e-book

Cellular automaton modeling of biological pattern formation : characterization, applications, and analysis

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The recent dramatic advances in biotechnology have led to an explosion of data in
the life sciences at the molecular level as well as more detailed observation and characterization
at the cellular and tissue levels. It is now absolutely clear that one needs a
theoretical framework in which to place this data to gain from it as much information
as possible. Mathematical and computational modelling approaches are the obvious
way to do this. Heeding lessons from the physical sciences, one might expect that
all areas in the life sciences would be actively pursuing quantitative methods to consolidate
the vast bodies of data that exist and to integrate rapidly accumulating new
information. Remarkably, with a few notable exceptions, quite the contrary situation
exists. However, things are now beginning to change and there is the sense that we
are at the beginning of an exciting new era of research in which the novel problems
posed by biologists will challenge the mathematicians and computer scientists, who,
in turn, will use their tools to inform the experimentalists, who will verify model
predictions. Only through such a tight interaction among disciplines will we have
the opportunity to solve many of the major problems in the life sciences.

One such problem, central to developmental biology, is the understanding of how
various processes interact to produce spatio-temporal patterns in the embryo. From
an apparently almost homogeneous mass of dividing cells in the very early stages of
development emerges the vast and sometimes spectacular array of patterns and structures
observed in animals. The mechanisms underlying the coordination required for
cells to produce patterns on a spatial scale much larger than a single cell are still
largely a mystery, despite a huge amount of experimental and theoretical research.
There is positional information inherent in oocytes, which must guide patterns, but
cells that are completely dissociated and randomly mixed can recombine to form
periodic spatial structures. This leads to the intriguing possibility that at least some
aspects of spatio-temporal patterning in the embryo arise from the process of selforganization.
Spatial patterns also arise via self-organization in other populations of
individuals, such as the swarming behaviour of bacteria, and in chemical systems, so
that it is a widespread phenomenon.

Modelling in this area takes many forms, depending on the spatio-temporal scale
and detail one wishes (or is able) to capture. At one extreme are coupled systems of
ordinary differential equations, in which one assumes that the system is well stirred
so that all spatial information is lost and all individuals (for example, molecules) are
assumed to have identical states. At the other extreme are cellular automata models,
in which each element may represent an individual (or a collection of individuals)
with assigned characteristics (for example, age) that can vary from one individual
to the next. This approach allows for population behaviour to evolve in response
to individual-level interactions. In hybrid cellular automata, one can model intracellular
phenomena by ordinary differential equations, while global signalling may
be modelled by partial differential equations. In this way, one can begin to address
the crucial issue of modelling at different scales. There are many modelling levels
between these extremes and each one has its own strengths and weaknesses.

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Ketersediaan

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Informasi Detail

Judul Seri

Modeling and simulation in science, engineering & technology

No. Panggil

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Penerbit

Boston : Birkha¨user Boston., 2005

Deskripsi Fisik

QH491.D42 - 571.3–dc22

Bahasa

English

ISBN/ISSN

0-8176-4281-1

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

BIOLOGI

Info Detail Spesifik

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Pernyataan Tanggungjawab

agus

Versi lain/terkait

Lampiran Berkas

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