Antimicrobial peptides (AmPs) are small proteins that are used by the innate immune system to combat bacterial infection in multicellular eukaryotes. There is mounting evidence that these peptides are less susceptible to bacterial resistance than traditional antibiotics and could form the basis for a new class of therapeutic agents. Here we report the rational design of new AmPs that show limited homology to naturally occurring proteins but have strong bacteriostatic activity against several species of bacteria, including Staphylococcus aureus and Bacillus anthracis. These peptides were designed using a linguistic model of natural AmPs: we treated the amino-acid sequences of natural AmPs as a formal language and built a set of regular grammars to describe this language. We used this set of grammars to create new, unnatural AmP sequences. Our peptides conform to the formal syntax of natural antimicrobial peptides but populate a previously unexplored region of protein sequence space.

Analysis of metabolomic profiling data from gas chromatography-mass spectrometry (GC/MS) measurements usually relies upon reference libraries of metabolite mass spectra to structurally identify and track metabolites. In general, techniques to enumerate and track unidentified metabolites are nonsystematic and require manual curation. We present a method and software implementation, freely available at http://spectconnect.mit.edu, that can systematically detect components that are conserved across samples without the need for a reference library or manual curation. We validate this approach by correctly identifying the components in a known mixture and the discriminating components in a spiked mixture. Finally, we demonstrate an application of this approach with a brief analysis of the Escherichia coli metabolome. By systematically cataloguing conserved metabolite peaks prior to data analysis methods, our approach broadens the scope of metabolomics and facilitates biomarker discovery.

The impact of gene patents on downstream research and innovation are unknown, in part because of a lack of empirical data on the extent and nature of gene patenting. In this Policy Forum, the authors show that 20% of human gene DNA sequences are patented and that some genes are patented as many as 20 times. Unsurprisingly, genes associated with health and disease are more patented than the genome at large. The intellectual property rights for some genes can become highly fragmented between many owners, which suggests that downstream innovators may face considerable costs to gain access to gene-oriented technologies.

No abstract

No abstract

No abstract

Acknowledgements:We thank Pierre Azoulay, Scott Stern, Nico Lacetera, Dietmar Harhoff, and participants in numerous seminars for comments and suggestions. Lisa Bassett, Anne-Marie Crain, Michaël Bikard, Devin Fensterheim, Robyn Fialkow, Jacob Magid, and Lexie Somers provided exceptional research assistance. All errors are our own. Financial support for this research was provided by the National Science Foundation, under grant #0738394.Electronic copy available at: http://ssrn.com/abstract=2014481Governing knowledge in the scientific community:Exploring the role of retractions in biomedicine ABSTRACTAlthough the validity of knowledge is critical to scientific progress, substantial concerns exist regarding the governance of knowledge production. While as or more important to the knowledge economy as defects are in the manufacturing economy, mechanisms to identify and signal "defective" or false knowledge are poorly understood. In this paper, we investigate one such institution -the system of scientific retractions. By analyzing the universe of peer-reviewed scientific articles retracted from the biomedical literature between 1972-2006 and comparing with a matched control sample, we identify the correlates, timing, and causal impact of scientific retractions, thus providing insight into the workings of a distributed, peer-based system for the governance of validity in scientific knowledge. Our findings suggest that attention is a key predictor of retraction -retracted articles arise most frequently among highly-cited articles. The retraction system is expeditious in uncovering knowledge that is ever determined to be false (the mean time to retraction is less than two years) and democratic (retraction is not systematically affected by author prominence). Lastly, retraction causes an immediate, severe, and long-lived decline in future citations. Conditional on the obvious limitation that we cannot measure the absolute amount of false science in circulation, these results support the view that distributed governance systems can be designed to relatively swiftly to uncover false knowledge and to mitigate the costs that false knowledge for future generations of producers.

Gemoda is freely available at http://web.mit.edu/bamel/gemoda