Here are our highlights from January’s PLOS Computational Biology
Predicting Anticancer Drug Activity
There is increasing evidence that altering different functional regions within the same protein can lead to dramatically distinct phenotypes. By focusing on individual regions instead of whole proteins, Adam Godzik and colleagues are able to identify novel correlations that predict the activity of anticancer drugs. The authors also show how associations found between protein regions and drugs – using only data from cancer cell lines – can predict the survival of cancer patients. All the associations described in the paper are available from http://www.cancer3d.org.
Identification of Constrained Cancer Driver Genes
Cancer genome sequencing projects result in vast amounts of cancer mutation data, but our understanding of which mutations are driving tumor growth and which are selectively neutral is lagging behind. Functional interactions among mutations can result in mutational dependencies, and these mutations then display low marginal mutation frequencies across tumor samples, complicating the identification of these drivers. Niko Beerenwinkel and colleagues present a new computational method for calling candidate driver mutations by discriminating dependent mutations from independent ones based on their dynamical patterns of occurrence.
The Adaptability of the Human Brain
The human brain is a complex system in which the interactions of billions of neurons give rise to a fascinating range of behaviours. Across situations involving rest, memory, focused attention, or learning, the brain dynamically switches between distinct patterns of activation. Jean M. Carlson and colleagues apply new techniques from dynamic network theory to describe the functional interactions between brain regions as an evolving network. By examining patterns of neural activity during rest – an attention-demanding task – and two memory-demanding tasks, the authors identify groups of brain region interactions that change cohesively together over time, both across tasks and within individual tasks.