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Modelling protein interactions, tracing sources of infection, stem cell timescales and mammalian navigation: the PLOS Comp Biol December issue

Here is our selection PLOS Computational Biology highlights for December.

PLOS Comp Biol Featured Image for December. Image Credit: Image Credit: Andreas Bunten and clix
PLOS Comp Biol Featured Image for December. Image Credit: Andreas Bunten and clix

One of the challenges in cancer research is inadequate understanding about the coordinated interactions between signalling proteins. Drugs specifically designed to target effects of these proteins are promising agents and so authors Chris Sander et al have developed a method adapted from statistical physics, called Belief Propagation. This method calculates the most likely interactions amongst all other potential solutions. The results have the potential to model the effects of hundreds of proteins on cellular responses.

Molecular data from pathogens may be useful for identifying sources of infection and identifying which host transmitted to the other. However, the source of transmission is often complicated by numerous factors, so Erik Volz et al present a method that incorporates additional information about infectious epidemics such as incidence and prevalence of infection over time. This informs estimates of the probability that one sampled host is the direct source of infection of another host in a pathogen gene genealogy.

Stem cells are known for their phenotypic heterogeneity and cell-to-cell variation. A new paper, entitled “Time Scales in Epigenetic Dynamics and Phenotypic Heterogeneity of Embryonic Stem Cells” analyses this phenotypic variation by simulating the stochastic dynamics of gene networks in embryonic stem cells. The researchers found that identifying the distribution of timescales of these processes is vital to the characterisation of the dynamical behaviour of the gene network.

Navigation in mammals is a well-studied area both behaviourally and neurophysiologically. Using computational neuroscience, researchers Will D. Penny et al created a probabilistic model to support multiple tasks such as computing a sequence of motor commands. The authors propose that these computations are reflected in recent findings of pattern replay in the mammalian brain.

By Chris Hall, Senior Publications Assistant, PLOS Computational Biology

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