Detecting cancer-causing genes, computing how to beat jetlag and a review of research into decision-making: The PLOS Comp Biol April issue

Here is our selection of PLOS Computational Biology highlights for April.

Computational prediction of cancer-associated single nucleotide polymorphisms (SNPs) from SNP datasets can now be used as a tool for detecting probable cancer-causing genes. This work, by Rituraj Purohit et al., applies computational tools to prioritize the most harmful disease associated mutation in Aurora kinases. Sequence and structural based approaches were used to refine cancer associated mutation, and a long-term simulation (MDS) was applied in order to understand the changes in structural conformation and function of the aurora kinases upon mutation. Out of 60 SNPs, 24 were calculated to be deleterious as well as damaging.

Two papers we published in April received widespread attention in the media. The first paper, by Daniel Forger et al., presents a mathematical model for dealing with the effects of jet lag. By calculating thousands of schedules, the authors show how the human circadian pacemaker is capable of shifting much more rapidly than previously thought, simply by adjusting the timing of the beginning and end of each day. You can read the New Scientist article here.

The second paper to gain attention, by David J. McIver and John S. Brownstein, estimated levels of influenza in America by monitoring Internet traffic on specific Wikipedia articles. The developed model can accurately estimate the percentage of Americans with influenza-like illnesses in real-time. You can read more about it in this article by the Huffington Post

Despite the research that has gone into the workings of decision-making, the neural mechanisms underlying these processes are not fully understood. This Review article by Ranulfo Romo et al. looks at the recent progress made in this field of study and performs a critical evaluation of the available results from a computational perspective. The study was guided by a central question, which was “how does the spatiotemporal structure of sensory stimuli affect the perceptual decision-making process?”