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Related Experiment Video

Updated: May 14, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Planar diagrams from optimization for concave potentials.

S K Nechaev¹, A N Sobolevski, O V Valba

¹LPTMS, Université Paris Sud, 91405 Orsay Cedex, France.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|February 16, 2013

Summary

This study introduces a simplified model for RNA secondary structures, revealing how nucleotide sequence and monomer spacing influence folding patterns. The research demonstrates a topological shift from sequential to nested configurations in RNA-like chains.

Related Experiment Videos

Last Updated: May 14, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

Computational Biology
Polymer Physics
Biophysics

Background:

RNA molecules form complex planar secondary structures crucial for their function.
Modeling these structures requires understanding the interplay between nucleotide sequence and physical constraints.
Existing models often simplify or neglect factors like monomer spacing variability.

Purpose of the Study:

To develop a simplified toy model for heteropolymer chains forming RNA-like planar secondary structures.
To investigate the influence of quenched random variables for sequential intervals and concave bond energies on folding.
To derive a model for the ground state free energy of planar RNA architectures.

Main Methods:

A simplified heteropolymer model neglecting loop factors, nucleotide-specific energies, stacking interactions, and loop size constraints.
Utilizing an optimization procedure from optimal transport analysis for concave-type potentials.
Deriving a local difference equation for the ground state free energy.
Analyzing truncated Gaussian and scale-free distribution functions for monomer intervals.

Main Results:

The model successfully captures folded structures without pseudoknots for arbitrary nucleotide sequences.
A topological crossover is demonstrated, transitioning from sequential to nested configurations of paired links.
The ground state free energy equation is derived for planar (RNA-like) architectures.

Conclusions:

The proposed toy model provides insights into RNA secondary structure formation, particularly the impact of monomer interval distributions.
The findings highlight a topological shift in folding patterns influenced by sequence and interval variability.
This work offers a framework for studying polymer folding with random sequential intervals and concave potentials.