Class C3 records a modeled outcome or a certified control property. The claim reaches only as far as the model, its reachable set, and the outcome it predicts. Boundary conditions.
Tercan / PBN
Tercan B, Aguilar B, Huang S, Dougherty ER, Shmulevich I
2022 · iScience
We developed a computational approach to find the best intervention to achieve transcription factor (TF) mediated transdifferentiation.
Abstract
From the original paper, iScience · PubMed
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.
Summary
Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how Tercan / PBN relates to the inverse-design framework of the review.
Why this class
Level 3 because candidate interventions enter an explicit forward operator and the predicted post-intervention outcome is what scores each candidate. Representation family is executable intervention model. Cited in §3.4 of the review. Editorial rationale pending review by the maintainer.