Spatiotemporal Modeling of Stem Cell Differentiation: Partial Differentiation Equation Analysis in R covers topics surrounding how stem cells evolve into specialized cells during tissue formation and in diseased tissue regeneration. As the process of stem cell differentiation occurs in space and time, the mathematical modeling of spatiotemporal development is expressed in this book as systems of partial differential equations (PDEs). In addition, the book explores important feature of six PDE model which can represent, for example, the development of tissue in organs. In addition, the book covers the computer-based implementation of example models through routines coded (programmed) in R. The routines described in the book are available from a download link so that example models can be executed without having to first study numerical methods and computer coding. The routines can then be applied to variations and extensions of the stem differentiation models, such as changes in the PDE parameters (constants) and the form of the model equations. - Includes PDE routines based on the method of lines (MOL) for computer-based implementation of PDE models - Offers transportable computer source codes for readers in R, with line-by-line code descriptions for mathematical models and algorithms - Authored by a leading researcher and educator in PDE models

Spatiotemporal Modeling of Stem Cell Differentiation: Partial Differentiation Equation Analysis in R covers topics surrounding how stem cells evolve into specialized cells during tissue formation and in diseased tissue regeneration. As the process of stem cell differentiation occurs in space and time, the mathematical modeling of spatiotemporal development is expressed in this book as systems of partial differential equations (PDEs). In addition, the book explores important feature of six PDE model which can represent, for example, the development of tissue in organs. In addition, the book covers the computer-based implementation of example models through routines coded (programmed) in R. The routines described in the book are available from a download link so that example models can be executed without having to first study numerical methods and computer coding. The routines can then be applied to variations and extensions of the stem differentiation models, such as changes in the PDE parameters (constants) and the form of the model equations. Includes PDE routines based on the method of lines (MOL) for computer-based implementation of PDE models Offers transportable computer source codes for readers in R, with line-by-line code descriptions for mathematical models and algorithms Authored by a leading researcher and educator in PDE models

This 1999 edition of The Neural Crest contains comprehensive information about the neural crest, a structure unique to the vertebrate embryo, which has only a transient existence in early embryonic life. The ontogeny of the neural crest embodies the most important issues in developmental biology, as the neural crest is considered to have played a crucial role in evolution of the vertebrate phylum. Data that analyse neural crest ontogeny in murine and zebrafish embryos have been included in this revision. This revised edition also takes advantage of recent advances in our understanding of markers of neural crest cell subpopulations, and a full chapter is now devoted to cell lineage analysis. The major research breakthrough since the first edition has been the introduction of molecular biology to neural crest research, enabling an elucidation of many molecular mechanisms of neural crest development. This book is essential reading for students and researchers in developmental biology, cell biology, and neuroscience.

This volume details methods and protocols to further the study of stem cells within the computational stem cell biology (CSCB) field. Chapters are divided into four sections covering the theory and practice of modeling of stem cell behavior, analyzing single cell genome-scale measurements, reconstructing gene regulatory networks, and metabolomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Stem Cell Biology: Methods and Protocols will be an invaluable guide to researchers as they explore stem cells from the perspective of computational biology.

New discoveries in the field of stem cells increasingly dominate the news and scientific literature revealing an avalanche of new knowledge and research tools that are producing therapies for cancer, heart disease, diabetes, and a wide variety of other diseases that afflict humanity. The Handbook of Stem Cells integrates this exciting area of life science, combining in two volumes the requisites for a general understanding of adult and embryonic stem cells. Organized in two volumes entitled Pluripotent Stem Cells and Cell Biology and Adult and Fetal Stem Cells, this work contains contributions from the world's experts in stem cell research to provide a description of the tools, methods, and experimental protocols needed to study and characterize stem cells and progenitor populations as well as a the latest information of what is known about each specific organ system. - Provides comprehensive coverage on this highly topical subject - Contains contributions by the foremost authorities and premiere names in the field of stem cell research - Companion website - http://booksite.elsevier.com/9780123859426/ - contains over 250 color figures in presentation format

This practical introduction to stochastic reaction-diffusion modelling is based on courses taught at the University of Oxford. The authors discuss the essence of mathematical methods which appear (under different names) in a number of interdisciplinary scientific fields bridging mathematics and computations with biology and chemistry. The book can be used both for self-study and as a supporting text for advanced undergraduate or beginning graduate-level courses in applied mathematics. New mathematical approaches are explained using simple examples of biological models, which range in size from simulations of small biomolecules to groups of animals. The book starts with stochastic modelling of chemical reactions, introducing stochastic simulation algorithms and mathematical methods for analysis of stochastic models. Different stochastic spatio-temporal models are then studied, including models of diffusion and stochastic reaction-diffusion modelling. The methods covered include molecular dynamics, Brownian dynamics, velocity jump processes and compartment-based (lattice-based) models.

Since the first successful isolation and cultivation of human embryonic stem cells at the University of Wisconsin, Madison in 1998, there has been high levels of both interest and controversy in this area of research. This book provides a concise overview of an exciting field, covering the characteristics of both human embryonic stem cells and pluripotent stem cells from other human cell lineages. The following chapters describe state-of-the-art differentiation and characterization of specific ectoderm, mesoderm and endoderm-derived lineages from human embryonic stem cells, emphasizing how these can be used to study human developmental mechanisms. A further chapter discusses genetic manipulation of human ES cells. The concluding section covers therapeutic applications of human ES cells, as well as addressing the ethical and legal issues that this research have raised.