Traditionally, statistical mechanics-based treatment of biological systems has focused on the molecular level, with larger systems being ignored. This book fills that gap, addressing recent developments in the field, in particular, the multi-scale nature of biological problems, ranging from the genetic to the multi-cellular, tissue and organ level.

Cells are complex objects, representing a multitude of structures and processes. In order to understand the organization, interaction and hierarchy of these structures and processes, a quantitative understanding is absolutely critical. Traditionally, statistical mechanics-based treatment of biological systems has focused on the molecular level, with larger systems being ignored. This book integrates understanding from the molecular to the cellular and multi-cellular level in a quantitative framework that will benefit a wide audience engaged in biological, biochemical, biophysical and clinical research. It will build new bridges of quantitative understanding that link fundamental physical principles governing cellular structure and function with implications in clinical and biomedical contexts.

This book explores Probabilistic Cellular Automata (PCA) from the perspectives of statistical mechanics, probability theory, computational biology and computer science. PCA are extensions of the well-known Cellular Automata models of complex systems, characterized by random updating rules. Thanks to their probabilistic component, PCA offer flexible computing tools for complex numerical constructions, and realistic simulation tools for phenomena driven by interactions among a large number of neighboring structures. PCA are currently being used in various fields, ranging from pure probability to the social sciences and including a wealth of scientific and technological applications. This situation has produced a highly diversified pool of theoreticians, developers and practitioners whose interaction is highly desirable but can be hampered by differences in jargon and focus. This book – just as the workshop on which it is based – is an attempt to overcome these difference and foster interest among newcomers and interaction between practitioners from different fields. It is not intended as a treatise, but rather as a gentle introduction to the role and relevance of PCA technology, illustrated with a number of applications in probability, statistical mechanics, computer science, the natural sciences and dynamical systems. As such, it will be of interest to students and non-specialists looking to enter the field and to explore its challenges and open issues.

This book provides a self-contained introduction to cellular automata and lattice Boltzmann techniques. Beginning with a chapter introducing the basic concepts of this developing field, a second chapter describes methods used in cellular automata modeling. Following chapters discuss the statistical mechanics of lattice gases, diffusion phenomena, reaction-diffusion processes and non-equilibrium phase transitions. A final chapter looks at other models and applications, such as wave propagation and multiparticle fluids. With a pedagogic approach, the volume focuses on the use of cellular automata in the framework of equilibrium and non-equilibrium statistical physics. It also emphasises application-oriented problems such as fluid dynamics and pattern formation. The book contains many examples and problems. A glossary and a detailed bibliography are also included. This will be a valuable book for graduate students and researchers working in statistical physics, solid state physics, chemical physics and computer science.

Self-contained, pedagogic introduction to powerful techniques for graduate students and researchers in physics and computer science.

Current biological research demands the extensive use of sophisticated mathematical methods and computer-aided analysis of experiments and data. This highly interdisciplinary volume focuses on structural, dynamical and functional aspects of cellular systems and presents corresponding experiments and mathematical models. The book may serve as an introduction for biologists, mathematicians and physicists to key questions in cellular systems which can be studied with mathematical models. Recent model approaches are presented with applications in cellular metabolism, intra- and intercellular signaling, cellular mechanics, network dynamics and pattern formation. In addition, applied issues such as tumor cell growth, dynamics of the immune system and biotechnology are included.

Most of the interesting and difficult problems in statistical mechanics arise when the constituent particles of the system interact with each other with pair or multipartiele energies. The types of behaviour which occur in systems because of these interactions are referred to as cooperative phenomena giving rise in many cases to phase transitions. This book and its companion volume (Lavis and Bell 1999, referred to in the text simply as Volume 1) are princi pally concerned with phase transitions in lattice systems. Due mainly to the insights gained from scaling theory and renormalization group methods, this subject has developed very rapidly over the last thirty years. ' In our choice of topics we have tried to present a good range of fundamental theory and of applications, some of which reflect our own interests. A broad division of material can be made between exact results and ap proximation methods. We have found it appropriate to inelude some of our discussion of exact results in this volume and some in Volume 1. Apart from this much of the discussion in Volume 1 is concerned with mean-field theory. Although this is known not to give reliable results elose to a critical region, it often provides a good qualitative picture for phase diagrams as a whole. For complicated systems some kind of mean-field method is often the only tractable method available. In this volume our main concern is with scaling theory, algebraic methods and the renormalization group.