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Glioblastoma is a complex disease that is difficult to treat. Network and data science offer alternative approaches to classical bioinformatics pipelines to study gene expression patterns from single-cell datasets, helping to distinguish genes associated with the control of differentiation and aggression. To identify the key molecular regulators of the networks driving glioblastoma/GSC and predict their cell fate dynamics, we applied a host of data theoretic techniques to gene expression patterns from pediatric and adult glioblastoma, and adult glioma-derived stem cells (GSCs). We identified eight transcription factors (OLIG1/2, TAZ, GATA2, FOXG1, SOX6, SATB2, and YY1) and four signaling genes (ATL3, MTSS1, EMP1, and TPT1) as coordinators of cell state transitions and, thus, clinically targetable putative factors differentiating pediatric and adult glioblastomas from adult GSCs. Our study provides strong evidence of complex systems approaches for inferring complex dynamics from reverse-engineering gene networks, bolstering the search for new clinically relevant targets in glioblastoma.