Microbial association networks, error in sensorimotor learning, genetic drift in range expansions
Check out our highlights from the PLOS Computational Biology December issue:
MPLasso: Inferring microbial association networks using prior microbial knowledge
Microbial communities exhibit rich dynamics including the way they adapt, develop, and interact with the human body and with the surrounding environment. The associations among microbes can provide a solid foundation to model the interplay between the (host) human body and the microbial populations. However, due to the unique properties of compositional and high-dimensional nature of microbial data, standard statistical methods are likely to produce spurious results. Although several existing methods can estimate the associations among microbes under the sparsity assumption, they still have major difficulties inferring the associations among microbes, given such high-dimensional data. To enhance the model accuracy on inferring microbial associations, Chieh Lo and Radu Marculescu propose integrating multiple levels of biological information by mining the co-occurrence patterns and interactions directly from large body of scientific literature. They first show that their proposed method can outperform existing methods in synthetic experiments, and then obtain credible inference results from Human Microbiome Project datasets when compared against laboratory data. By creating a more accurate microbial association network, scientists will be able to better focus their efforts when experimentally verifying microbial associations by eliminating the need to perform exhaustive searches on all possible pairs of associations.
An error-tuned model for sensorimotor learning
Research in motor learning has focused on how we acquire new motor memories for novel situations. However, in many real-world motor tasks, the challenge is to select appropriate memories for a given context. In such tasks, we are guided by two key types of information. First, contextual information from vision (for example) is available before we perform the task. Second, movement errors are available as we begin to perform the task. Here James Ingram and colleagues present a model that provides a mechanism by which these two processes operate in parallel to enable them to tune and adapt our motor commands. They show that a model consisting of multiple simple modules, each of which can correct errors in a single direction only, can account for learning in multidimensional tasks. The model makes predictions about which tasks should interfere and how experience of errors alone without any contextual information can drive learning. They confirm these predictions in a series of experiments. The model provides a new framework for understanding the interaction between task context and error feedback during sensorimotor control and learning.
Genetic drift and selection in many-allele range expansions
Population expansions occur naturally during the spread of invasive species and played a role in our evolutionary history when humans migrated out of Africa. Bryan T. Weinstein and colleagues use a colony of non-motile bacteria expanding into unoccupied, nutrient-rich territory on an agar plate as a model system to explore how an expanding population’s spatial structure impacts its evolutionary dynamics. Spatial structure is present in expanding microbial colonies because daughter cells migrate only a small distance away from their mothers each generation. Generally, the constituents of expansions occurring in nature and in the lab have different genetic compositions (genotypes, or alleles if a single gene differs), each instilling different fitnesses, which compete to proliferate at the frontier. Here, they show that a random-walk model can accurately predict the dynamics of four expanding strains of E. coli with different fitnesses; each strain represents a competing allele. The authors’ results can be extended to describe any number of competing genotypes with different fitnesses in a naturally occurring expansion as long as the underlying motility of the organisms does not cause our model to break down. Their model can also be used to precisely measure small selective differences between spatially competing genotypes in controlled laboratory settings.