The complexity of comparing reaction systems

Abstract

We show that comparing the stoichiometric structure of two reactions systems is equivalent to the graph isomorphism problem. This is encouraging because graph isomorphism is, in practice, a tractable problem using heuristics. The analogous problem of searching for a subsystem of a reaction system is NP-complete. We also discuss heuristic issues in implementations for practical comparison of stoichiometric matrices.

Information extraction from full text scientific articles: Where are the keywords?

“…But further tagging using markup codes in XML style [17] identifying biological objects and concepts (under development; see for example [18] or the GENIA project ) could ultimately make text mining a children's game. We hope for future interfaces for writers of Molecular Biology articles that should do the job upon validation by the authors (for example, marking every occurrence of a gene name with a unique and stable link to any of the existing gene sequence databases).…”

The Database of Quantitative Cellular Signaling: management and analysis of chemical kinetic models of signaling networks

“…We have implemented this by creating a thesaurus of canonical names, as previously discussed by Juty et al (2001), but this entails additional curation effort. There are also possible heuristic approaches to matching molecules, reactions and enzymes based on their positions in reaction graphs (Patel et al, 2001;Ettinger, 2002). Each of these approaches presents drawbacks.…”

Model comparison via simplicial complexes and persistent homology

“…There are very few attempts to establish distances between models [24,25], and many of the candidates for a distance between models have obvious shortcomings: graph-based distances on model structures, for example, fail because many dynamical systems cannot be described in terms of simple graphs [18]; other well-defined distances used in functional analysis are too limited in scope, as are distances among e.g. stoichiometric matrices, which only apply for certain types of models; semantic distances between model formulations in e.g.…”