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Book reviews
Molecular Driving Forces:
Statistical Thermodynamics in
Chemistry and Biology
Ken Dill and Sarina Bromberg
Garland Science, Taylor and
Francis Group, New York;
ISBN 0 8153 2051 5; 642 pp.;
£35.95 (paperback); 2002
This is a beautifully written and engaging
book that is destined to become a classic.
In addition to offering compliments on its
wonderful literary and graphical style, two
adjectives come to mind to describe its
contents: ‘complete’ and ‘modern’.
‘Complete’ refers not just to the range
of topics considered, but also to the
cogent summaries of necessary
mathematics that precede discussion of a
topic. For example, it would not be
unusual to find a text on statistical
thermodynamics starting with Chapter 1
on Principles of Probability, and this text
is no exception. However, in later
chapters the reader is reminded of (or
introduced to) principles of multivariate
calculus and exact differentials (Chapter 5)
preparatory to Maxwell’s relations
(Chapter 6), and vector calculus (Chapter
17) before discussion of electrostatic
effects (Chapter 20). Appendices include
‘Useful Taylor Expansions’ and ‘Useful
Integrals’. This attention to necessary
mathematical background makes the book
self-contained and accessible to advanced
undergraduates, in addition to graduate
students, and yet the book is still of
reasonable length.
The range of topics is also complete,
with Chapters 1–10 laying the theoretical
foundations for concepts of heat, work,
free energy, Maxwell’s relations, the
Boltzmann probability distribution, etc,
which are then used in subsequent
chapters in various applications. A
particularly nice feature is the Suggested
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Reading and References section ending
each chapter, which not only point the
reader to other texts for additional
material, but also lead directly into the
modern research literature. For example,
Chapter 18 on ‘Physical Kinetics’ not
only derives the classic Einstein–
Smoluchowski equation and the classic
fluctuation–dissipation relation, but also
discusses Brownian ratchets including
pointers into the current literature. In
Chapter 22 on ‘Electrochemical
Equilibria’ the Nernst equation is of
course covered, but also discussed is
application to voltage gated ion channels,
complete with beautiful illustrations and
reference to a recent 1999 Science paper.
Further examples of the completeness,
and modern flavour of the book is in
Chapter 25, ‘Phase Transitions’. Yes, the
classic Ising model and analysis of the
helix–coil transition is covered, but so
too is a phase diagram for surfactant
molecules in which micelles and liquid
crystal phases are introduced. The authors
do not just stop there, ie with covering
classic material such as the helix–coil
transition, and then bringing in ‘research
level’ topics. In this chapter, as in other
chapters, are also applications of the
chapter’s concepts to everyday
phenomena. Want to know how
supercriticality relates to decaffeinating
coffee? See the box in Chapter 25.
Delightful!
One apparent omission seems slightly
odd in view of one of the author’s (Ken
Dill) contributions to the field, and that is
a more complete discussion of the
concepts underlying protein folding (for
example, one does not find ‘Levinthal
paradox’ in the index). Perhaps protein
folding was considered to be too
advanced for an introductory text, but
given the success the authors display with
other topics, it was slightly surprising not
& HENRY STEWART PUBLICATIONS 1467-5463. B R I E F I N G S I N B I O I N F O R M A T I C S . VOL 4. NO 4. 382–384. DECEMBER 2003
Book reviews
to see a whole chapter discussing one of
the more important instantiations of
‘Molecular Driving Forces’ (the title of
the book). Nevertheless, the elements are
all beautifully covered, including a
chapter (Chapter 30) simply titled ‘Water’
and then ‘Water as Solvent’ (Chapter 31)
in which the interaction of water with
polar and non-polar molecules is
discussed. Finally the last few chapters of
the book are on polymers.
In summary, this book is one of those
rare introductory texts where the author
is able to so clearly convey his/her
physical intuition that not just students
should read it, but researchers in related
fields would also find it of value. Highly
recommended!
Alan Lapedes
Microarrays for an Integrative
Genomics
Isaac S. Kohane, Alvin Kho and Atul
J Butte
Computational Molecular Biology
Series; MIT Press, Cambridge,
MA; ISBN 0 262 11271 X;
326 pp.; $42.00/£27.95; 2002
‘Microarrays for an Integrative Genomics’
begins in the foreword with the
following: ‘The impact of microarray
measurements on biology and
bioinformatics has been astounding.’
Nothing could be closer to the truth. This
book represents a noble effort to teach
scientists – biologists and engineers alike
– the how, why and when of genomiclevel gene expression experiments. This
book should be required reading for any
(future and present) post-genomic
researcher, because it gives new
perspective to functional genomics.
The book is intelligently organised into
seven chapters. The opening is an
introduction that includes motivational
comments and an optional section on
basic biology. The tone is set well by
describing the intended audience
(basically everybody with an interest in
microarrays and functional genomics), and
then carefully defining functional
genomics and the traditional functional
genomics pipeline. One of the most
interesting claims is that this is a book on
so-called integrative genomics, not a book
focusing solely on microarrays. The
authors suggest that as proteomics
methods become appropriately
‘engineered and cost effective’, they too
will become part of the discussion within
future versions of this text. This reviewer
looks forward to that.
The next two chapters focus on
experimental design and actually
performing microarray experiments. Both
spotted arrays and oligonucleotide arrays
are discussed in detail. It is very clear,
almost immediately, that this book has its
interests in data analysis. It is not going to
tell us how to build the DeRisi/Brown
spotted array robot. No, after a short
(approximately 20 page) overview of
microarrays, the authors quickly go back
to telling us how to design experiments
intelligently for getting the most bang
from your data. Sections include
instructions on the philosophy behind
designing a functional genomic
experiment, gene clustering and a
significant section on replicate
experiments and noise. Finally, the two
chapters conclude with a nuts and bolts
analysis on processing data.
The following two chapters focus on
data, particularly in the areas of
knowledge representation and data
mining. Many of the required goodies are
here, including a significant discussion on
supervised learning (classification)
methods and unsupervised learning
(clustering) methods for data mining.
After a detailed discussion of clustering
applications, including K-means and
hierarchical methods, there is an
important section on determining
statistical significance. Finally, the chapter
concludes with a section on genetic
networks. This reviewer expects to see
this grow into its own chapter in future
editions, as technologies for network
& HENRY STEWART PUBLICATIONS 1467-5463. B R I E F I N G S I N B I O I N F O R M A T I C S . VOL 4. NO 4. 382–384. DECEMBER 2003
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