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Algorithms for detecting and analysing autocatalytic sets.

Wim Hordijk1, Joshua I Smith2, Mike Steel2

  • 1SmartAnalytiX.com, Lausanne, Switzerland.

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|May 14, 2015
PubMed
Summary
This summary is machine-generated.

Researchers improved an algorithm for detecting autocatalytic sets, crucial for the origin of life. The enhanced method speeds up analysis and reveals millions of minimal autocatalytic sets, impacting theories on life's evolvability.

Keywords:
AlgorithmsAutocatalytic setsOrigin of life

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Area of Science:

  • Biochemistry
  • Theoretical Chemistry
  • Systems Biology

Background:

  • Autocatalytic sets are fundamental to understanding the origin of life.
  • Existing algorithms for detecting these sets are computationally intensive.
  • Theoretical and computational studies require efficient detection methods.

Purpose of the Study:

  • To introduce and apply a modified and extended algorithm for detecting autocatalytic sets.
  • To improve the computational efficiency of detecting autocatalytic sets.
  • To investigate autocatalytic sets in systems with inhibitory molecules.

Main Methods:

  • Modification of a basic algorithm to reduce computational cost.
  • Application of extensions to sample minimal autocatalytic sets.
  • Development of a statistical test to estimate the number of minimal sets.
  • Adaptation of the algorithm to handle inhibitory reactions.

Main Results:

  • The modified algorithm significantly reduces computational expense and running time.
  • A statistical test supports the existence of millions of minimal autocatalytic sets in a polymer model.
  • Autocatalytic sets can be found in systems with inhibition, with numbers dependent on catalysis vs. inhibition levels.

Conclusions:

  • The modified algorithm offers potential for improved running times in autocatalytic set searches.
  • The abundance of minimal autocatalytic sets has implications for their evolvability.
  • Inhibitory effects can be managed in reaction systems if inhibitors are limited.