, taking a unique approach by presenting case studies encountered in the authors’ research work. Each chapter begins with the statement of a biomedical signal problem, followed by a selection of real-life case studies and illustrations with the associated signals. Signal processing, modeling,...
Emerging Research in the Analysis and Modeling of Gene Regulatory Networks
While technological advancements have been critical in allowing researchers to obtain more and better quality data about cellular processes and signals, the design and practical application of computational models of genomic regulation continues to be a challenge. Emerging Research in the Analysis and Modeling of Gene Regulatory Networks presents a compilation of recent and emerging research topics addressing the design and use of technology in the study and simulation of genomic regulation. Exploring both theoretical and practical topics, this publication is an essential reference source for students, professionals, and researchers working in the fields of genomics, molecular biology, bioinformatics, and drug development.
Functional Genomics
Functional genomics: approaches and methodologies (F. J. Livesey, S . P. Hunt). Construction and screening of a subtractive cDNA library (A. N. Akopian, J. N. Wood). Differential display analysis of alteration in gene expression (Y.-J. Cho, P. Liang). Representational difference analysis of cDNA (M. Humbank, D. G. Schatz). Identification of differentially expressed genes by suppression subtractive hybridization (S. Desai, J. Hill, S. Trelogan, L. Diatchenko, P. D. Siebert). Gene expression analysis by cDNA microarrays (S. Mousses, M. L. Bittner, Y. Chen, R. Dougherty, A. Baxevanis, P. S. Meltzer, J. M. Trent). SADE: a microassay for serial analysis of gene expression (L. Cheval, B. Virlon, J.-M. Elalouf). Analysis of gene expression in single cells using three prime end amplification PCR (T. C. Freeman, A. K. Dixon, K. Lee, P. J. Richardson). Analysis of gene expression by two-dimensional gel electrophoresis (A. M. Tolkovsky). Proteome research: methods for protein characterization (I. Humphery-Smith, M. A. Ward). List of suppliers. Index.
