Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component(gene, molecule, protein etc.) is represented by a variable. The variables take binary values-1/0 or true/false- that express, for instance, activation/deactivation or high/low concentrations. However, these models suffer from the state space explosion, i.e. there are exponentially many states with the number of BN variables. This presentation refers to dimensionality reduction of BNs with a technique called Backward Equivalence. We will explain the Boolean Network, its utility on the modeling of a real biological system (T-Cell), and how reduction can facilitate the analysis of large models. Finally, we will discuss some relevant and ongoing work on another reduction technique called Forward Equivalence.
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