Metraipolis › archive › dc_b23a952fd91c

Co$^2$ODE: Conditionally Coarse-Grained Model Discovery for Rewiring in Biochemical Networks — arXiv:2610.41634

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41634
License (as archived): CC0-1.0
Archived: 2026-11-02T04:53:24.661Z
SHA-256 of the archived text: fe807d1d66ee3fa1eef954b9b21d71cd4d586e99cfee5a1b44ab9538d22cd1ae
Stable URL of this page: https://metraipolis.com/documento/dc_b23a952fd91c

How to cite

Co$^2$ODE: Conditionally Coarse-Grained Model Discovery for Rewiring in Biochemical Networks — arXiv:2610.41634 — arXiv (en), https://arxiv.org/abs/2610.41634 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b23a952fd91c (Metraipolis archive dc_b23a952fd91c, sha256 fe807d1d66ee3fa1eef954b9b21d71cd4d586e99cfee5a1b44ab9538d22cd1ae)

BibTeX

@misc{dcb23a952fd91c,
  title = {Co$^2$ODE: Conditionally Coarse-Grained Model Discovery for Rewiring in Biochemical Networks — arXiv:2610.41634},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41634},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b23a952fd91c; sha256 fe807d1d66ee3fa1eef954b9b21d71cd4d586e99cfee5a1b44ab9538d22cd1ae},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41634. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Biochemical networks propagate the signals that regulate cellular behaviour, and which routes carry them depends on cellular context: a shift in context rewires the network's effective dynamics. Mechanistic simulators are misspecified here because the effective dynamics emerge from an interaction structure that is only partially known. Model discovery instead learns the dynamics from data, but sparse, correlated measurements poorly identify a full network model. What matters is narrower: the portion the context rewires. We introduce conditionally coarse-grained ODEs (Co$^2$ODE), which model that portion as a sparse neural or symbolic ODE: a conditional mask selects the markers carrying the rewiring, and a variational autoencoder compresses the remaining markers into a learnt closure term. On single-cell phosphoproteomic data spanning 30 cancer-relevant overexpressions, Co$^2$ODE compresses a 30-marker panel into a 10-dimensional state while matching the predictive accuracy of a neural ODE fit to the full panel. The closure absorbs what the mask drops, hence the accuracy is insensitive to which markers are kept. Adding a prior score to the loss shifts the selected markers at no cost in fit, indicating the data are not interventional enough to identify the underlying biology.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Quantitative Methods (q-bio.QM) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.41634 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T04:45:12.960Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).