Boolean network simulations for life scientists

At the most general level systems biology approaches consist of two steps. The first is building a model of the biological system of interest, a representation that incorporates existing knowledge and experimental observations. This model then can be subjected to various conditions and may be allowed to evolve in time, a step typically referred to as simulation. These simulations then can be used to generate qualitative or quantitative predictions on the overall behavior of the system.Mathematical models of biological systems range from continuous to discrete (based on the representation of the status of the system's components) and from deterministic to stochastic (based on their incorporation of randomness and noise) [1-4]. The simplest models assume that each element of the system has a binary (Boolean) state, and are therefore discrete, deterministic and parameter-free. However, several extensions of the Boolean modeling formalism also allow for iterative parameterization and the incorporation of continuous and stochastic elements [5-7]. The most important barrier precluding a more general use of Boolean models consists of the difficulties of the computational implementation of a given model. This implementation needs to perform in a consistent, correct and extensible manner to allow for the integration of all empirical knowledge as well as the unfettered exploration of the dynamical behaviors allowed by the model. There are very few tools that focus on qualitative modeling, for example the Genetic Network Analyzer [8] supports qualitative predictions via a piece-wise linear model and provides advanced visualization capabilities. There are several software packages that focus on quantitative modeling via differential equations [9-11], and discrete modeling [7,12] but these tools are less suited for exploratory analyses of biological systems in which the majority of kinetic parameters are unknown.In this paper we present a software toolbox (from now on referred t