BoNesis + CABEAN
Chevalier S, Becker J, Gui Y, Noël V, Su C, Jung S, Calzone L, Zinovyev A, Del Sol A, Pang J, Sinkkonen L, Sauter T, Paulevé L
2025 · NPJ systems biology and applications
Boolean networks provide robust, explainable, and predictive models of cellular dynamics, especially for cellular differentiation and fate decision processes.
Abstract
From the original paper, NPJ systems biology and applications · PubMed
Boolean networks provide robust, explainable, and predictive models of cellular dynamics, especially for cellular differentiation and fate decision processes. Yet, the construction of such models is extremely challenging, as it requires integrating prior knowledge with experimental observation of the transcriptome, potentially relating thousands of genes. We present a general methodology for integrating transcriptome data and prior knowledge on the underlying gene regulatory network in order to generate automatically ensembles of Boolean networks able to reproduce the modeled qualitative behavior. Our methodology builds on the software BoNesis, which implements the automatic construction of Boolean networks from a specification of their expected structural and dynamical properties. We show how to transform transcriptome data into such a qualitative specification, and then how to exploit the generated ensembles of Boolean networks for identifying families of candidate models, and for predicting robust cellular reprogramming targets. We illustrate the scalability and versatility of our overall approach with two applications: the modeling of hematopoiesis from single-cell RNA-Seq data, and modeling the differentiation of bone marrow stromal cells into adipocytes and osteoblasts from bulk RNA-seq time series data. For this latter case, we took advantage of ensemble modeling to predict combinations of reprogramming factors for trans-differentiation that are robust to model uncertainties due to variations in experimental replicates and choice of binarization method. Moreover, we performed an in silico assessment of the fidelity and efficiency of the reprogramming and conducted preliminary experimental validation.
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 BoNesis + CABEAN relates to the cross-modality inverse-design framework of the review.
Why this level
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.