This NATO Advanced Study Institute centered on large-scale molecular systems: Quantum mechanics, although providing a general framework for the description of matter, is not easily applicable to many concrete systems of interest; classical statistical methods, on the other hand, allow only a partial picture of the behaviour of large systems. The aim of the ASI was to present both aspects of the subject matter and to foster interaction between the scientists working in these important areas of theoretical physics and theoretical chemistry. The quantum-mechanical part was mostly based on the operator-algebraic formulation of quantum mechanics and comprised quantum statistics of infinite systems with special em phasis on macroscopic observables, equilibrium conditions, irreversibility on the one hand, symmetry breaking for molecules in the radiation field and macroscopic quantum phenomena in the theory of superconductivity (BCS-theory) on the other hand. In addition, phase-space methods for many-body systems were also presented. Statistical physics was the main topic in the other lectures of the School; much emphasis was put on the statistical features of macros copic ("large") systems, the lectures dealt with mass and energy transport im polymers, in gels and in microemulsions, with aggregation and growth phenomena, with relaxation in complex, correlated systems, with conduction and optical properties of polymers, and with the means of describing disordered systems, above all fractals and related hierarchical models.

Mathematical models are used to simulate, and sometimes control, the behavior of physical and artificial processes such as the weather and very large-scale integration (VLSI) circuits. The increasing need for accuracy has led to the development of highly complex models. However, in the presence of limited computational accuracy and storage capabilities model reduction (system approximation) is often necessary. Approximation of Large-Scale Dynamical Systems provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications.

The latest developments in quantum and classical molecular dynamics, related techniques, and their applications to several fields of science and engineering. Molecular simulations include a broad range of methodologies such as Monte Carlo, Brownian dynamics, lattice dynamics, and molecular dynamics (MD).Features of this book:• Presents advances in methodologies, introduces quantum methods and lists new techniques for classical MD• Deals with complex systems: biomolecules, aqueous solutions, ice and clathrates, liquid crystals, polymers• Provides chemical reactions, interfaces, catalysis, surface phenomena and solidsAlthough the book is not formally divided into methods and applications, the chapters are arranged starting with those that discuss new algorithms, methods and techniques, followed by several important applications.

Approach your problems from the It isn't that they can't see the end and begin with the answers. solution. It is that they can't Then one day, perhaps you will see the problem. find the final question. G.K. Chesterton. The Scandal of 'The Hermit Clad in Crane Father Brown 'The Point of a Pin'. Feathers' in R. van Gulik's The Chinese Maze Murders. Growing specialization and diversification have brought a host of mono graphs and textbooks on increasingly topics. However, the "tree" of knowledge of mathematics and related fields does not grow only by putting forth new branches. It also happens, quite often in fact, that branches which were thought to be completely disparate are suddenly seen to be related. Further, the kind and level of sophistication of mathematics applied in various sciences has changed drastically in recent years: measure theory is used (non-trivially) in regional and theoretical economics; algebraic geometry interacts with physics; the Minkowsky lemma, coding theory and the structure of water meet one another in packing and covering theory; quantum fields, crystal defects and mathematical pro gramming profit from homotopy theory; Lie algebras are relevant to filtering; and prediction and electric engineering can use Stein spaces. And in addition to this there are such new emerging subdisciplines as "complete integrable systems", "chaos, synergetics and large-scale order", which are almost impossible to fit into the existing classifica tion schemes. The draw upon widely different sections of mathematics.

This book examines detailed experimental and computational approaches for the analysis of many aspects vital to the understanding of membrane protein structure and function. Readers will receive guidance on the selection and use of methods for over-expression and purification, tools to characterize membrane proteins within different phospholipid bilayers, direction on functional studies, and approaches to determine the structures of membrane proteins. Detailed experimental steps for specific membrane proteins with critical notes allow the protocols to be modified to different systems. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of practical information and implementation advice that leads to excellent, reproducible results. Authoritative and up-to-date, Structure and Function Studies of Membrane Proteins serves as an ideal guide for biologists, biochemists, and biophysicists striving to further understand these essential proteins and their many biological roles.

Mathematical and statistical network modeling is an important step toward uncovering the organizational principles and dynamic behavior of biological networks. This chapter focuses on methods to construct discrete dynamic models of gene regulatory networks from experimental data sets, also sometimes referred to as top-down modeling or reverse engineering. Time-discrete dynamical systems models have long been used in biology, particularly in population dynamics. The models mainly focused on here are also assumed to have a finite set of possible states for each variable. That is, the modeling framework discussed in this chapter is that of time-discrete dynamical systems over a finite state set.