{
  "abstract": "Cell fate plasticity dynamics in glioma ecosystems is usually inferred from static single-cell snapshots, leaving a gap between manifold geometry, predictive modelling, and mechanistic intervention ranking. We developed scCTM, a complexity theory and machine learning framework that combines the Sakana AI Continuous Thought Machine (CTM) with autoencoding, a continuous actor-critic policy over synchronization-derived latent states for predicting single-cell fate dynamics. We also incorporated integrated-gradient and decoder-weight explainability, network science, empirical Waddington-like landscape (attractor) reconstruction, and post-hoc Block Decomposition Method (BDM) perturbation analysis. The complexity framework was evaluated in two malignant cell secondary analyses: GSE210567 stem-like versus differentiated H3K27M cells (18,490 cells) and longitudinal H3K27M initial versus recurrent disease trajectory (20,000 cells) from Sussman et al. (2026) as a ML binary classification problem. CTM-AE achieved AUROC values of $0.99$ in both datasets. CTM-RL preserved classification almost exactly but did not improve reconstruction. Out-of-fold CTM-RL latent states produced potential basins across PCA, UMAP, and PHATE embeddings. BDM edge perturbation analysis further identified recurrent high-impact network interactions, including epigenetic/chromatin-associated signatures involving ARID1B and developmental or neuronal-state genes such as AUTS2, CTNND2, DPP6, NRXN1, and CADM2. These in silico findings support CTM synchronization as an interpretable latent-state representation and predictive forecasting of cell fate dynamics, as well as BDM perturbation signatures as a complementary in silico intervention ranking layer for systems medicine.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.23927",
  "author_total": 1,
  "authors": [
    {
      "name": "Abicumaran Uthamacumaran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fkm5e7jmathj9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.23927",
  "fulltext_url": null,
  "id": "fkm5e7jmathj9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.23927",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.OT"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7223002504",
  "pdf_url": "https://arxiv.org/pdf/2610.23927",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fkm5e7jmathj9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:31:01Z",
      "native_id": "2610.23927",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.23927"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "scCTM: Continuous Thought Machine Reconstruction of Glioma Cell Fate Dynamics Attractor Landscapes and Algorithmic Perturbation Signatures",
  "title_text": "scCTM: Continuous Thought Machine Reconstruction of Glioma Cell Fate Dynamics Attractor Landscapes and Algorithmic Perturbation Signatures",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:50Z",
  "url": "https://research.draconitic.com/paper/fkm5e7jmathj9",
  "venue": "arXiv",
  "versions": []
}