If you are not already familiar with applying formal methods to biological systems, avoid this book. It is a multidisciplinary collection of research papers, by researchers, for researchers. The papers target specialists in particular formalisms. You could not learn the formalisms from them. The papers were presented at a satellite workshop of the 15th International Symposium on Formal Methods at Turku, in 2008. The goal of the research is to supplement “wetlab” science (in vitro or in vivo) by in silico or in numero studies.
The first paper compares a half-dozen process algebras, as ways of modeling biochemical processes. It covers the &pgr;-calculus, beta-binders, performance evaluation process algebra (PEPA), bio-PEPA, and stochastic concurrent constraint programming (sCCP). The second paper shows that some formal models of membranes are universal. The complexity of the model trades off with the number of membranes needed to achieve universality. The eighth paper presents a different membrane model--the PABM brane calculus. This is a simpler calculus than the original brane calculus by Cardelli, in that it only uses the Bud and Mate processes. The paper notes that one possible model of the activity of the influenza virus in a cell can be expressed using this limited set. The paper notes that the model can be animated/simulated using SPiM--the stochastic &pgr; machine.
The third and fifth papers are about the bio-PEPA process algebra. The third paper focuses on biochemical networks with discretized levels of concentration and events. There are errors in some of the formulas attached to the events. The authors propose using Gillespie’s algorithm to animate the models. Bio-PEPA is also applied, in the fifth paper, to a biochemical pathway, using tools such as Prism and Dizzy. The fourth paper also uses Prism, but with Markov chains to model ribosome kinetics and mRNA chemistry in E.coli. The authors calculate the probabilities of correct versus incorrect insertion and the time taken per codon. I was impressed by this paper because it predicts that the correct decoding occurs much more often than the incorrect one.
The sixth paper discusses Kappa, a rule-based formalism with inheritance. It demonstrates the value of inheritance. The seventh paper describes stochastic Petri nets, with tools such as probabilistic linear-time temporal logic (PLTL), Snoopie, and Systems Biology Markup Language (SBML) for model checking. The ninth paper describes accepting networks of noninserting evolutionary processes (ANNIEP). Each node contains a word and permits only the deletion and substitution of symbols. The nodes also have input and output conditions. The paper proves that these networks can recognize any recursively enumerable (RE) language.
The tenth and eleventh papers seem to be reinventing the wheel. The tenth proposes a scalable algorithm to simulate complex biochemistry based on membrane systems. The paper describes a classic event-based simulation. Even with enhancement, the models do not match equivalent ordinary differential equation (ODE) models. The eleventh paper explores the relation between stochastic programming (sCCP) and ODE. The authors show that stochastic models and ODE models differ near instabilities. The experiments confirm this. The approach reinvents the Monte Carlo methods of the 1950s and 1960s.
The last paper presents an extended and meandering series of analogies between biology (genetic and epigenetic expression), physics (spin glasses, free energy), mathematics (groupoids, information theory), and cognition.
Some of the figures are hard to read in the printed edition because the color is missing. Several papers assume rates follow the mass action law, which may not be accurate. Preprints and similar papers are on the Web. In conclusion, this book is for specialists only.
