Zebra finch song is represented in the high-level motor control nucleus high vocal center (HVC) (Reiner et al., 2004) as a sparse sequence of spike bursts. In contrast, the vocal organ is driven continuously by smoothly varying muscle control signals. To investigate how the sparse HVC code is transformed into continuous vocal patterns, we recorded in the singing zebra finch from populations of neurons in the robust nucleus of arcopallium (RA), a premotor area intermediate between HVC and the motor neurons. We found that highly similar song elements are typically produced by different RA ensembles. Furthermore, although the song is modulated on a wide range of time scales (10-100 ms), patterns of neural activity in RA change only on a short time scale (5-10 ms). We suggest that song is driven by a dynamic circuit that operates on a single underlying clock, and that the large convergence of RA neurons to vocal control muscles results in a many-to-one mapping of RA activity to song structure. This permits rapidly changing RA ensembles to drive both fast and slow acoustic modulations, thereby transforming the sparse HVC code into a continuous vocal pattern.

Recent advances in computation-based protein engineering offer opportunities to introduce or modify the biophysical characteristics of proteins at will. The power of computational design comes from the ability to surpass the combinatorial and physical limitations inherent to laboratory-based high-throughput or trial-and-error methods. As a result, modifications that require significant changes to the amino acid sequence of a protein are now accessible to the protein engineering community. Hydrophobic cores of proteins have been repacked to increase their thermostability. Binding sites in proteins have been modified to increase affinity or alter specificity for proteins, peptides, and small molecules. Enzymes have been designed de novo. Non-natural protein folds have been created. For the most part, these achievements have been applied to proteins that make good model systems in academic settings. How can these computational methods be applied to therapeutically relevant proteins? This review will focus on the ground-breaking achievements of computation-based protein engineering and on recent applications of rational design to improve therapeutic proteins. - See more at: http://www.eurekaselect.com/90585/article/computation-based-design-and-engineering-protein-and-antibody-therapeutics#sthash.kSt3UaNE.dpuf

Mutations in the Drosophila retained/dead ringer (retn) gene lead to female behavioral defects and alter a limited set of neurons in the CNS. retn is implicated as a major repressor of male courtship behavior in the absence of the fruitless (fru) male protein. retn females show fru-independent male-like courtship of males and females, and are highly resistant to courtship by males. Males mutant for retn court with normal parameters, although feminization of retn cells in males induces bisexuality. Alternatively spliced RNAs appear in the larval and pupal CNS, but none shows sex specificity. Post-embryonically, retn RNAs are expressed in a limited set of neurons in the CNS and eyes. Neural defects of retn mutant cells include mushroom body beta-lobe fusion and pathfinding errors by photoreceptor and subesophageal neurons. We posit that some of these retn-expressing cells function to repress a male behavioral pathway activated by fruM.

The insulin signaling pathway regulates multiple physiological processes, including energy metabolism, organismal growth, aging and reproduction. Here we show that genetic manipulations in Drosophila melanogaster that impair the function of insulin-producing cells or of the insulin-receptor signaling pathway in the nervous system lead to increased sensitivity to the intoxicating effects of ethanol. These findings suggest a previously unknown role for this highly conserved pathway in regulating the behavioral responses to an addictive drug.

There is little consensus about the computational function of top-down synaptic connections in the visual system. Here we explore the hypothesis that top-down connections, like bottom-up connections, reflect partwhole relationships. We analyze a recurrent network with bidirectional synaptic interactions between a layer of neurons representing parts and a layer of neurons representing wholes. Within each layer, there is lateral inhibition. When the network detects a whole, it can rigorously enforce part-whole relationships by ignoring parts that do not belong. The network can complete the whole by filling in missing parts. The network can refuse to recognize a whole, if the activated parts do not conform to a stored part-whole relationship. Parameter regimes in which these behaviors happen are identified using the theory of permitted and forbidden sets [3, 4]. The network behaviors are illustrated by recreating Rumelhart and McClelland’s “interactive activation” model [7].