## Computational Biology: A Statistical Mechanics PerspectiveQuantitative 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 without sacrificing mathematical rigor, Computational Biology: A Statistical Mechanics Perspective is the perfect tool to broaden your skills in computational biology. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Other editions - View all

### Common terms and phrases

Acad algorithm alignment approach assume average base pairs behaviour binding biological biomolecules calculation cell chain Chapter chemical chromatin compute conﬁguration consider correlation critical exponent deﬁned deﬁnition denaturation diﬀerent diﬀerential equation diﬀusion discussion distribution DNA molecule dynamics eﬀects electrostatic ensemble entropy equilibrium example exponent expression ﬁeld Figure ﬁnd ﬁnite ﬁrst ﬁxed ﬂuctuations Fokker-Planck equation folding free energy Gaussian gene genomic Ginzburg-Landau theory given graph hence histone hybridization interactions Ising model kinetics length Lett linear loop master equation matrix mean-ﬁeld melting modiﬁcations molecular Natl noise nonequilibrium nucleosome obtain parameter particle partition function phase transition Phys Poisson-Boltzmann equation potential probability Proc proﬁles protein pseudoknots random recursion repressor Reprinted with permission result RNA structure scaling Schiessel secondary structure sequence solution spatial speciﬁc statistical mechanics statistical physics stochastic strands temperature term theory thermal thermodynamic transcription factor variable

### Popular passages

Page 245 - U. (2003). Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 100, 1 1980-5.