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Ruleset Optimization on Isomorphic Oritatami Systems.

Yo-Sub HanHwee Kim
Published in: Theoretical computer science (2019)
We study an optimization problem of a computational folding model, proving its hardness and proposing heuristic algorithms. RNA cotranscriptional folding refers to the phenomenon in which an RNA transcript folds upon itself while being synthesized out of a gene. An oritatami model (OM) is a computational model of this phenomenon that lets its sequence of beads (abstract molecules) fold cotranscriptionally by the interactions between beads, according to its ruleset. We study the problem of reducing the ruleset size, while keeping the terminal conformations geometrically the same. We first prove the hardness of finding the smallest ruleset, and then suggest two approaches that reduce the ruleset size efficiently.
Keyphrases
  • machine learning
  • single molecule
  • deep learning
  • gene expression
  • transcription factor
  • nucleic acid