Literature DB >> 12870897

A graph-based toy model of chemistry.

Gil Benkö1, Christoph Flamm, Peter F Stadler.   

Abstract

Large scale chemical reaction networks are a ubiquitous phenomenon, from the metabolism of living cells to processes in planetary atmospheres and chemical technology. At least some of these networks exhibit distinctive global features such as the "small world" behavior. The systematic study of such properties, however, suffers from substantial sampling biases in the few networks that are known in detail. A computational model for generating them is therefore required. Here we present a Toy Model that provides a consistent framework in which generic properties of extensive chemical reaction networks can be explored in detail and that at the same time preserves the "look-and-feel" of chemistry: Molecules are represented as labeled graphs, i.e., by their structural formulas; their basic properties are derived by a caricature version of the Extended Hückel MO theory that operates directly on the graphs; chemical reaction mechanisms are implemented as graph rewriting rules acting on the structural formulas; reactivities and selectivities are modeled by a variant of the Frontier Molecular Orbital Theory based on the Extended Hückel scheme. The approach is illustrated for two types of reaction networks: Diels-Alder reactions and the formose reaction implicated in prebiotic sugar synthesis.

Entities:  

Year:  2003        PMID: 12870897     DOI: 10.1021/ci0200570

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  10 in total

1.  Learning to predict chemical reactions.

Authors:  Matthew A Kayala; Chloé-Agathe Azencott; Jonathan H Chen; Pierre Baldi
Journal:  J Chem Inf Model       Date:  2011-09-02       Impact factor: 4.956

2.  Mutations and lethality in simulated prebiotic networks.

Authors:  Aron Inger; Ariel Solomon; Barak Shenhav; Tsviya Olender; Doron Lancet
Journal:  J Mol Evol       Date:  2009-09-29       Impact factor: 2.395

Review 3.  Hidden Concepts in the History and Philosophy of Origins-of-Life Studies: a Workshop Report.

Authors:  Carlos Mariscal; Ana Barahona; Nathanael Aubert-Kato; Arsev Umur Aydinoglu; Stuart Bartlett; María Luz Cárdenas; Kuhan Chandru; Carol Cleland; Benjamin T Cocanougher; Nathaniel Comfort; Athel Cornish-Bowden; Terrence Deacon; Tom Froese; Donato Giovannelli; John Hernlund; Piet Hut; Jun Kimura; Marie-Christine Maurel; Nancy Merino; Alvaro Moreno; Mayuko Nakagawa; Juli Peretó; Nathaniel Virgo; Olaf Witkowski; H James Cleaves
Journal:  Orig Life Evol Biosph       Date:  2019-08-09       Impact factor: 1.950

4.  The emergence of interstellar molecular complexity explained by interacting networks.

Authors:  Miguel García-Sánchez; Izaskun Jiménez-Serra; Fernando Puente-Sánchez; Jacobo Aguirre
Journal:  Proc Natl Acad Sci U S A       Date:  2022-07-22       Impact factor: 12.779

5.  What makes a reaction network "chemical"?

Authors:  Stefan Müller; Christoph Flamm; Peter F Stadler
Journal:  J Cheminform       Date:  2022-09-19       Impact factor: 8.489

6.  No electron left behind: a rule-based expert system to predict chemical reactions and reaction mechanisms.

Authors:  Jonathan H Chen; Pierre Baldi
Journal:  J Chem Inf Model       Date:  2009-09       Impact factor: 4.956

7.  Efficient reconstruction of metabolic pathways by bidirectional chemical search.

Authors:  Liliana Félix; Francesc Rosselló; Gabriel Valiente
Journal:  Bull Math Biol       Date:  2008-12-20       Impact factor: 1.758

8.  Exploring astrobiology using in silico molecular structure generation.

Authors:  Markus Meringer; H James Cleaves
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2017-12-28       Impact factor: 4.226

9.  Spatial rule-based modeling: a method and its application to the human mitotic kinetochore.

Authors:  Bashar Ibrahim; Richard Henze; Gerd Gruenert; Matthew Egbert; Jan Huwald; Peter Dittrich
Journal:  Cells       Date:  2013-07-02       Impact factor: 6.600

10.  Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure.

Authors:  Devlin Moyer; Alan R Pacheco; David B Bernstein; Daniel Segrè
Journal:  J Mol Evol       Date:  2021-07-06       Impact factor: 2.395

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.