Steady State Analysis of Boolean Models: A Dimension Reduction Approach

Boolean models have been used to study biological systems where it is of interest to understand the qualitative behavior of the system or when the precise regulatory mechanisms are unknown. A feature of especial interest of Boolean models are the states where the system is invariant over time, because they correspond to stable patterns of the biological system. Thus, finding steady states or fixed points is an important problem in computational systems biology.Although Boolean networks provide a strong mathematical framework, the analysis by simulation is difficult for models of large size. Thus, it is necessary to develop tools to analyze large Boolean models other than by exhaustive simulation.Here we present an approach based on dimension reduction that allows us to study large Boolean models by systematically removing nodes without changing the number of steady states.