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Scoop.it!
A team led by Stanford bioengineering Professor Markus Covert used data from more than 900 scientific papers to account for every molecular interaction that takes place in the life cycle of Mycoplasma genitalium — the world’s smallest free-living bacterium. 
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Scoop.it!
A team of scientists from the UCSF School of Pharmacy, Novartis Institutes for Biomedical Research (NIBR) and SeaChange Pharmaceuticals has developed a set of computer models that can predict negative side effects associated with existing drugs. By speeding up the process and increasing accuracy, the software could potentially save billions in research and decrease the number of animals used in toxicity tests.
The model, based on UCSF’s “similarity ensemble approach” (SEA), uses the similarities between the shape of each drug and thousands of other compounds to predict possible side effects. The theory behind SEA technology is that proteins can be related by their pharmacology, and these network relationships can be explored to discover new targets for established drugs.
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