Statistical methods to evaluate discrete Boolean mathematical models from systems biology experimental data sets

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Discrete Boolean models offer qualitative insights into gene regulatory networks, enhancing understanding of cellular phenotypes. Despite lacking quantitative parameters, their reliability stems from data derived through systematic reviews and databases supported by experimental validation. Unlike other mathematical models, they don't employ statistical tests for intrinsic validity, making it challenging to establish their utility in interpreting experimental data. In this paper, we propose two statistical procedures to validate different description levels obtained from Boolean models, addressing simulated mutations and stationary states. These methods provide guidelines for integrating data from massive sequencing techniques, such as RNA-seq.