Computational Biology from a Statistical Mechanics Perspective

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*Provides the first detailed application of statistical physics concepts to problems in computational biology *Illustrates the concepts with simple applications focused exclusively on biological systems *Focuses on the properties of biomolecules, their stability, and the structures that they form *Includes a detailed discussion of chromatin and its role in gene regulation, a topic unique among statistical mechanics books *Uses statistical mechanics models to describe the dynamics and structure of complex gene and protein networks Quantitative methods have a particular knack for improving any field they touch. For biology, computational techniques have led to enormous strides in our understanding of biological systems, but there is still vast territory to cover. Statistical physics especially holds great potential for elucidating the structural-functional relationships in biomolecules, as well as their static and dynamic properties. Breaking New Ground Computational Biology: A Statistical Mechanics Perspective is the first book dedicated to the interface between statistical physics and bioinformatics. Introducing both equilibrium and nonequilibrium statistical mechanics in a manner tailored to computational biologists, the author applies these methods to understand and model the properties of various biomolecules and biological networks at the systems level. Unique Vision, Novel Approach Blossey combines his enthusiasm for uniting the fields of physics and computational biology with his considerable experience, knowledge, and gift for teaching. He uses numerous examples and tasks to illustrate and test understanding of the concepts, and he supplies a detailed keyword list for easy navigation and comprehension. His approach takes full advantage of the latest tools in statistical physics and computer science to build a strong set of tools for confronting new challenges in computational biology. Making the concepts crystal clear withou

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Product Details

Table of Contents

Equilibrium Statistical Mechanics
Z: The Partition Function
Relation to Thermodynamics
Computing Z
Nonequilibrium Statistical Mechanics
Stochastic Processes
The Master Equation
Fluctuation Theorems
The Fokker-Planck and Langevin Equations
Sequence Alignment: A Nonequilibrium Phase Transition
Molecules, Code and Representation
DNA and RNA: The Building Blocks
Representing RNA Structure
Thermal Stability of DNA: The Melting Transition
The Melting Profiles of Genomic DNA and cDNA
Hybridizing DNA
Computing RNA Secondary Structure: Combinatorics
The RNA Partition Function
RNA Phase Behavior and Folding Kinetics
Proteins: Folding
Proteins: Docking
Bacterial Chemotaxis: Cooperativity Once More
Network Dynamics I: Deterministic
Deterministic Dynamics: ?-Repressor Expression
The Turing Insight
The Min System
Network Dynamics II: Fluctuations
Noise in Signaling
Stochastic Cascades
Stochastic Focusing
Fluctuating Gene Network Dynamics
Extrinsic vs. Intrinsic Noise
Networks: Structure
Networks as Graphs
Probability Generating Functions and Network Characteristics
Statistical Mechanics of Networks
Network Growth

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