Inferring chemical reaction patterns using rule composition in graph grammars

BackgroundModeling molecules as undirected graphs and chemical reactions as graph rewriting operations is a natural and convenient approach to modeling chemistry. Graph grammar rules are most naturally employed to model elementary reactions like merging, splitting, and isomerisation of molecules. It is often convenient, in particular in the analysis of larger systems, to summarize several subsequent reactions into a single composite chemical reaction.ResultsWe introduce a generic approach for composing graph grammar rules to define a chemically useful rule compositions. We iteratively apply these rule compositions to elementary transformations in order to automatically infer complex transformation patterns. As an application we automatically derive the overall reaction pattern of the Formose cycle, namely two carbonyl groups that can react with a bound glycolaldehyde to a second glycolaldehyde. Rule composition also can be used to study polymerization reactions as well as more complicated iterative reaction schemes. Terpenes and the polyketides, for instance, form two naturally occurring classes of compounds of utmost pharmaceutical interest that can be understood as “generalized polymers” consisting of five-carbon (isoprene) and two-carbon units, respectively.ConclusionThe framework of graph transformations provides a valuable set of tools to generate and investigate large networks of chemical networks. Within this formalism, rule composition is a canonical technique to obtain coarse-grained representations that reflect, in a natural way, “effective” reactions that are obtained by lumping together specific combinations of elementary reactions.

[1]  Bernhard O Palsson,et al.  Network-based analysis of metabolic regulation in the human red blood cell. , 2003, Journal of theoretical biology.

[2]  Alexander Bockmayr,et al.  A new constraint-based description of the steady-state flux cone of metabolic networks , 2009, Discret. Appl. Math..

[3]  Gemma L. Holliday,et al.  MACiE: exploring the diversity of biochemical reactions , 2011, Nucleic Acids Res..

[4]  Peter F. Stadler,et al.  A Graph-Based Toy Model of Chemistry , 2003, J. Chem. Inf. Comput. Sci..

[5]  Michael Löwe,et al.  Algebraic Approach to Single-Pushout Graph Transformation , 1993, Theor. Comput. Sci..

[6]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[7]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[8]  J. Ziegler,et al.  Artificial Chemistries-A Review , 2001 .

[9]  Hartmut Ehrig,et al.  Parallelism and concurrency in high-level replacement systems , 1991, Mathematical Structures in Computer Science.

[10]  Christopher D. Thompson-Walsh,et al.  Graphs, Rewriting and Pathway Reconstruction for Rule-Based Models , 2012, FSTTCS.

[11]  Gerik Scheuermann,et al.  Evolution of metabolic networks: a computational frame-work , 2010 .

[12]  Benno Schwikowski,et al.  Graph-based methods for analysing networks in cell biology , 2006, Briefings Bioinform..

[13]  Reiko Heckel,et al.  Algebraic Approaches to Graph Transformation - Part II: Single Pushout Approach and Comparison with Double Pushout Approach , 1997, Handbook of Graph Grammars.

[14]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[15]  Christoph Flamm,et al.  The Graph Grammar Library - a generic framework for chemical graph rewrite systems , 2013, ICMT.

[16]  Ulrike Golas Analysis and correctness of algebraic graph and model transformations , 2011 .

[17]  Hartmut Ehrig,et al.  Fundamentals of Algebraic Graph Transformation , 2006, Monographs in Theoretical Computer Science. An EATCS Series.

[18]  Hartmut Ehrig,et al.  Introduction to the Algebraic Theory of Graph Grammars (A Survey) , 1978, Graph-Grammars and Their Application to Computer Science and Biology.

[19]  Francesc Rosselló,et al.  Graph Transformation in Molecular Biology , 2005, Formal Methods in Software and Systems Modeling.

[20]  Hartmut Ehrig,et al.  Fundamentals of Algebraic Graph Transformation (Monographs in Theoretical Computer Science. An EATCS Series) , 1992 .

[21]  Steffen Klamt,et al.  Hypergraphs and Cellular Networks , 2009, PLoS Comput. Biol..

[22]  Francesc Rosselló,et al.  Efficient Reconstruction of Metabolic Pathways by Bidirectional Chemical Search , 2009, Bulletin of mathematical biology.

[23]  D. Fell,et al.  A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks , 2000, Nature Biotechnology.