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Modelling cell interaction, deciding on optimal paths and tracking tumour evolution: The PLOS Comp Biol August Issue

Here is our selection of PLOS Computational Biology highlights for August.

Using a model of collective learning to understand how individual learning affects consensus group decisions. Image credit: Albert Kao

During the embryonic development of multicellular organisms, millions of cells cooperatively build structured tissues, organs and whole organisms, a process called morphogenesis. It is still not entirely understood how the behaviour of so many cells is coordinated to produce complex structures. In response to this question, Roeland Merks et al. propose a new computational model that shows a simple form of mechanical cell-cell communication suffices for reproducing the formation of blood vessel-like structures in cell cultures. These findings advance our understanding of biomechanical signalling during morphogenesis, and introduce a new set of computational tools for modelling mechanical interactions between cells. Combining the present mechanical model with aspects of previously proposed mechanical and chemical models may lead to a more complete understanding of in vitro angiogenesis.

Human behaviour has long been recognised to display a hierarchical structure, in that we organise tasks into subtasks, which fit into extended goal-directed activities. Arranging actions hierarchically has well established benefits, allowing behaviours to be represented efficiently by the brain, and allowing solutions to new tasks to be discovered easily. But how do we learn to subdivide our goals in this way?  Matthew M. Botninick et al. have developed a mathematical account for how we differentiate between hierarchies and choose the optimal path. The authors then presented results from four behavioural experiments, suggesting that human learners spontaneously discover optimal action hierarchies.

Errors in sample annotation or labelling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. On that basis, Jun Zhu et al. developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labelling errors in multiple types of molecular data, which can be used in further integrative analysis.

Often, accurately characterizing a tumour requires analysing multiple samples from the same patient. To address this need, Li Ding et al. present SciClone, a computational method that identifies the number and genetic composition of subclones by analysing the variant somatic mutations. They used it to detect subclones in leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumour sample. By doing so, they tracked tumour evolution and identified the spatial origins of cells resisting therapy.

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