Here is a selection of PLOS Computational Biology highlights for June.
“Minimization of opportunity costs” is a commonly used phrase within economics; however, less commonly known is that the fruit fly Drosophila melanogaster also uses this theory. In “A Normative Theory of Forgetting: Lessons from the Fruit Fly”, Brea et al. note that fruit fly behaviour is compatible with the classical optimality criterion of choosing actions that maximize future rewards. A consequence of future reward maximization is that negative experiences that lead to timid behaviour should be quickly forgotten in order to not miss out on potentially rewarding opportunities. Recent experiments have revealed that the fruit fly has a dedicated mechanism for forgetting, which is consistent with the view that forgetting is adaptive, rather than a consequence of limitations of the memory system. The authors show that forgetting in Drosophila appears as an optimal adaptive behaviour in a changing environment.
The 2009 H1N1 influenza pandemic provides a unique opportunity for Gog et al. to provide a detailed examination of the spatial dynamics of an emerging pathogen. In “Spatial Transmission of 2009 Pandemic Influenza in the US” the authors apply statistical and mathematical models to disease data and find that the main fall wave of the 2009 pandemic in the US was remarkably spatially structured. The authors report that the protracted spread of pandemic influenza in fall 2009 in the US was dominated by short-distance spatial spread partially catalysed by school openings rather by than long-distance transmission events. The findings of the paper underline the critical role that school-age children play in facilitating the geographic spread of pandemic influenza and highlight the need for further information on the movement and mixing patterns of this age group.
An open question in human genetics is what underlies the tissue-specific manifestation of hereditary diseases, which are caused by genomic aberrations present in cells across the entire human body. In order to answer this question, Barshir et al. analyzed this phenomenon for over 300 hereditary diseases and created a resource of protein expression and interactions across 16 main human tissues. In “Comparative Analysis of Human Tissue Interactomes Reveals Factors Leading to Tissue-Specific Manifestation of Hereditary Diseases” the authors identify two distinct, statistically-significant factors that could lead to tissue-specific vulnerability in the face of this broad expression: (i) many disease-causing genes have elevated expression levels in their disease tissues, and (ii) disease-causing genes have a significantly higher tendency for tissue-specific interactions in their disease tissues. Together the two factors identified are relevant for as many as two thirds of the tissue-specific hereditary diseases.