Here are our highlights from September’s PLOS Computational Biology:
Model-Based Reasoning in Humans Becomes Automatic with Training
Automaticity develops with task familiarity. One possible explanation is that automaticity arises when performance of the task becomes habitual (model-free). Using a well-characterized task that differentiates model-based from model-free action, Marcos Economides and colleagues investigate whether goal-directed, or model-based, reasoning could also become automatic, or resistant to distraction. Their results suggest that humans can deploy sophisticated and flexible reasoning more extensively than previously thought.
A Gene Gravity Model for the Evolution of Cancer Genomes
Zhongming Zhao and colleagues have developed a new mathematical model to incorporate the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across nine cancer types from The Cancer Genome Atlas into a broad gene network. The authors found that cancer driver genes may shape somatic genome evolution by inducing mutations in other genes in cancer.
Social Feedback and the Emergence of Rank in Animal Society
An individual’s success depends critically on socially-constructed properties such as rank. Through a detailed study of two independent groups of captive parakeets, Elizabeth Hobson and Simon DeDeo show how these properties come into being. Their paper demonstrates how individuals can use localized patterns in the aggression network to learn the relative ranks of individuals, and that these signals of rank strongly correlate with individual decisions to aggress. Ultimately, individuals focus their aggression strategically on those closest in rank.
Mapping the Conformation Space of Mutant Ras
Important human diseases are linked to mutations in proteins. One such protein, Ras, undergoes mutations in over 25% of human cancers. Despite significant investigation in silico via methods based on Molecular Dynamics, details are missing on how mutations affect the ability of Ras to access the states it needs to perform its biological activity. Amarda Shehu and colleagues present an algorithm that is capable of providing such details by exploring the breadth of the structure space of a given protein, and apply it to normal H-Ras and two common oncogenic variants.