doi:10.1016/S0092-8240(03)00053-3Bulletin of Mathematical Biology (2004) 66, 195–197

Book Review

Computational Analysis of Biochemical Systems, by Eberhart O. Voit,November 2000. Paperback (Hardback), 544 pages, $140.00,ISBN: 0521785790.

Computational Analysis of Biochemical Systems is one of the few books currentlyavailable that covers kinetic modeling of biochemical pathways. Now that manycomplete genome sequences are available the focus has turned to experimentalsystems that collect large amounts of data on cellular molecular concentrations,such as those using microarrays. Human intuition is really poor at understandingnonlinear phenomena, and even worse at coping with more than some six simulta-neous variables. It is then becoming clear that to fully understand those data willrequire modeling the dynamics of biochemical networks. That a large number ofmolecular biologists, and funding agencies, believe in this is very welcome newsto the old practitioners in the field. Biochemical systems’ modeling is by no meansa new field, but it has, until now, been somewhat relegated to the fringes of molec-ular biology research. This is, however, changing very rapidly and the field isnow accepted as a necessary component of biology—now referred to as systemsbiology. This sudden recognition has generated a surge in interest by new gradu-ates who are keen to specialize in biochemical modeling and simulation. A newtextbook covering the field, such as this one, is then a needed resource.

This well-written book provides an in-depth introduction to the S-systems frame-work, a derivation from Savageau’s Biochemical Systems Theory. The book isaccompanied by a CD-ROM containing Antonio Ferreira’s PLAS software whichis intended to allow the reader to run the numerous simulations described in thechapters. This is an excellent feature of the book and makes it particularly wellsuited for education. Along the same lines, there is an appendix that covers themajor mathematical concepts used in the book. Although it does not substituteproper mathematical training (especially in differential calculus), it will be use-ful to frame the mathematical concepts in the context of biochemical modeling.Many chapters contain exercises to which there are hints or partial answers at theend of the book. Additionally, the mathematical concepts are carefully explainedwherever they appear in the book.

The first chapter is dedicated to graphical representations of biochemicalsystems. This is a very good choice as graphics are only useful if they are notmisleading. Very often the modeling process starts with diagrams of the networks.The chapter defines very clear rules to construct graphical representations that areunequivocal. Unfortunately, the remainder of the book has graphics that do notalways adhere to the rules defined here. Chapter two introduces mathematicalmodeling of biochemical networks and is mostly dedicated to methods based on

0092-8240/04/010195 + 03 $30.00/0 c© 2003 Society for Mathematical Biology. Published byElsevier Ltd. All rights reserved.

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power-law approximations, in particular the S-system representation. The chaptermentions other approaches such as flux balance analysis and Boolean functions,however other widely used methods are surprisingly absent. This does indeed setthe tone to the rest of the book, where modeling means essentially ‘modeling withpower-laws’. Towards the end of the chapter, one is left with the impression thatcomputational approaches are inferior to mathematical analysis—rather strangefrom a book about computational approaches! Chapter three gives a thoroughexplanation on how to go from a network diagram to a mathematical model basedon power laws. Chapter four is dedicated to computer simulation. The importanttopics are all covered here: steady state analyses, time courses, oscillations, stabi-lity analysis, etc. Even though they are all presented in the S-systems framework,the concepts are general and appear in all methodologies. Chapter five is dedicatedto the problem of estimating parameter values for models and once again manydetails are particular to S-systems, however the chapter is full of general conceptssuch as regression, or issues of in vitro versus in vivo data, which are true for otherapproaches. Chapter six covers steady states with emphasis on analytical solutions,which is a strength of the S-system framework. Chapter seven is about sensitivityanalysis, described from the perspective of Biochemical Systems Theory, whichwas developed decades ago by Michael Savageau. There is also a brief discussionabout MCA, developed by Kacser, Burns, Heinrich and Rapoport, but it is mostlyconcerned with minimizing the importance of MCA’s summation theorems. This isa topic that has been the subject of wide discussions at the end of the 1980s and thebook provides a rather biased view on this. Finally, Chapters 8–11 are dedicated tospecific examples and show application of the concepts introduced earlier. Again,these chapters tend to be partial to previous studies that employed S-systems.

S-systems are a useful approximation to biochemical systems, since they requirejust a few parameters but still are able to represent the nonlinear phenomena obser-ved in biochemical systems. S-systems, like the related framework of metaboliccontrol analysis, are also amenable for theoretical analyses, not just numericalcalculations. However, and despite having been developed back in the 1970s,S-systems have not been as widely used as other frameworks. This book is anexcellent resource for learning how to model biochemical networks with the S-systems approach but is unfortunately very weak about any other approaches, inparticular the most common one: kinetic modeling with mechanistic rate equa-tions. Interestingly, the examples presented in the book required fitting parametersof mechanistic enzyme rate laws to obtain the S-system parameters, thus show-ing that the mechanistic models are needed contrary to what is hinted in the book.As such, a good course on biochemical network modeling cannot be based solelyon the contents of this book. A particular deficiency is the lack of coverage ofthe most popular software packages for biochemical modeling. Apart from PLAS,the book only mentions generic mathematical packages such as Mathematica orMathcad. Where are SCAMP, XPP, or Gepasi (developed by one of us), amongothers? The major deficiency of the book is indeed that it is not a general textbook

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on computational analyses of biochemical systems (as the title may hint at), butrather a great introduction to modeling biochemical networks with S-systems. It is,indeed, very good at that! Many areas of computational biochemical network anal-ysis are still to be covered by textbooks and these are eagerly awaited. In the mean-time, this is one of the best resources available. Those interested should consideralso searching the literature for recent articles as this field is now moving fast.

ALBERTO DE LA FUENTE AND PEDRO MENDES,Virginia Bioinformatics Institute,

Virginia Polytechnic Institute and State University,1880 Pratt Drive,

Blacksburg,VA 24061,

U.S.A.E-mail: mendes@vt.edu