No abstract

Collagen fibrils are essential for metazoan life. They are the largest, most abundant, and most versatile protein polymers in animals, where they occur in the extracellular matrix to form the structural basis of tissues and organs. Collagen fibrils were first observed at the turn of the 20th century. During the last 40 years, the genes that encode the family of collagens have been identified, the structure of the collagen triple helix has been solved, the many enzymes involved in the post-translational modifications of collagens have been identified, mutations in the genes encoding collagen and collagen-associated proteins have been linked to heritable disorders, and changes in collagen levels have been associated with a wide range of diseases, including cancer. Yet despite extensive research, a full understanding of how cells assemble collagen fibrils remains elusive. Here, we review current models of collagen fibril self-assembly, and how cells might exert control over the self-assembly process to define the number, length and organisation of fibrils in tissues.

Many biological processes, including tissue morphogenesis, are driven by cell sorting. However, the primary mechanical drivers of sorting in multicellular aggregates (MCAs) remain controversial, in part because there is no appropriate computational model to probe mechanical interactions between cells. To address this important issue, we developed a three-dimensional, local force-based simulation based on the subcellular element method. In our method, cells are modelled as collections of locally interacting force-bearing elements. We use the method to investigate the effects of tension and cell–cell adhesion on MCA sorting. We predict a minimum level of adhesion to produce inside-out sorting of two cell types, which is in excellent agreement with observations in several developmental systems. We also predict the level of tension asymmetry needed for robust sorting. The generality and flexibility of the method make it applicable to tissue self-organization in a myriad of other biological processes, such as tumorigenesis and embryogenesis.

No abstract

In this paper, we introduce a mechanistic model of migratory movement patterns in birds, inspired by ideas and methods from physics. Previous studies have shed light on the factors influencing bird migration but have mainly relied on statistical correlative analysis of tracking data. Our novel method offers a bottom up explanation of population-level migratory movement patterns. It differs from previous mechanistic models of animal migration and enables predictions of pathways and destinations from a given starting location. We define an environmental potential landscape from environmental data and simulate bird movement within this landscape based on simple decision rules drawn from statistical mechanics. We explore the capacity of the model by qualitatively comparing simulation results to the non-breeding migration patterns of a seabird species, the Black-browed Albatross (Thalassarche melanophris). This minimal, two-parameter model was able to capture remarkably well the previously documented migration patterns of the Black-browed Albatross, with the best combination of parameter values conserved across multiple geographically separate populations. Our physics-inspired mechanistic model could be applied to other bird and highly-mobile species, improving our understanding of the relative importance of various factors driving migration and making predictions that could be useful for conservation.

Coordination of cell proliferation and migration is fundamental for life, and its dysregulation has catastrophic consequences, such as cancer. How cell cycle progression affects migration, and vice-versa, remains largely unknown. We address these questions by combining in-silico modelling and in vivo experimentation in the zebrafish Trunk Neural Crest (TNC). TNC migrate collectively, forming chains with a leader cell directing the movement of trailing followers. We show that the acquisition of migratory identity is autonomously controlled by Notch signalling in TNC. High Notch activity defines leaders, while low Notch determines followers. Moreover, cell cycle progression is required for TNC migration and is regulated by Notch. Cells with low Notch activity stay longer in G1 and become followers, while leaders with high Notch activity quickly undergo G1/S transition and remain in S-phase longer. In conclusion, TNC migratory identities are defined through the interaction of Notch signalling and cell cycle progression.

In development, lineage segregation of multiple lineages must be coordinated in time and space. An important example is the mammalian inner cell mass (ICM), in which the primitive endoderm (PrE, founder of the yolk sac) physically segregates from the epiblast (EPI, founder of the foetus). The physical mechanisms that determine this spatial segregation between EPI and PrE are still poorly understood. Here, we identify an asymmetry in cell-cell affinity, a mechanical property thought to play a significant role in tissue sorting in other systems, between EPI and PrE precursors (pEPI and pPrE). However, a computational model of cell sorting indicated that these differences alone appeared insufficient to explain the spatial segregation. We also observed significantly greater surface fluctuations in pPrE compared to pEPI. Including the enhanced surface fluctuation in pPrE in our simulation led to robust cell sorting. We identify phospho-ERM regulated membrane tension as an important mediator of the increased surface fluctuations in pPrE. Using aggregates of engineered cell lines with different surface fluctuation levels cells with higher surface fluctuations were consistently excluded to the outside of the aggregate. These cells behaved similarly when incorporated in the embryo. Surface fluctuations-driven segregation is reminiscent of activity-induced phase separation, a sorting phenomenon in colloidal physics. Together, our experiments and model identify dynamic cell surface fluctuations, in addition to static mechanical properties, as a key factor for orchestrating the correct spatial positioning of the founder embryonic lineages.

Coordination of cell proliferation and migration is fundamental for life, and its dysregulation has catastrophic consequences, as cancer. How cell cycle progression affects migration, and vice-versa, remains largely unknown. We address these questions by combining in silico modelling and in vivo experimentation in the zebrafish Trunk Neural Crest (TNC). TNC migrate collectively, forming chains with a leader cell directing the movement of trailing followers. We show that the acquisition of migratory identity is autonomously controlled by Notch signalling in TNC. High Notch activity defines leaders, while low Notch determines followers. Moreover, cell cycle progression is required for TNC migration and is regulated by Notch. Cells with low Notch activity stay longer in G1 and become followers, while leaders with high Notch activity quickly undergo G1/S transition and remain in S-phase longer. We propose that migratory behaviours are defined through the interaction of Notch signalling and cell cycle progression.