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Simulation algorithms for computational systems biology
Marchetti L., Priami C., Thanh V., Springer International Publishing, New York, NY, 2017. 238 pp. Type: Book (978-3-319631-11-0)
Date Reviewed: Mar 30 2018

Simulation lies at the heart of systems biology and is arguably one of the most useful computational tools for understanding, modifying, and even designing complex biological systems. Simulation techniques have already shown themselves useful in physics and chemistry, but biology, and particularly molecular biology, provides some interesting challenges, such as the widely disparate scales in which different processes occur and the difficulty in obtaining data about the dynamics of individual cells. One of the most fundamental concepts is that of stochastic simulation, which makes tractable the effects over time of chemical species that occur in very small quantities but that have large effects, such as genes. Practical approaches to stochastic simulation are based primarily on Gillespie’s seminal work [1].

The mathematics justifying the legitimacy of simulation models and results is complex, and it is unlikely that many biologists, even computational biologists, and more so experimentalists, would find it necessary to understand all the details. Several tools exist (such as COPASI [2]) that make it possible for users to interact with the simulation engines as black boxes that provide believable results. However, there are many different variations in how the basic idea of the stochastic simulation algorithm (SSA) can be implemented, and subtle differences in the interpretation of the processes can have a huge effect on the outcome. Genetic networks can be quite brittle, and small perturbations (such as even a mutation in one nucleotide) can have a significant effect. By blindly using third-party simulators, users may lack the background to question the accuracy of the model. What is needed is an accessible, detailed, and accurate description of stochastic simulation techniques that can help researchers from a variety of diverse backgrounds compare and evaluate the different variations available, and thus bolster their judgment and trust. This is exactly what is provided by this well-written volume.

The book starts with two very short but informative chapters introducing and motivating the use of stochastic simulation to elucidate the workings of biochemical networks. Chapter 3, which occupies roughly one-third of the book, is the heart of the book because it explains many of the choices made in implementing the basic SSA. The main variation is in choosing which reaction to perform next and how much time should be advanced by the simulation clock after each state change. The choice is broadly and invariably based on Monte Carlo sampling, but there are many ways in which the approach can be interpreted. The authors of this book, all experienced in the use of simulation techniques, explain these variations in clear and simple text first and then in mathematical notation.

Chapter 3 considers exact simulations, whereas chapter 4 covers approximate algorithms, which can sacrifice accuracy for speed. This class of algorithms includes the well-known tau-leaping method: several reactions that could have occurred during a given time period are fired at once. The larger the leaping time, the more efficient, but the less accurate. There are many techniques to choose the leap time, and often this period is recalculated during the simulation in order to keep accuracy within bounds.

Two further topics are covered: deterministic simulations (which, confusingly, are tacked at the end of chapter 4 instead of having their own chapter) and, in chapter 5, hybrid approaches.

The book is very clearly written and presented, with enough informal explanations to counter any difficulty in following the detailed mathematical formulation. The explanations are enhanced by reference to well-known benchmark models and extensive displays of graphed results of their simulations. Algorithms are clearly displayed and easy to follow. The authors must have aimed for readability and accessibility and in my opinion have achieved these aims.

I work in the very multidisciplinary area of synthetic biology and am very often in the position of having to introduce both colleagues and new postgraduate students, from both computer science as well as life sciences backgrounds, to the concept of stochastic simulation. I will not hesitate to recommend that they read this book, both as an introductory explanation as well as later on when they are deep in a modeling exercise and need to understand the many subtle yet important variations of stochastic simulation techniques applicable to biological systems.

Reviewer:  Sara Kalvala Review #: CR145941 (1806-0290)
1) Gillespie, D. Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry 81, 25(1977), 2340–2361.
2) COPASI: Biochemical System Simulator, http://copasi.org (02/26/2018).
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