Ensemble threshold Boolean modeling reveals robust attractors and regulatory drivers in pediatric leukemia.
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Pediatric B-cell acute lymphoblastic leukemia (B-ALL) is characterized by substantial phenotypic heterogeneity, reflecting the complex structure of its underlying gene regulatory network (GRN). To investigate the dynamical principles governing these phenotypes, we model a curated GRN as a threshold Boolean network (TBN) and analyze its behavior across a large ensemble of weighted network realizations sharing the same topology. For each realization, we compute all asymptotic states and project them onto leukemia-relevant key genes to obtain a reduced attractor representation. The ensemble reveals a small set of highly recurrent, topology-enforced attractors, indicating that the GRN admits a limited number of robust leukemia-associated states. Among all weight configurations, we identify a single representative TBN that reproduces over 97% of these structural attractors. Applying Multiple Correspondence Analysis (MCA) to the attractors of this representative network, we uncover three well-separated attractor clusters and identify two additional regulators, BCL6 and IRF4, as major contributors to the organization of the attractor landscape. These findings provide a mathematically grounded characterization of robust leukemia attractors in pediatric B-ALL and highlight regulatory drivers that may guide future mechanistic and therapeutic investigations.