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Nonequilibrium Self-Assembly Time Forecasting by the Stochastic Landscape Method.

Michael Faran1, Gili Bisker1,2,3,4

  • 1Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel.

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Summary
This summary is machine-generated.

This study introduces a new method to predict how long nonequilibrium self-assembly takes. The stochastic landscape method (SLM) uses data segmentation to forecast assembly times more accurately.

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Area of Science:

  • Physical Chemistry
  • Biophysics
  • Statistical Mechanics

Background:

  • Biological systems often self-assemble complex structures from molecular building blocks using external energy sources.
  • The process involves navigating a complex energy landscape with many local minima, making prediction difficult.
  • Understanding and predicting assembly times is crucial for controlling these nonequilibrium processes.

Purpose of the Study:

  • To develop a predictive framework for the first assembly times in multicomponent systems driven by nonequilibrium conditions.
  • To explore the statistical distribution of assembly times under varying nonequilibrium drive strengths.
  • To introduce and validate a data-driven algorithmic scheme for improved assembly time forecasting.

Main Methods:

  • Utilized a toy physical model of multicomponent nonequilibrium self-assembly.
  • Employed data segmentation via a Bayesian estimator of abrupt changes (BEAST) to analyze system dynamics.
  • Developed and implemented the stochastic landscape method (SLM) for assembly time prediction.

Main Results:

  • Demonstrated that a segmented description of system dynamics accurately predicts first assembly times.
  • Observed a log-normal distribution for first assembly time statistics across a wide range of nonequilibrium drive values.
  • Showed that the stochastic landscape method (SLM) offers improved prediction power over simpler methods.

Conclusions:

  • The stochastic landscape method (SLM) provides a robust, data-based approach for forecasting nonequilibrium self-assembly times.
  • The findings establish a quantitative framework for understanding and controlling complex nonequilibrium self-assembly processes.
  • This work has implications for designing and optimizing self-assembly protocols in various scientific and engineering fields.