Book Reviews

Book: Genetics and Molecular Biology 2nd Ed. by Robert Schleif (JHU)

StevenWang — Wed, 13 Jan 2010 19:10:00 GMT

With the generosity of Prof. Robert Schleif (Biology department, John Hopkins University), there is another book of him that can be accessed for free. The book "Genetics and Molecular Biology 2nd Ed" can be download from his website: http://gene.bio.jhu.edu/pub.html

Book: Analysis of Protein Structure and Function: A Beginner's Guide to CHARMM by Robert Schleif (JHU)

StevenWang — Wed, 13 Jan 2010 19:04:00 GMT

Prof. Robert Schleif (Biology department, John Hopkins University) has made his book "Analysis of Protein Structure and Function: A Beginner's Guide to CHARMM" available for free download: http://gene.bio.jhu.edu/pub.html

He also provided CHARMM input scripts free for download: http://gene.bio.jhu.edu/scriptindex.html

Steven

Molecular Driving Forces: statistical thermodynamics for chemistry and biology

Deepthi — Wed, 23 Dec 2009 20:25:00 GMT

I used this book for my course on statistical mechanics. It is a very good introduction to statistical mechanics. The use of lattice model in explaining various topics made it easy for us to understand the underlying concepts. It starts with a simple introduction to probability, equilibrium, math tools such as series and approximations, and then goes on to Boltzmann distribution law, entropy, free energies, statistical mechanics, phase transitions, the details of how water is different from other substances, polymer solutions etc. I liked the some of the chapters discussed in the book like the entropy, free energy of mixing, phase transitions and the topics on water because of the way they were interpreted in detail. I was fascinated by the anamolous properties of water due to its hydrogen bonding network explained using statistical thermodynamics. The chapters were well divided into microscopic and macroscopic properties of various substances.This book is a very good book for beginners tol earn about statistical mechanics. I had previous knowledge about many of the concepts discussed but still I liked the way the author explained the different concepts using various models. But I thought that the exercise problems towards the end of each chapter were a bit confusing due to the lack of sufficient information in some of the problems. Other than that the book was really good.

Comments on "Molecular driving forces: statistical thermodynamics in chemistry and biology" by Ken A. Dill and Sarina Bromberg.

Linh — Fri, 18 Dec 2009 17:21:00 GMT

"Molecular Driving Forces: Statistical Thermodynamics in Chemistry and Biology" is a moderately good book. It really depends on your purposes and expectations whether you should choose this book or not.

This book covers an impressive amount of material on statistical thermodynamics in both chemistry and biochemistry areas. The material that Ken and Sarina try to present in this book is a little overwhelming for a one semester course. They have gone over most of the classical concepts that need to develop the statistical mechanics understanding vary from easy to difficult level. On the top of that, they apply the statistical thermodynamics onto biological systems such as proteins and nucleic acids, which is tremendously helpful for people who are interested in studying the physical properties of biological systems. However, the book is too spread out since it tries to combine two large areas into one. I would think if they split one good book into two better books, one is focusing on the statistical thermodynamics concepts and theories itself and one is focusing on the applications particularly in biological systems, they can discuss both subjects at a more thoroughly detailed level. Another thing that bother me just as much is the book completely ignores the quantum mechanics and quantum statistics, not even mention them in introductions or reviews sections. The idea of quantum statistics might be frightening to me as well as many students, but in order for students to have a better appreciation, a better understanding of a bigger picture, I suppose they should dedicate a chapter to discuss about quantum mechanics and quantum statistic. The last thing that I dislike about this book is the problems set at the end of each chapter. The problems are ranging from easy "plug in numbers and get answers" problems to extreme difficult derivations problems. I didn't find many intermediate levels problems. A lots of the problems are just testing whether one can interpret a plot or figures. Some of the questions are almost helpless in trying to inspire you develop your critical thinking skills.

On the bright side, I have found the book of Ken and Sarina to be a very good book for self-study. It has some very well done reviews on mathematical tools that help you develop mathematical skill in studying statistical mechanics. Chapter 1 is called "principles of probability", which goes over the fundamental of probability concepts and thus lead to the foundation of entropy. Chapter 4 is another math review on series and approximations. Follow by chapter 5, which is a review on multivariate calculus. Chapter 17 is a vector calculus reviews and Gauss's theorem. These chapters makes the books qualify to be a very suitable self-study book. I am very grateful for these so called reviews; a lots of them were new to the students. The chapter about Boltzmann Distribution Law (chapter 10) was done very nicely. The detailed, well put and very organized explanations and derivations about Boltzmann Distribution Law and Partition Function was impressively presented. This chapter is one of many of the chapters in this book where statistical thermodynamics concepts are explained and derived slowly and clearly and easily to understand. Overall, I like the book. It is a moderately good book like I said in the beginning. It has the potential to be a great book. I was just slightly upset because there was so much material was mentioned in the book and that make it hard for learners to keep up; it was a little crowded and overwhelming.

Book Review: K. Dill and S. Bromberg, "Molecular driving forces: statistical thermodynamics in chemistry and biology", Garland Science Publisher, 2003

StevenWang — Fri, 18 Dec 2009 01:13:00 GMT

Molecular driving forces: statistical thermodynamics in chemistry and biology

by Ken A. Dill and Sarina Bromberg. Garland Science (Taylor & Francis Group), New York, 2003. 686 pp. $109

The book "Molecular driving forces: statistical thermodynamics in chemistry and biology" by Ken Dill and Sarina Bromberg, published by the Garland Science publisher in 2003, is the most comprehensive and useful book I have ever read on the the topic of thermodynamics.This book starts from an elementary introduction of probability calculation, and gradually extends to the free energy and entropy concepts, based on which a lot of phenomena are clearly explained (both microscopically and macroscopically), like surface tension, diffusion, catalysis, phase transition, electrostatics, cooperativity, etc. A special topic on polymer science is also discussed (Dr. Ken. A. Dill used to be a postdoc of Dr. Paul J. Flory (1, 2)), which is not common for a textbook on this topic.There are several merits that make it a very good textbook. First, the prerequisite requirement is low. All mathematical tools are included in the book chapters or appendixes. Secondly, the authors have a very clear thought on explaining the topic. Each chapter is based on the text in the previous chapters and all of them are well connected. The reader will feel no gap between them. Thirdly, the authors use a very friendly style in discussing the topics. Also, the authors are frank about the knowledge in discussion. No obscure expression (linguistically or mathematically) is used. Everything is expressed in a direct and easy to understand manner. But there are also some flaws of this book. Besides some typos (not intended by the authors), some exercise questions are not well stated. Sometimes the reader has to guess what the authors really want to say. Also, the book does not cover the non-equilibrium thermodynamics, therefore some recent advances in statistical thermodynamics are not discussed. Although with these little flaws, this is really a good book to study if one is interested in learning thermodynamics.

References:

1.The homepage of Ken. A. Dill's group (http://www.dillgroup.ucsf.edu/)

2. Dill and Flory. Molecular organization in micelles and vesicles. Proc. Natl. Acad. Sci. U S A. 1981, 78:676–680.

Steven Wang

e-mail: stevenaura [at] ou.edu

December 17, 2009

Free energy reconstruction from nonequilibrium single-molecule pulling experiments

kxia — Mon, 20 Apr 2009 20:07:00 GMT

Recently, the development of single molecule manipulation has led new sight into mechanical properties of individual molecules. However, the measurement in experiment drive systém away from equilibrium. How can one extract equilibrium properties? According to the second thermodynamics law, the mechanical work is always larger than the free energy, except that the experiment is performed reversibly or infinitely slowly. However, recently Jarzynski discovered the identity between thermodynamic free energy differences and the irreversible work. Thus one should be able to extract free energy surfaces from the atomic force measurements in repeated pulling single molecule experiments.

In the single molecular force measuring experiment, the sample is moved with a constant speed relative to the cantilever with spring constant k. The position of the cantilever with respect to the sample is recorded. After repeating this measurement a certain times, the free energy profile of the equilibrium systém can be obtained exactly according to Jarzynski's equality. This reconstruction result fits well with the reconstruction curve. Through measuring the pulling force, one can also estimate the free energy profile through integrating. However, this introduces some artifacts and should not be considered accurate.

In another experiment, the free energy profile from Jarzynski's equality is compared with that from fluctuation-dissipation relation. The results show that in reversible process both of them fits well with theoretical predictions for near-equilibrium conditions. In irreversible process, the free energy profile from Jarzynski's equality performs consistently over the entire range, while the free energy profile fails at large extension range. The effect of repeating times is also tested. The results shows that the more times the process repeats, the better the free energy profile recovers.

In conclusion, Jarzynski raised an equality relating the irreversible work to the equilibrium free energy difference. And this is approved by the recently developed single molecule manipulation experiments.

Reference: Gerhard Hummer and Attila Szabo, PNAS, 98, 7, 2001

Jan Liphardt etc, Science, 296, 1832, 2002

Modeling Materials Properties Without Experimental Input

kxia — Mon, 20 Apr 2009 20:01:00 GMT

Empirical physical models rely on parameters from experiments, and so induce various inaccuracies. However, a quantum mechanical model can offer independent data. Also, QM is able to predict everything theoretically. But QM has properties not easily addressed, which depends on complexities at larger length scales over heterogeneities. And, there is no universal QM method appropriate for all materials and phenomena.

Ab initio post-Hartree-Fock quantum chemistry and quantum Monte Carlo simulation are the most accurate. They make no other physical approximations and can get accurate ground and excited state properties. However, the main drawback of such methods is computational expense. Therefore, more common methods for QM modeling uses or builds upon density functional theory(DFT). It is much simpler and less costly. DFT is a formally exact ground state theory in which the material's energy is expressed as a fun of the electron density alone. The primary disadvantage of current DFT methods is the approximate XC functional. However, for most ground state properties, the generalized gradient approximation(GGA) XC functionals provide sufficient accuracy. Hybrid XC DFT-GGA techniques are developed, such as DFT+U, include some exact HF exchange, and are suitable for description of mid-to-late first row transition metal oxides and sulfides, but not appropriate for metals. TD-DFT can be used to calculate electronic or optical spectra of materials and GW method can be used to obtain ionization energies and electron affinities. BSE takes DFT and GW data as input and accounts for electron -hole interactions. With these methods in mind, we can use the appropriate method to predict a given property for a given materials class as a function of accuracy and expense.

Amorphous structures are difficult to model with QM because the usual 3D periodic boundary conditions introduce correlation length artifacts and it is certain that a random amorphous structure generated. Heterogeneous mixtures offer the most severe challenge for future materials modeling. Multiscale modeling aims to bridge length and time scales to make overarching predictions of materials behavior. Major unsolved issues in this area include how to transfer heat and mass across all scales and etc.

Now the typical simulation still starts with guidance from experiment regarding approximate initial structure and composition, but given such guidance, QM can provide sight to how properties change when composition and structure change, thereby furthering atomic-scale manipulation of material design.

Reference: Emily A. Carter, Science, 321, 800, 2008

Recent Advances in the Generalized Born Method

Jason — Mon, 20 Apr 2009 14:54:00 GMT

Due to the high computational costs of explicit solvent simulations, the development of accurate implicit solvent models is an attractive approach to reach longer timescales in biomolecular simulations. Possibly the most crucial aspect an implicit solvent model is the calculation of the electrostatic solvation energy. To completely describe the electrostatic solvation energy the response of the solvent to the solute and vice versa should be considered, but in empirical force-field calculations the solute charge response to the solvent is not considered. The benchmark of continuum electrostatic calculations is the Poisson-Boltzmann (PB) equation, where a finite difference approach is used to solve for the potential. This method, although accurate, is computationally demanding and does not lend itself to energy minimization or dynamics because the forces cannot be calculated analytically. Recent development of the generalized born approach, a pairwise-approximation to the PB method, has been shown to reproduce remarkably well the PB electrostatic solvation energy if one uses the same definition for the molecular volume. In 2002 Lee et. al. developed new grid-based and analytical born models that empirically correct for the non-spherical nature of the molecular volume. These models were shown to have excellent agreement with PB calculations. In 2003 an extension to the previous work of Lee et. al. refined their empirical correction for the molecular volume, and included a vector based scaling approach to correct error in the standard overlapping atomic functions approach. They also developed an accurate solvent-accessible surface area approximation to account for the nonpolar contribution to the solvation energy that is based on the same computational machinery as their GB model. This recent method by Lee et. al. has been shown to be one of the most accurate GB models to date. Lee, Salsbury and Brooks, J. Chem. Phys. 116: 10606-10614 (2002) Lee, Feig, Salsbury and Brooks, J. Comput. Chem 24:1348-1356 (2003)

Nelson, P., Biological Physics (2004)

Jason — Tue, 09 Dec 2008 03:12:00 GMT

Nelson's book covers the basics of biological physics. The book reviews fundamental topics such as thermodynamics and makes the connection to biological processes. The book is a good survey of biological physics and discusses recent research areas such as single molecule experiments. The book gives many examples and thorough explanation. It is appropriate for anyone who is beginning to learn about biological physics.

Pathria R.K., Statistical Mechanics 2nd Ed. (1996)

Jason — Tue, 09 Dec 2008 02:49:00 GMT

Pathria's book covers all the essential material related to equilibrium statistical mechanics. The material is covered in depth but, much of the learning must be done by working the numerous problems found at the end of each chapter. This book is great for anyone who wants to learn the tools of statistical mechanics. The book is concise but gives adequate explanation.