On RAF Sets and Autocatalytic Cycles in Random Reaction Networks

  • Alessandro Filisetti
  • Marco Villani
  • Chiara Damiani
  • Alex Graudenzi
  • Andrea Roli
  • Wim Hordijk
  • Roberto Serra
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 445)


The emergence of autocatalytic sets of molecules seems to have played an important role in the origin of life context. Although the possibility to reproduce this emergence in laboratory has received considerable attention, this is still far from being achieved.

In order to unravel some key properties enabling the emergence of structures potentially able to sustain their own existence and growth, in this work we investigate the probability to observe them in ensembles of random catalytic reaction networks characterized by different structural properties.

From the point of view of network topology, an autocatalytic set have been defined either in term of strongly connected components (SCCs) or as reflexively autocatalytic and food-generated sets (RAFs).

We observe that the average level of catalysis differently affects the probability to observe a SCC or a RAF, highlighting the existence of a region where the former can be observed, whereas the latter cannot. This parameter also affects the composition of the RAF, which can be further characterized into linear structures, autocatalysis or SCCs.

Interestingly, we show that the different network topology (uniform as opposed to power-law catalysis systems) does not have a significantly divergent impact on SCCs and RAFs appearance, whereas the proportion between cleavages and condensations seems instead to play a role.

A major factor that limits the probability of RAF appearance and that may explain some of the difficulties encountered in laboratory seems to be the presence of molecules which can accumulate without being substrate or catalyst of any reaction.


Chemical Species Molecular Species Molecular Type Catalyst Product Reaction Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement n. 284625 and from “INSITE - The Innovation Society, Sustainability, and ICT” Pr.ref. 271574, under the 7th FWP - FET programme.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alessandro Filisetti
    • 1
  • Marco Villani
    • 1
    • 2
  • Chiara Damiani
    • 3
  • Alex Graudenzi
    • 3
  • Andrea Roli
    • 4
  • Wim Hordijk
    • 5
  • Roberto Serra
    • 1
    • 2
  1. 1.European Centre for Living TechnologyUniversity Ca’ Foscari of VeniceVeniceItaly
  2. 2.Department of Physics, Informatics and MathematicsUniversity of Modena and Reggio EmiliaModenaItaly
  3. 3.Department of Informatics, Systems and CommunicationUniversity of Milano BicoccaMilanItaly
  4. 4.Department of Computer Science and Engineering (DISI)University of BolognaBolognaItaly
  5. 5.SmartAnalytiX.comLausanneSwitzerland

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