Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation

Bahar Tercan; Boris Aguilar; Sui Huang; Edward R Dougherty; Ilya Shmulevich

doi:10.1016/j.isci.2022.104951

Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation

iScience. 2022 Aug 17;25(9):104951. doi: 10.1016/j.isci.2022.104951. eCollection 2022 Sep 16.

Authors

Bahar Tercan¹, Boris Aguilar¹, Sui Huang¹, Edward R Dougherty², Ilya Shmulevich¹

Affiliations

¹ Institute for Systems Biology, Seattle, WA, USA.
² Texas A&M University Department of Electrical & Computer Engineering, College Station, TX, USA.

Abstract

We developed a computational approach to find the best intervention to achieve transcription factor (TF) mediated transdifferentiation. We construct probabilistic Boolean networks (PBNs) from single-cell RNA sequencing data of two different cell states to model hematopoietic transcription factors cross-talk. This was achieved by a "sampled network" approach, which enabled us to construct large networks. The interventions to induce transdifferentiation consisted of permanently activating or deactivating each of the TFs and determining the probability mass transfer of steady-state probabilities from the departure to the destination cell type or state. Our findings support the common assumption that TFs that are differentially expressed between the two cell types are the best intervention points to achieve transdifferentiation. TFs whose interventions are found to transdifferentiate progenitor B cells into monocytes include EBF1 down-regulation, CEBPB up-regulation, TCF3 down-regulation, and STAT3 up-regulation.

Keywords: Biological sciences; cell engineering.