A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Abstract

Genomic data now allow the large-scale manual or semi-automated reconstruction of metabolic networks. A network reconstruction represents a highly curated organism-specific knowledge base. A few genome-scale network reconstructions have appeared for metabolism in the baker’s yeast Saccharomyces cerevisiae. These alternative network reconstructions differ in scope and content, and further have used different terminologies to describe the same chemical entities, thus making comparisons between them difficult. The formulation of a ‘community consensus’ network that collects and formalizes the ‘community knowledge’ of yeast metabolism is thus highly desirable. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. Special emphasis is laid on referencing molecules to persistent databases or using database-independent forms such as SMILES or InChI strings, since this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language, and we describe the manner in which it can be maintained as a community resource. It should serve as a common denominator for system biology studies of yeast. Similar strategies will be of benefit to communities studying genome-scale metabolic networks of other organisms.

Dynamic Transcriptional and Metabolic Responses in Yeast Adapting to Temperature Stress

“…The complexity of covered metabolites is further reduced to those, which can be profiled accurately by mass isotopomer ratios. A recent effort to consolidate the various metabolic network reconstructions resulted in an estimated number of 911 unique chemical transformations (excluding transport reactions) operating on 1,168 predicted metabolites (Herrgard et al, 2008). Thus, because of technological constraints, the metabolite data reported in this study correspond to only as small portion of the complete yeast metabolome, highlighting the needed for a substantial expansion of reach of metabolomics technologies.…”

Flux balance analysis: A geometric perspective

“…Whilst the first stoichiometric model of E. coli was limited to the central metabolic pathways (Varma and Palsson, 1993), the most recent reported model is much more comprehensive, consisting of 2077 reactions and 1039 metabolites (Feist et al, 2007). Reaction networks for S. cerevisiae have been similarly expanded through incorporation of more genes and their corresponding metabolites -a recent consensus model consists of 1761 reactions and 1168 metabolites (Herrgå rd et al, 2008). Genome-scale stoichiometric models for other micro-organisms (Kim et al, 2008) and even H. sapiens (Duarte et al, 2007) have been developed.…”

Computing life: Add logos to biology and bios to physics

“…If a cell contains about 1000 metabolic enzymes ('enzyme types' really, but we assume that all enzymes defined by the same gene(s) behave as a single ensemble) and about 500 metabolites ('metabolite types' really, but we again assume ensemble behaviour), maximally 5 Â 10 5 binary enzymemetabolite interactions are possible. These are the current numbers for yeast (Herrgard et al, 2008;Smallbone et al, 2011;Heavner et al, 2012) number, but taking into account increasing computational power, it should not constitute cause for any principal limitation. The essence of these calculations is that, if one foregoes the natural organisation of living systems, the number of interactions appears astronomical, but with a bit of realism, these numbers turn out to become manageable within a few decades.…”