Journal of The Royal Society Interface最新文献

The contact network structure resulting from social interaction between people is a key aspect of epidemic dynamics and control. While many studies have measured first-order network characteristics such as degree, measuring higher order properties of these networks, such as clustering, remains a challenge. Here, we present the results of a study of first- and second-order network structure from a representative cohort of individuals in Guangdong province, China. The number of reported daily contacts is similar across individuals aged 2 to 55 years, except for young adults (ages 16-25) who have relatively fewer daily contacts, while the number of contacts declines with age above 55 years old. The association between age and contact rate persisted after adjusting for mediating factors. Individuals living in higher population density areas made more contacts outside the home than individuals in low-density areas. Contacts of young children and older adults were more locally clustered than middle-aged adults. Individuals living in high population density areas had lower levels of local clustering compared with individuals from low-density areas. Adjustment for characteristics of the contacts themselves reduces the variation in local clustering between participants of different ages; however, the strong association with population density remains.

The biomechanical behaviour of vascular tissues is influenced by the presence of residual stresses, yet their role in vascular adaptation to pathological conditions remains largely unexplored. These residual stresses may arise within the vessel wall as a result of growth and remodelling (G&R) processes governed by the principles of tensional homeostasis. This study extends our previous work by refining a computational workflow that integrates homeostasis-driven G&R into patient-specific carotid geometries. Key advancements include adopting a total Lagrangian framework to handle complex geometries, introducing novel post-processing metrics for improved comparisons and conducting statistical analyses to assess G&R's impact on biomechanical evaluations of atherosclerotic vessels. These improvements enabled the analysis of a cohort of 18 cases, incorporating patient-specific geometries and pathological tissue distributions reconstructed from clinical imaging data. Results suggest that G&R generally reduces peak stress, though its effectiveness depends on plaque morphology and tissue composition. High calcification leads to localized stress concentrations, limiting remodelling, whereas matrix-rich regions promote stress homogenization. At the cohort level, findings underscore the need for patient-specific analyses in plaque risk evaluation, reinforcing the importance of personalized biomechanical modelling in assessing atherosclerotic disease and guiding clinical decision-making.

Human cooperation arises naturally and is essential for the development of successful societies. This study aims to identify which aspects of the interaction influence societal cooperation and defection. Specifically, we investigate human cooperation within the framework of the Multiplayer Iterated Prisoner's Dilemma game, modelling the decision-making process by using the drift-diffusion model (DDM). We propose a novel Bayesian model for the evolution of the DDM parameters, based on the nature of interactions experienced with other players. This approach enables us to predict the evolution of the expected rate of cooperation within the population. We successfully validate our model using an unseen test dataset-separated from the training one-and apply it to explore three strategic scenarios known from previous research to affect cooperation: (i) manipulation of co-players, (ii) the use of rewards and punishments, and (iii) time pressure. Our model successfully explains the test dataset and behaves consistently with established findings in the literature on human behaviour in these simulated scenarios. These results support the potential of our model as a foundational tool for developing and testing strategies that foster cooperation, improving our ability to study, understand and intervene in scenarios where individual and collective interests conflict.

Cell competition is a fitness control mechanism in tissues, where less fit cells are eliminated to maintain tissue homeostasis. Two primary mechanisms of cell competition have been identified: contact-dependent apoptosis and mechanical stress-induced competition. While both operate in tissues, their combined impact on population dynamics is unclear. Here, we present a cell-based computational model that integrates cellular mechanics with proliferation, contact-induced apoptosis and mechanically triggered apoptosis to investigate competition between two distinct cell types. Using this framework, we systematically examine how differences in physical traits-such as stiffness, adhesion and crowding sensitivity-govern competitive outcomes. Our results show that apoptosis rates alone are insufficient to predict cell fate; differences in proliferation and contact inhibition play equally important, context-dependent roles. Notably, we find that increased cell stiffness can confer a fitness advantage, enabling stiffer cells to outcompete softer neighbours. However, cells with reduced stiffness can become 'soft' supercompetitors if they exhibit faster growth and lower sensitivity to crowding. We also demonstrate that colony size critically influences competition: a minimum size is required for mutant expansion, below which elimination becomes stochastic. This stochastic clearance is driven by a protrusive instability in the interface between two cells that promotes invasion of the supercompetitors.

Termite mounds are known for their ability to maintain self-sustained ventilation and thermoregulation irrespective of external climatic conditions. Although there has been extensive interest in this topic, especially for designing energy-efficient buildings, it is still not fully understood how mound properties are controlled. This article reviews established knowledge and identifies gaps in the study of climate control within termite mounds, proposing an interdisciplinary approach that combines X-ray tomography and flow field simulations. Through specific examples, we demonstrate how these methods can deepen our understanding of termite mound structure and its climate-regulating functions.

Integrating factors affecting the success of invasive insect pests into dynamical models can help assessing their invasion risks and control. Here, we model the spread of a gall-forming hymenopteran parasite of chestnut trees, Dryocosmus kuriphilus, and its control agent, Torymus sinensis, across 23 natural forest sites located in the French Eastern Pyrenees. The integration of field estimates of the levels of bottom-up (frequency, density and genetic susceptibility of chestnut trees) and top-down (hyperparasitism by native insects and fungi) control of the pest in a Nicholson-Bailey model allowed to identify source and sink sites for the invasive species and its control agent. Comparisons with the observed levels of hyperparasitism by T. sinensis showed that it was found in 7/23 sink sites. The extension of our modelling into a two-site model showed that dispersal rates as low as 1‰ can be responsible for the persistence of T. sinensis in sinks, regardless of the precise dynamical regime of D. kuriphilus-T. sinensis coexistence in the source. Although dispersal promotes the persistence of the control agent and tends to homogenize its effectiveness in both sites, it was also shown to reduce the global biological control effectiveness at high rates of coupling.

By linking genetic sequences to phenotypic traits, genotype-phenotype maps represent a key layer in biological organization. Their structure modulates the effects of genetic mutations which can contribute to shaping evolutionary outcomes. Recent work based on algorithmic information theory introduced an upper bound on the likelihood of a random genetic mutation causing a transition between two phenotypes, using only the conditional complexity between them. Here we evaluate how well this bound works for a range of genotype-phenotype maps, including a differential equation model for circadian rhythm, a matrix-multiplication model of gene regulatory networks, a developmental model of tooth morphologies for ringed seals, a polyomino-tile shape model of biological self-assembly, and the hydrophobic/polar (HP) lattice protein model. By assessing three levels of predictive performance, we find that the bound provides meaningful estimates of phenotype transition probabilities across these complex systems. These results suggest that transition probabilities can be predicted to some degree directly from the phenotypes themselves, without needing detailed knowledge of the underlying genotype-phenotype map.

Living systems have evolved cognitive complexity to reduce environmental uncertainty, enabling them to predict and prepare for future conditions. Anticipation, distinct from simple prediction, involves active adaptation before an event occurs and is a key feature of both neural and aneural biological agents. Building on the moving average convergence-divergence principle from financial trend analysis, we propose an implementation of anticipation through synthetic biology by designing and evaluating experimentally testable minimal genetic circuits capable of anticipating environmental trends. Through deterministic and stochastic analyses, we demonstrate that these motifs achieve robust anticipatory responses under a wide range of conditions. Our findings suggest that simple genetic circuits could be naturally exploited by cells to prepare for future events, providing a foundation for engineering predictive biological systems.

Fractures of the distal limb in Thoroughbred racehorses primarily occur because of accumulation of bone microdamage from high-intensity training. Mathematical models of subchondral bone adaptation of the third metacarpal lateral condyles are capable of approximating existing data for Thoroughbred racehorses in training or at rest. To improve upon previous models, we added a dynamic resorption rate and microdamage accumulation and repair processes. Our ordinary differential equation model simulates the coupled processes of bone adaptation and microdamage accumulation, and is calibrated to data on racehorses in training and rest. Sensitivity analyses of our model suggest that joint loads and distances covered per day are among the most significant parameters for predicting microdamage accumulated during training. We also use the model to compare the impact of incrementally increasing training programmes as horses enter training from a period of rest, and maintenance workloads of horses that are race fit on bone adaptation. We find that high-speed training accounts for the majority of damage to the bone. Furthermore, for horses in race training, the estimated rates of bone repair are unable to offset the rate of damage accumulation under a typical Australian racing campaign, highlighting the need for regular rest from training.

The Beckman Institute for Advanced Science of Technology at the University of Illinois Urbana-Champaign was established in 1989 with the generous support of the Arnold and Mabel Beckman Foundation. It was built to break through disciplinary boundaries and produce scientific discoveries that could only be made by teams using interdisciplinary approaches. After 36 years, I reflect on the transformative legacy of the Beckman Institute at Illinois and how it informs my perspective on future of interdisciplinary research.