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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning.

Chengming Zhang1, Zexi Chen2, Yuanxiang Miao3

  • 1International Research Center For Neurointelligence, The University of Tokyo Institutes For Advanced Study, The University of Tokyo, Tokyo, Japan.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 16, 2026
PubMed
Summary

CauFinder identifies key gene regulators driving cell state changes using causal modeling. This framework prioritizes master regulators for therapeutic development, as shown by its prediction of DAAM1 in drug resistance.

Keywords:
causal inferencecell‐state transitiondisentangled representationdrug resistancenetwork control

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Cell state and phenotype transitions are crucial for biological processes and disease.
  • Identifying causal regulators from complex transcriptomic data is challenging due to confounding factors.

Purpose of the Study:

  • To develop a computational framework, CauFinder, for prioritizing causal regulators of cell state transitions from observational transcriptomic data.
  • To integrate causal disentanglement with network control for robust regulator identification.

Main Methods:

  • CauFinder utilizes causal reasoning based on do-calculus.
  • It employs information-flow metrics to separate causal factors from spurious associations.
  • The framework quantifies transition-relevant states and nominates master regulators.

Main Results:

  • CauFinder successfully identified regulators across various transitions, including differentiation and drug resistance.
  • It pinpointed DAAM1 as a novel driver in epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) resistance.
  • Small interfering RNA (siRNA)-mediated knockdown of DAAM1 validated its role in enhancing drug sensitivity.

Conclusions:

  • CauFinder provides a robust method for discovering causal regulators from transcriptomic data.
  • The framework enables actionable target nomination for therapeutic interventions in disease-relevant state transitions.
  • It generates testable hypotheses for modulating cell phenotype shifts.