Article open access publication

An intermediate level of abstraction for computational systems chemistry

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences, The Royal Society, ISSN 1364-503X

Volume 375, 2109, 2017

DOI:10.1098/rsta.2016.0354, Dimensions: pub.1092674096, PMC: PMC5686410, PMID: 29133452,

Authors

Stadler, Peter F. (4) (5) (6) (7) (8)

Affiliations

Organisations

  1. (1) Tokyo Institute of Technology, grid.32197.3e
  2. (2) University of Southern Denmark, grid.10825.3e, SDU
  3. (3) University of Vienna, grid.10420.37
  4. (4) Fraunhofer Institute for Cell Therapy and Immunology, grid.418008.5
  5. (5) Leipzig University, grid.9647.c
  6. (6) Max Planck Institute for Mathematics in the Sciences, grid.419532.8
  7. (7) Santa Fe Institute, grid.209665.e
  8. (8) University of Copenhagen, grid.5254.6, KU

Description

Computational techniques are required for narrowing down the vast space of possibilities to plausible prebiotic scenarios, because precise information on the molecular composition, the dominant reaction chemistry and the conditions for that era are scarce. The exploration of large chemical reaction networks is a central aspect in this endeavour. While quantum chemical methods can accurately predict the structures and reactivities of small molecules, they are not efficient enough to cope with large-scale reaction systems. The formalization of chemical reactions as graph grammars provides a generative system, well grounded in category theory, at the right level of abstraction for the analysis of large and complex reaction networks. An extension of the basic formalism into the realm of integer hyperflows allows for the identification of complex reaction patterns, such as autocatalysis, in large reaction networks using optimization techniques.This article is part of the themed issue 'Reconceptualizing the origins of life'.

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University of Southern Denmark

University of Copenhagen

Dimensions Citation Indicators

Times Cited: 3

Field Citation Ratio (FCR): 0.88

Relative Citation ratio (RCR): 0.19

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