Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT
Abstract
Predictive logical models have made substantial contributions to biomedical research by facilitating the identification of regulatory mechanisms driving biological processes. Yet, their construction remains a major bottleneck, as it typically relies on extensive prior knowledge at the genome scale. The emergence of high-throughput omics technologies has opened new opportunities for data-driven logical model construction by providing high-resolution observations of biological states. In parallel, formal constraint-based approaches have emerged, reformulating executable model inference as a constraint-satisfaction problem. However, transforming omics observations into biologically meaningful Boolean state abstractions and dynamical constraints suitable for logical synthesis engines remains challenging, particularly for poorly understood biological systems and multi-condition experimental settings. To address these challenges, we introduce scBOLT, a semi-automated framework for Boolean constraint engineering and logical model inference from multi-condition single-cell transcriptomes. Beyond these methodological contributions, scBOLT substantially improves computational tractability, enabling the inference of Boolean networks comprising several hundred components within practical runtimes.
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BibTeX
@misc{roncalli2026scalable,
title = {{Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT}},
author = {T. Roncalli and E. Remy and L. Pauleve},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.02.756175},
url = {https://www.biorxiv.org/content/10.64898/2026.10.02.756175v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT
AU - T. Roncalli
AU - E. Remy
AU - L. Pauleve
PY - 2026
DA - 2026/10/02
PB - bioRxiv
DO - 10.64898/2026.10.02.756175
UR - https://www.biorxiv.org/content/10.64898/2026.10.02.756175v1
AB - Predictive logical models have made substantial contributions to biomedical research by facilitating the identification of regulatory mechanisms driving biological processes. Yet, their construction remains a major bottleneck, as it typically relies on extensive prior knowledge at the genome scale. The emergence of high-throughput omics technologies has opened new opportunities for data-driven logical model construction by providing high-resolution observations of biological states. In parallel, formal constraint-based approaches have emerged, reformulating executable model inference as a constraint-satisfaction problem. However, transforming omics observations into biologically meaningful Boolean state abstractions and dynamical constraints suitable for logical synthesis engines remains challenging, particularly for poorly understood biological systems and multi-condition experimental settings. To address these challenges, we introduce scBOLT, a semi-automated framework for Boolean constraint engineering and logical model inference from multi-condition single-cell transcriptomes. Beyond these methodological contributions, scBOLT substantially improves computational tractability, enabling the inference of Boolean networks comprising several hundred components within practical runtimes.
ER -