Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level - Biological Networks - page 76

example, existing Boolean approaches may not be successful in systems in which
stochastic effects propagate from the micro- to the macro-level. Probabilistic
Boolean Networks introduce stochasticity in defining the causal relationships.
Though this is useful to infer a network; dynamic stochasticity such as in the
transport of molecules cannot be easily implemented in the current Boolean
models. One can argue that the final outcome of most of the biological systems is
rather deterministic and robust and thus, stochasticity is a rather exotic
phenomenon in regulatory networks (Acar et al. 2008). Nevertheless, it may be
relevant in specific circumstances, for example, cell differentiation. We expect
that exploring the incorporation and effects of stochasticity will play an
increasing role in discrete modeling. Still, in the current state of knowledge on
biological networks the most frequent model failure is also the simplest:
insufficient information of the network architecture. We hope that increasing
communication between modelers and experimentalists will decrease this
deficiency.
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