Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

Pyruvate formate-lyase (PFL) catalyzes the non-oxidative conversion of pyruvate to formate and acetyl-CoA. PFL and its activating enzyme (PFL-AE) are common among strict anaerobic and microaerophilic prokaryotes but are very rare among eukaryotes. In a proteome survey of isolated Chlamydomonas reinhardtii mitochondria, we found several PFL-specific peptides leading to the identification of cDNAs for PFL and PFL-AE, establishing the existence of a PFL system in this photosynthetic algae. Anaerobiosis and darkness led to increased PFL transcripts but had little effect on protein levels, as determined with antiserum raised against C. reinhardtii PFL. Protein blots revealed the occurrence of PFL in both chloroplast and mitochondria purified from aerobically grown cells. Mass spectrometry sequencing of C. reinhardtii mitochondrial proteins, furthermore, identified peptides for phosphotransacetylase and acetate kinase. The phosphotransacetylase-acetate kinase pathway is a common route of ATP synthesis or acetate assimilation among prokaryotes but is novel among eukaryotes. In addition to PFL and pyruvate dehydrogenase, the algae also expresses pyruvate:ferredoxin oxidoreductase and bifunctional aldehyde/alcohol dehydrogenase. Among eukaryotes, the oxygen producer C. reinhardtii has the broadest repertoire of pyruvate-, ethanol-, and acetate-metabolizing enzymes described to date, many of which were previously viewed as specific to anaerobic eukaryotic lineages.

BackgroundConstraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users.ResultsHere, we present sybil, an open source software library for constraint-based analyses in R; R is a free, platform-independent environment for statistical computing and graphics that is widely used in bioinformatics. Among other functions, sybil currently provides efficient methods for flux-balance analysis (FBA), MOMA, and ROOM that are about ten times faster than previous implementations when calculating the effect of whole-genome single gene deletions in silico on a complete E. coli metabolic model.ConclusionsDue to the object-oriented architecture of sybil, users can easily build analysis pipelines in R or even implement their own constraint-based algorithms. Based on its highly efficient communication with different mathematical optimisation programs, sybil facilitates the exploration of high-dimensional optimisation problems on small time scales. Sybil and all its dependencies are open source. Sybil and its documentation are available for download from the comprehensive R archive network (CRAN).

lercher@cs.uni-duesseldorf.de.