Computer Science Books

This book is an introduction to numerical computing using Java providing "non-theoretical explanations of practical numerical algorithms." While this sounds like heady stuff, freshman level calculus should be sufficient to get the most out of this text.

The first three chapters are amazingly useful, and worth the price of admission alone. Mak does a fine job explaining in simple terms the pitfalls of even routine integer and floating-point calculations, and how to mitigate these problems. Along the way the reader learns the details of how Java represents numbers and why good math goes bad. The remainder of the book covers iterative computations, matrix operations, and several "fun" topics, including fractals and random number generation.

The author conveys his excitement for the subject in an easy-to-read, easy-to-understand manner. Examples in Java clearly demonstrate the topics covered. Some may not like that the complete source is in-line with the text, but this is subjective. Overall, I found this book educational, interesting, and quite enjoyable to read.

The interesting information sprinkled throughout the book, including the boxes and figures, help keep the reader stimulated and yearning for greater knowledge of this exciting field. The color graphics also complement the book nicely. Although the subject covered in the book is extremely broad, the author managed to convey the perspectives of multiple scientific disciplines (e.g., biology, chemistry, computer science, math) very well. The combination of breadth and depth in a readable style is remarkable.

Overall, I highly recommend this book to readers interested in the area.

Dr. Schlick is an expert in this field and her group has published tons of molecular modeling research papers. Her expertise also makes this book valuable for computational scientific researchers. I highly recommend it.

This book's focus is generally on interactions with large molecules, DNA and proteins, although it does discuss small molecules (drugs, a few dozen to a few hundred atoms) too. That means that it skips most of the quantum mechanical modeling of more advanced computational chemistry texts.

Nothing is lost, because Schlick covers her chosen topic (molecular modeling and dynamics) in such detail. She starts with a very clear discussion of the structure of large biomolecules, with emphasis on the features that need quantitative description for modeling. That covers protein structure at ever level. It also covers DNA/RNA structure in the best detail I've ever seen. The double-helix is the just the starting point. There are alternative helix forms, non-standard binding between nucleotides, and asymmetries caused by nucleotide composition. The next chapters describe the geometric model and, briefly, the forces acting between atoms.

The second half of the book gets down to the nuts and bolts of modeling. This includes numerical techniques, minimization, sampling and Monte Carlo techniques, and the start of dynamics. Schlick attacks some of the nasty points of the calculations, such as modeling of forces that act on very different time scales. As with the simpler material, the development is clear, descriptive, and free of pointless theorems. The meticulous reader should come away able to implement most or all of the techniques described. The level of presentation is consistent and approachable. I think freshman physics should be enough preparation for most students to get most of the value out of the discussion.

The book is written with clarity as a top priority. The glossary is in the front, making sure that the reader knows it's a first-class part of the text. After that, every chapter starts with a list of the mathematical symbols and variables used and a one-line description of each. These are small things, but they increase the book's readability immensely. The illustrations are generally informative enough. On the whole, though, they don't seem quite up to the level of the textual and mathematical presentations.

I needed a crash course in the mathematical techniques used for describing molecular structure and behavior. I should have read this book first - its clarity and thoroughness would have saved me a lot of time. After this one, I can now go back and reread the more complex texts with more hops of understanding. Do yourself a favor and read this one first.

This upper-level undergraduate/lower-level graduate course was centered on mathematical and computational models of the three dimensional structure of DNA, and DNA topology. We found Professor T. Schlick's book very useful in our class preparation. In particular we covered chapter 5 (DNA structure) completely, sections 3 and 4 from chapter 7 (basic principles and formulation of atomic interactions in molecular mechanics), and several sections or subsections from chapters 8 and 9 (force terms used in molecular dynamics simulations). We also covered most of the material in chapter 10 (Multivariate Minimization), and gave a brief introduction to chapter 11 (Monte-Carlo techniques) and chapter 12 (Molecular Dynamics algorithms).

Chapter 5 starts with a very amenable and brief introduction that relates DNA with other biological processes and describes some of the challenges in studying DNA structure. It continues describing the basic building blocks of DNA. The author wisely spends some time defining the nomenclature for each of the atoms, angles and bonds that form these basic blocks. The following sections teach the reader what parameters are relevant for describing a DNA double helix and how they characterize the A, B and Z- forms of DNA. Illustrations in this chapter are particularly helpful.

Although our course's approach to DNA supercoiling was different that the one in the book I found particularly useful some illustrations in chapter 6 and movies (to be found in her webpage) that Prof. Schlick's group has developed over the years. In brief, chapter 6 is a study of more complex structures and behavior of DNA (such as structural role of the DNA sequence, DNA-protein interactions, and higher order organization of DNA -i.e. DNA supercoiling and histone-DNA interactions). This chapter can be a good source for short research projects (e.g. final projects).

Chapters 7, 8 and 9 describe the basic concepts in molecular mechanics. From sections 7.3 and 7.4 I found of interest how the author addresses the problem of the system size (i.e. number of interacting molecules) and some of the details that the author gives for modeling the geometry of atomic interactions. At the end of the chapter (section 7.4.3) interested readers can find some of the limitations of current approaches. Chapters 8 and 9 describe in depth the force fields and how to implement them. Chapter 9 also illustrates with clarity how to implement periodic boundary conditions and the advantages of using different lattice models.

Chapter 10 describes a number of familiar methods for energy minimization (i.e. steepest descent, conjugate gradient, etc....). We used sections 10.1 to 10.4 and section 10.5.2 (conjugate gradient). I found the Hessian patterns shown in figures 10.4 and 10.5 and the minimization trajectories shown in 10.10 very pedagogical. As in previous chapters the author finishes with practical recommendations and future challenges.

We left chapter 11 (Monte Carlo methods) for last in the course and discussed chapter 12 (molecular dynamics) first. As in previous chapters the author gives a very nice introduction (section 12.1 and 12.2) and covers the basics on simulation protocols in sections 12.3 and 12.4. Section 12.4 describes the basic integration algorithms such as leap-frog, verlet, etc... Figure 12.3 was revealing for the students as it compares the time scales in biological systems.

Chapter 11 (Monte-Carlo methods) provides a very comprehensive introduction to Monte-Carlo methods. We found particularly useful some of the subsections of random number generation and the treatment of Importance sampling and Markov chains in section 11.5.

As mentioned earlier we were particularly delighted with the amount of details given in each topic. For example chapters 7 and 8 provide all the formalism needed for the problems of molecular mechanics. In section 8.4 (bond angle potential) the author highlights the differences (both formally and by figures-see figure 8.4) between different formulations of the problem (see also figure 8.6). In Chapter 10 the author describes minimization algorithms in detail and shows some of the patterns that one observes in the Hessian associated to minimization functions of biological structures (see figs. 10.4, 10.5 and 10.11). She also makes very detailed comparisons between the different minimization methods (see figs 10. 2, 10.10). In chapter 12 she compares the different methods and initial conditions for the algorithms discussed (figs 12.3, 12.4, 12.6).

Overall we found that Prof. T. Schlick's book is very adequate for a broad spectrum of levels and very accessible to both graduate and undergraduate students interested in mathematical modeling and computational biology. It is also very well organized facilitating the option of selecting parts of the material for the classroom or for use in one's research.

Finally we may play with the "building blocks of matter" we've been hearing so much about. Here is an instruction manual, detailing the Elements, and their Interactions, while at the same time suggesting possible Design Models for construcion.

Curious about the subject?
Start with Drexler's Engines of Creation, instead. Maybe some other collections of theoretical applications to whet your appetite. Come back to this when you begin to see a bigger picture.

Know some, want to know more?
Definately read. But be warned, it is quite techincal when it is not being necessarily vague. This is a halmark. The basis of this book was Drexler's thesis for his doctorate in Molecular Nanotechnology, the first awarded (MIT 1991, I believe).

Serious about the topic?
You already have access to a copy...or should.

You might very well be able to download significant portions from Foresight's website (it's an org.anization, not a com.mercial); but I would suggest supporting them with at least the price of the book. They seem to be committed to developing this Potential responsibly.

Your book is an excellent guide. Thank you for inviting me to the field of nanotechnology.

Sincerely,

Kenneth L. Buckingham, Founder Tiny Technology, Inc.

OPM provides a new framework for specifying design intents and capturing the complexity of hardware and software interaction. Through OPL, it is possible to translate the process into a machine executable code. In addition, OPM can capture the dynamic behavior of the hardware attributes and software states in a single integrated graphical and textual language that is understandable by domain experts who have no programming experience. These traits of OPM ease the development effort for evaluating the system reliability during the design stages. Simulation and testing protocols can be automatically generated though future extensions of OPM to reduce lengthy system verification efforts.
The main benefit of OPM is its ability to identify system objects, processes, and the relationships among them in a structured way. The resulting OPD set becomes an excellent framework for identifying how to implement structural and procedural improvements. The resulting OPL script provides a well-defined set of existing and future specifications for the system. The ability to freely switch from text to graphics and back is of great value to understanding the system as a whole with a single graphic and textual model, without the need to consult various models and carry out mental transformation among these various models.
Based on my personal experience, the following points highlight the benefits OPM can bring to the particular projects described in this paper.
1. OPM is an excellent way to represent daily activities, products, processes and other complex things
2. OPM has allowed representing the complete system with its various aspects in a single model. The model specifies the systems function, structure and behavior aspects without sacrificing clarity.
3. OPM can be used as a common language to exchange design among members of a team.
4. Since OPM design is visual and textual at the same time, it is easy to explain the design.
5. OPL is very easy to generate from OPD
6. OPM will be a good tool for documenting the existing processes and as ISO documentation.

UML uses complex rules to model complex systems, something that is very difficult to make happen, therefore it is very difficult to learn and use. OPM uses simple rules and consistant notations to model complex systems. After simple introductions to the methology, we have been able to start using it in our organization. More powerful and far simpler then UML. The way UML should have been done long time ago.

And he never says you can't understand. He just offers another way to see his life.