Download A minimal genome is the smallest set of genes required for an o

Designing smart drugs relies on well defined drug targets to severely perturb the
cellular system of the pathogenic organism. For a large range of single-cellular
parasites including bacteria and fungi, this task has been tackled by viability screens
of single-gene-knock-outs on a large scale comprising of more or less all open reading
frames of the parasitic genome (see e.g. Baba et al., Mol Syst Biol, 2006). Recently,
we employed a machine learning strategy to study and qualitatively validate the
outcome of these experimental screens. We showed that topologic (qualitative flux
deviations, damage, hubs, network clustering), genomic (sequence homologies) and
transcriptomic (for detecting analogs) attributes describing the network are sufficient
for defining the essentiality of a reaction (accuracy = 93% for the E. coli KEIO
collection (Baba et al., 2006)) (Plaimas, BMC Syst Biol, 2008). In contrast to flux
balance analyses, our approach can also be applied if no experimental quantitative
metabolomic and specific enzymatic data is available. We have now developed our
method further for predictions across organisms. From the machine learning
perspective, this is a more challenging task. We trained and validated with E. coli and
Pseudomonas aeruginosa, respectively, and vice versa, resulting in good accuracies
of >75%. We then applied these machines to a less comprehensive experimental
knock out screen of the aggressive pathogen Salmonella typhimurium and identified
new interesting drug targets. In principle, our technique can be applied to any cellular
network and may also be used define drug target combinations to define specific drug
cocktails.