[This corrects the article DOI: 10.1371/journal.pcbi.1008412.].

How epithelial cells coordinate their polarity to form functional tissues is an open question in cell biology. Here, we characterize a unique type of polarity found in liver tissue, nematic cell polarity, which is different from vectorial cell polarity in simple, sheet-like epithelia. We propose a conceptual and algorithmic framework to characterize complex patterns of polarity proteins on the surface of a cell in terms of a multipole expansion. To rigorously quantify previously observed tissue-level patterns of nematic cell polarity (Morales-Navarrete et al., eLife 2019), we introduce the concept of co-orientational order parameters, which generalize the known biaxial order parameters of the theory of liquid crystals. Applying these concepts to three-dimensional reconstructions of single cells from high-resolution imaging data of mouse liver tissue, we show that the axes of nematic cell polarity of hepatocytes exhibit local coordination and are aligned with the biaxially anisotropic sinusoidal network for blood transport. Our study characterizes liver tissue as a biological example of a biaxial liquid crystal. The general methodology developed here could be applied to other tissues and in-vitro organoids.

Abstract Summary: Morpheus is a modeling environment for the simulation and integration of cell-based models with ordinary differential equations and reaction-diffusion systems. It allows rapid development of multiscale models in biological terms and mathematical expressions rather than programming code. Its graphical user interface supports the entire workflow from model construction and simulation to visualization, archiving and batch processing. Availability and implementation: Binary packages are available at http://imc.zih.tu-dresden.de/wiki/morpheus for Linux, Mac OSX and MS Windows. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Food webs are complex networks of trophic interactions in ecological communities that are crucial in creating and maintaining biodiversity, and are prominent examples of biological complexity. In this paper, we present an individual-based model of an artificial ecology demonstrating the emergence of complex food webs through the evolution of rich phenotypes. Individuals are simple structures that map several traits in a nonlinear fashion. Interaction and evolution of these structures leads to the self-assembly of food webs in complex ecological communities. Ecological and network analysis of the evolved artificial ecologies shows remarkable similarities in various patterns known from natural ecological communities.

No abstract available.