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This book highlights the theory and practical applications of the
chemical master equation (CME) approach for very large biochemical
networks, which provides a powerful general framework for model
building in a variety of biological networks. The aim of the book
is to not only highlight advanced numerical solution methods for
the CME, but also reveal their potential by means of practical
examples. The case studies presented are mainly from biology;
however, the applications from novel methods are discussed
comprehensively, underlining the interdisciplinary approach in
simulation and the potential of the chemical master equation
approach for modelling bionetworks. The book is a valuable guide
for researchers, graduate students, and professionals alike.
Alzheimer's disease (AD) is the leading cause of dementia and,
unfortunately, remains incurable. The social, emotional and
financial implications of AD are immeasurable, and about 47 million
people worldwide are affected by AD or other forms of dementia. As
lifespans are improved by healthcare systems worldwide,
age-associated neurodegenerative diseases are imposing an
increasing challenge to science. It is becoming imperative for us
to understand the causes of these diseases, AD in particular, at
molecular and cellular levels. Starting with the broader picture
from a biological perspective, this book takes the reader through
fascinating dynamics within and outside of neurons in the
brain.Alzheimer's Disease: Biology, Biophysics and Computational
Models helps the reader to understand AD from mechanistic and
biochemical perspectives at intra- and inter-cellular levels. It
focuses on biochemical pathways and modeling associated with AD.
Some of the recent research on biophysics and computational models
related to AD are explained using context-driven computational and
mathematical modeling and essential biology is discussed to
understand the modeling research.
This book demonstrates the power of mathematical thinking in
understanding the biological complexity that exists within the
brain. It looks at the latest research on modelling of biochemical
pathways within synapses, and provides a clear background for the
study of mathematical models related to systems biology. Discussion
then focusses on developments in computational models based on
networks linked to synaptic plasticity. The models are used to
understand memory formation and impairment and they provide a
mathematical basis for memory research.Computational Systems
Biology of Synaptic Plasticity is a valuable source of knowledge to
postgraduate students and researchers in computational systems
biology, and as a reference book for various techniques that are
needed in modelling biological processes.
Most of the natural and biological phenomena such as solute
transport in porous media exhibit variability which can not be
modeled by using deterministic approaches. There is evidence in
natural phenomena to suggest that some of the observations can not
be explained by using the models which give deterministic
solutions. Stochastic processes have a rich repository of objects
which can be used to express the randomness inherent in the system
and the evolution of the system over time. The attractiveness of
the stochastic differential equations (SDE) and stochastic partial
differential equations (SPDE) come from the fact that we can
integrate the variability of the system along with the scientific
knowledge pertaining to the system. One of the aims of this book is
to explaim some useufl concepts in stochastic dynamics so that the
scientists and engineers with a background in undergraduate
differential calculus could appreciate the applicability and
appropriateness of these developments in mathematics. The ideas are
explained in an intuitive manner wherever possible with out
compromising rigor.
The solute transport problem in porous media saturated with water
had been used as a natural setting to discuss the approaches based
on stochastic dynamics. The work is also motivated by the need to
have more sophisticated mathematical and computational frameworks
to model the variability one encounters in natural and industrial
systems. This book presents the ideas, models and computational
solutions pertaining to a single problem: stochastic flow of
contaminant transport in the saturated porous media such as that we
find in underground aquifers. In attempting to solve this problem
using stochastic concepts, different ideas and new concepts have
been explored, and mathematical and computational frameworks have
been developed in the process. Some of these concepts, arguments
and mathematical and computational constructs are discussed in an
intuititve manner in this book.
This book highlights the theory and practical applications of the
chemical master equation (CME) approach for very large biochemical
networks, which provides a powerful general framework for model
building in a variety of biological networks. The aim of the book
is to not only highlight advanced numerical solution methods for
the CME, but also reveal their potential by means of practical
examples. The case studies presented are mainly from biology;
however, the applications from novel methods are discussed
comprehensively, underlining the interdisciplinary approach in
simulation and the potential of the chemical master equation
approach for modelling bionetworks. The book is a valuable guide
for researchers, graduate students, and professionals alike.
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