This article is part of the theme issue 'New insights on tidal dynamics and tidal energy harvesting in the Alderney Race’.The Alderney Race is assumed to have the largest tidal-stream energy potential in the north-western European coastal seas. Interaction of the powerful tidal stream with strong wind, high waves and irregular bathymetry creates hydrodynamic conditions of extreme complexity, with high levels of turbulence. A comprehensive dataset has been created to improve the understanding of physical processes, turbulence, tidal stream and resource variability at the site. The database contains a large amount of oceanographic and meteorological measurements acquired in Alderney Race in 2017-2018. This exceptionally long period of observations (nearly one year) became possible due to modern tools and strategies of data acquisition. The paper presents some significant results from the database analysis. Among many results, we would like to underline the following (i) a wide range of variability of mean flow and sea state parameters was documented; (ii) exceptionally large values of current velocity (7 m s-1) and significant wave height (8 m) were measured during extreme meteorological conditions; (iii) high-frequency variability of current speed during storm events was also found to be very large, with the standard deviation of velocity reaching 0.3 m s-1 in the bottom boundary layer, and 0.6 m s-1 in the surface layer; and (iv) predominant wind and wave direction relative to the flow impacts the wave height and significantly increases the turbulence kinetic energy of the flow. To our knowledge, this is the largest multi-variable database available on potential tidal energy sites. The results of database analysis can represent a significant advance in environmental conditions and resource characterization and provide advanced information to turbine developers. This article is part of the theme issue 'New insights on tidal dynamics and tidal energy harvesting in the Alderney Race’.This research provides an updated energy yield assessment for a large tidal stream turbine array in the Alderney Race. The original array energy yield estimate was presented in 2004. Enhancements to this original work are made through the use of a validated two-dimensional hydrodynamic model, enabling the resolution of flow modelling to be improved and the impacts of array blockage to be quantified. Results show that a range of turbine designs (i.e. rotor diameter and power capacity) are needed for large-scale development, given the spatial variation in bathymetry and flow across the Alderney Race. Array blockage causes a reduction in flow speeds in the array of up to 2.5 m s-1, increased flow speeds around the array of up to 1 m s-1 and a reduction in the mean volume flux through the Alderney Race of 8%. The annual energy yield estimate of the array is 3.18 TWh, equivalent to the electricity demand of around 1 million homes. The capacity factor of the array is 18%, implying sub-optimal array design. This result demonstrates the need for turbine rated speed to be selected based on the altered flow regime, not the ambient flow. Further enhancement to array performance is explored through increases to rotor diameter and changes to device micro-siting, demonstrating the significant potential for array performance improvement. This article is part of the theme issue 'New insights on tidal dynamics and tidal energy harvesting in the Alderney Race’.Group-living organisms that collectively migrate range from cells and bacteria to human crowds, and include swarms of insects, schools of fish, and flocks of birds or ungulates. Unveiling the behavioural and cognitive mechanisms by which these groups coordinate their movements is a challenging task. These mechanisms take place at the individual scale and can be described as a combination of interactions between individuals and interactions between these individuals and the physical obstacles in the environment. Thanks to the development of novel tracking techniques that provide large and accurate datasets, the main characteristics of individual and collective behavioural patterns can be quantified with an unprecedented level of precision. However, in a large number of studies, social interactions are usually described by force map methods that only have a limited capacity of explanation and prediction, being rarely suitable for a direct implementation in a concise and explicit mathematical model. Here, we present a general method to extract the interactions between individuals that are involved in the coordination of collective movements in groups of organisms. We then apply this method to characterize social interactions in two species of shoaling fish, the rummy-nose tetra (Hemigrammus rhodostomus) and the zebrafish (Danio rerio), which both present a burst-and-coast motion. From the detailed quantitative description of individual-level interactions, it is thus possible to develop a quantitative model of the emergent dynamics observed at the group level, whose predictions can be checked against experimental results. This method can be applied to a wide range of biological and social systems. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems’.Cells of epithelial tissue proliferate and pack together to attain an eventual density homeostasis. As the cell density increases, spatial distribution of velocity and force show striking similarity to the dynamic heterogeneity observed elsewhere in dense granular matter. While the physical nature of this heterogeneity is somewhat known in the epithelial cell monolayer, its biological relevance and precise connection to cell density remain elusive. Relevantly, we had demonstrated how large-scale dynamic heterogeneity in the monolayer stress field in the bulk could critically influence the emergence of leader cells at the wound margin during wound closure, but did not connect the observation to the corresponding cell density. In fact, numerous previous reports had essentially associated long-range force and velocity correlation with either cell density or dynamic heterogeneity, without any generalization. Here, we attempted to unify these two parameters under a single framework and explored their consequence on the dynamics of leader cells, which eventually affected the efficacy of collective migration and wound closure. To this end, we first quantified the dynamic heterogeneity by the peak height of four-point susceptibility. Remarkably, this quantity showed a linear relationship with cell density over many experimental samples. We then varied the heterogeneity, by changing cell density, and found this change altered the number of leader cells at the wound margin. At low heterogeneity, wound closure was slower, with decreased persistence, reduced coordination and disruptive leader-follower interactions. Finally, microscopic characterization of cell-substrate adhesions illustrated how heterogeneity influenced orientations of focal adhesions, affecting coordinated cell movements. Together, these results demonstrate the importance of dynamic heterogeneity in epithelial wound healing. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems’.In animal groups, individual decisions are best characterized by probabilistic rules. Furthermore, animals of many species live in small groups. Probabilistic interactions among small numbers of individuals lead to a so-called intrinsic noise at the group level. Theory predicts that the strength of intrinsic noise is not a constant but often depends on the collective state of the group; hence, it is also called a state-dependent noise or a multiplicative noise. Surprisingly, such noise may produce collective order. However, only a few empirical studies on collective behaviour have paid attention to such effects owing to the lack of methods that enable us to connect data with theory. Here, we demonstrate a method to characterize the role of stochasticity directly from high-resolution time-series data of collective dynamics. We do this by employing two well-studied individual-based toy models of collective behaviour. We argue that the group-level noise may encode important information about the underlying processes at the individual scale. In summary, we describe a method that enables us to establish connections between empirical data of animal (or cellular) collectives and the phenomenon of noise-induced states, a field that is otherwise largely limited to the theoretical literature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems’.Glioblastoma multiforme (GBM) is the most aggressive form of brain cancer with a short median survival time. GBM is characterized by the hallmarks of aggressive proliferation and cellular infiltration of normal brain tissue. miR-451 and its downstream molecules are known to play a pivotal role in regulation of the balance of proliferation and aggressive invasion in response to metabolic stress in the tumour microenvironment (TME). Surgery-induced transition in reactive astrocyte populations can play a significant role in tumour dynamics. In this work, we develop a multi-scale mathematical model of miR-451-LKB1-AMPK-OCT1-mTOR pathway signalling and individual cell dynamics of the tumour and reactive astrocytes after surgery. We show how the effects of fluctuating glucose on tumour cells need to be reprogrammed by taking into account the recent history of glucose variations and an AMPK/miR-451 reciprocal feedback loop. The model shows how variations in glucose availability significantly affect the activity of signalling molecules and, in turn, lead to critical cell migration. The model also predicts that microsurgery of a primary tumour induces phenotypical changes in reactive astrocytes and stem cell-like astrocytes promoting tumour cell proliferation and migration by Cxcl5. Finally, we investigated a new anti-tumour strategy by Cxcl5-targeting drugs. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems’.While only a single sperm may fertilize the egg, getting to the egg can be facilitated, and possibly enhanced, by sperm group dynamics. Examples range from the trains formed by wood mouse sperm to the bundles exhibited by echidna sperm. In addition, observations of wave-like patterns exhibited by ram semen are used to score prospective sample fertility for artificial insemination in agriculture. In this review, we discuss these experimental observations of collective dynamics, as well as describe recent mechanistic models that link the motion of individual sperm cells and their flagella to observed collective dynamics. Establishing this link in models involves negotiating the disparate time- and length scales involved, typically separated by a factor of 1000, to capture the dynamics at the greatest length scales affected by mechanisms at the shortest time scales. Finally, we provide some outlook on the subject, in particular, the open questions regarding how collective dynamics impacts fertility. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems’.