PubMed · 15259152
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M P Cebollero Benito. 2004. [Spanish language].. https://doi.org/10.4321/s1130-01082004000400012
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BACKGROUND: Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R. RESULTS: A model structure that parallels SBML to a limited degree is defined in R. An interface between this structure and SBML is provided through two function definitions: write.SBML() which maps this R model structure to SBML level 2, and read.SBML() which maps a limited range of SBML level 2 files back to R. A published model of purine metabolism is provided in this SBML-like format and used to test the interface. The model reproduces published time course responses before and after its mapping through SBML. CONCLUSIONS: List infrastructure preexisting in R makes it well-suited for manipulating SBML models. Further developments of this SBML-R interface seem to be warranted.
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Biological evolution has parallels with the development of natural languages, man-made artifacts, and manuscript texts. As a result, phylogenetic methods developed for evolutionary biology are increasingly being used in linguistics, anthropology, archaeology, and textual criticism. Despite this popularity, there have been few critical tests of their suitability. Here, we apply phylogenetic methods to artificial manuscripts with a known true phylogeny, produced by modern 'scribes'. Although the survival of ancestral forms and multiple descendants from a single ancestor are probably much more common in manuscript evolution than biological evolution, we were able to reconstruct most of the true phylogeny. This is important because phylogenetic methods are influencing the production of critical editions of major written works. We also show that the variation in rates of change at different locations in the text follows a gamma distribution, as is often the case in DNA sequences.