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PreprintCC BY-NCbioRxiv

Mechanistic gene-regulation-based trajectory inference with bowside

  • J. T. Schleicher
  • M. Zago
  • M. Claassen1
  1. 1University Hospital Tübingen
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Abstract

While single-cell RNA sequencing enables the in-depth study of differentiation processes, simultaneous trajectory inference and gene regulatory network reconstruction remain challenging. We propose bowside, a shallow neural stochastic differentiation model that mechanistically models gene regulation. Our method successfully recovers gene expression trends and the underlying regulatory interactions. We demonstrate bowside on hematopoiesis and induced pluripotent stem cell reprogramming data, retrieving known fate-determining network motifs.

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Cite

BibTeXRIS
BibTeX
@misc{schleicher2026mechanistic,
  title = {{Mechanistic gene-regulation-based trajectory inference with bowside}},
  author = {J. T. Schleicher and M. Zago and M. Claassen},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.26.761800},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.26.761800v1},
  note = {CC BY-NC},
}
RIS
TY  - UNPB
TI  - Mechanistic gene-regulation-based trajectory inference with bowside
AU  - J. T. Schleicher
AU  - M. Zago
AU  - M. Claassen
PY  - 2026
DA  - 2026/10/26
PB  - bioRxiv
DO  - 10.64898/2026.10.26.761800
UR  - https://www.biorxiv.org/content/10.64898/2026.10.26.761800v1
AB  - While single-cell RNA sequencing enables the in-depth study of differentiation processes, simultaneous trajectory inference and gene regulatory network reconstruction remain challenging. We propose bowside, a shallow neural stochastic differentiation model that mechanistically models gene regulation. Our method successfully recovers gene expression trends and the underlying regulatory interactions. We demonstrate bowside on hematopoiesis and induced pluripotent stem cell reprogramming data, retrieving known fate-determining network motifs.
ER  -