New and Exciting on PLoS Computational Biology

PERIOD-TIMELESS Interval Timer May Require an Additional Feedback Loop by Robert S. Kuczenski, Kevin C. Hong, Jordi GarcÃa-Ojalvo and Kelvin H. Lee:

The ability of an organism to adapt to daily changes in the environment, via a circadian clock, is an inherently interesting phenomenon recently connected to several human health issues. Decades of experiments on one of the smallest model animals, the fruit fly Drosophila, has illustrated significant similarities with the mammal circadian system. Within Drosophila, the PERIOD and TIMELESS proteins are central to controlling this rhythmicity and were recently shown to have a rapid and stable association creating an "interval" timer in the cell's cytoplasm. This interval timer creates the necessary delay between the expression and activity of these genes, and is directly opposed to the previous hypothesis of a delay created by slow association. We use several mathematical models to investigate the unknown factors controlling this timer. Using a novel positive feedback loop, we construct a circadian model consistent with the interval timer and many wild-type and mutant experimental observations. Our results suggest several novel genes and interactions to be tested experimentally.

Distributed Representations Accelerate Evolution of Adaptive Behaviours by James V. Stone:

Some behaviours are purely innate (e.g., blinking), whereas other, "apparently innate," behaviours require a degree of learning to refine them into a useful skill (e.g., nest building). In terms of biological fitness, it matters how quickly such learning occurs, because time spent learning is time spent not eating, or time spent being eaten, both of which reduce fitness. Using artificial neural networks as model organisms, it is proven that it is possible for an organism to be born with a set of "primed" connections which guarantee that learning part of a skill induces automatic learning of other skill components, an effect known as free-lunch learning (FLL). Critically, this effect depends on the assumption that associations are stored as distributed representations. Using a genetic algorithm, it is shown that primed organisms can evolve within 30 generations. This has three important consequences. First, primed organisms learn quickly, which increases their fitness. Second, the presence of FLL effectively accelerates the rate of evolution, for both learned and innate skill components. Third, FLL can accelerate the rate at which learned behaviours become innate. These findings suggest that species may depend on the presence of distributed representations to ensure rapid evolution of adaptive behaviours.