Shmulevich, Dougherty, and Zhang's paper focuses on shifting from Boolean to probabilistic Boolean networks (PBNs) for modeling genetic regulatory networks. The authors argue for Boolean models due to their simplicity and capacity to capture large-scale genetic network dynamics. They also introduce the concept of PBNs to address the deterministic limitations of Boolean networks and better represent the stochastic nature of genetic regulation.
PBNs are explored in the context of quantifying gene influence, analyzing genetic network stability, and developing therapeutic intervention tools. This probabilistic approach enables more accurate modeling of biological systems and paves the way for targeted therapeutic strategies.