Journal of The Royal Society Interface最新文献

Integral controller is widely used in industry for its capability of endowing perfect adaptation to disturbances. To harness such capability for precise gene expression regulation, synthetic biologists have endeavoured in building biomolecular (quasi-)integral controllers, such as the antithetic integral controller. Despite demonstrated successes, challenges remain with designing the controller for improved transient dynamics and adaptation. Here, we explore and investigate the design principles of alternative RNA-based biological controllers, by modifying an antithetic integral controller with prevalently found natural feed-forward loops (FFL), to improve its transient dynamics and adaptation performance. With model-based analysis, we demonstrate that while the base antithetic controller shows excellent responsiveness and adaptation to system disturbances, incorporating the type-1 incoherent FFL into the base antithetic controller could attenuate the transient dynamics caused by changes in the stimuli, especially in mitigating the undesired overshoot in the output gene expression. Further analysis on the kinetic parameters reveals similar findings to previous studies that the degradation and transcription rates of the circuit RNA species would dominate in shaping the performance of the controllers.

Following lower limb amputation residuum skin from the lower leg is used to reconstruct the residual limb. Unlike skin on the sole of the foot (plantar skin), leg skin is not inherently load bearing. Despite this, leg skin is required to be load bearing in the prosthetic socket. Current hypotheses propose that lower limb amputee skin can adapt to become load bearing with repeated prosthesis use. Here, we show using confocal Raman spectroscopy, mechanical characterization and cytokine analysis that adaptations occur which actually result in impaired barrier function, higher baseline inflammation, increased coefficient of friction and reduced stiffness. Our results demonstrate that repeated frictional trauma does not confer beneficial adaptations in amputee skin. We hypothesize that non-plantar skin lacks the biological capabilities to respond positively to repeated mechanical trauma in the same manner observed in plantar skin. This finding highlights the need for improved therapies as opposed to current mechanical conditioning or product solutions that directly relate to improving load-bearing capacity on the skin of lower limb amputees. This study also highlights the importance of measuring multiple parameters of application-specific skin at different scales for skin tribology applications.

The current situation of COVID-19 measures makes it difficult to accurately assess the prevalence of SARS-CoV-2 due to a decrease in reporting rates, leading to missed initial transmission events and subsequent outbreaks. There is growing recognition that wastewater virus data assist in estimating potential infections, including asymptomatic and unreported infections. Understanding the COVID-19 situation hidden behind the reported cases is critical for decision-making when choosing appropriate social intervention measures. However, current models implicitly assume homogeneity in human behaviour, such as virus shedding patterns within the population, making it challenging to predict the emergence of new variants due to variant-specific transmission or shedding parameters. This can result in predictions with considerable uncertainty. In this study, we established a state-space model based on wastewater viral load to predict both reported cases and potential infection numbers. Our model using wastewater virus data showed high goodness-of-fit to COVID-19 case numbers despite the dataset including waves of two distinct variants. Furthermore, the model successfully provided estimates of potential infection, reflecting the superspreading nature of SARS-CoV-2 transmission. This study supports the notion that wastewater surveillance and state-space modelling have the potential to effectively predict both reported cases and potential infections.

Plants often respond to drier climates by slow evolutionary adaptations from fast-growing to stress-tolerant species. These evolutionary adaptations increase the plants' resilience to droughts but involve productivity losses that bear on agriculture and food security. Plants also respond by spatial self-organization, through fast vegetation patterning involving differential plant mortality and increased water availability to the surviving plants. The manners in which these two response forms intermingle and affect productivity and resilience have not been studied. Here we ask: can spatial patterning inhibit undesired evolutionary adaptation without compromising ecosystem resilience? To address this question, we integrate adaptive dynamics and vegetation pattern-formation theories and show that vegetation patterning can inhibit evolutionary adaptations to less productive, more stress-tolerant species over a wide precipitation range while increasing their resilience to water stress. This evolutionary homeostasis results from the high spatial plasticity of vegetation patterns, associated with patch thinning and patch dilution, which maintains steady local water availability despite decreasing precipitation. Spatial heterogeneity expedites the onset of vegetation patterning and induces evolutionary homeostasis at an earlier stage of evolutionary adaptation, thereby mitigating the productivity loss that occurs while the vegetation remains spatially uniform. We conclude by discussing our results in a broader context of evolutionary retardation.

An increasing number of evolutionary studies seek to quantify the morphological complexity of organisms, particularly those comprising serially homologous elements at different hierarchical levels of organization. Numerous operational frameworks have been proposed for doing this, but most focus on one or multiple conflated aspects of what is really a multidimensional concept. Here, we advocate the use of 'complexity spaces': multidimensional spaces defined by different vectors of complexity. We explore their application to biological systems composed of homologous parts and identify three axes on which those systems differ: part number, part differentiation and the regularity of that differentiation. Such complexity spaces can be constructed for systems at different hierarchical levels of biological organization. To illustrate this, we explore the complexity spaces for trilobite body plans (comprising body segments of varying number and form), and for ant colonies (comprising differentiated worker polymorphisms of varying number and form within a 'superorganism'). Many different complexity spaces are possible, but all seek to distinguish different aspects of complexity within an information-theoretic framework, and thereby to clarify patterns of complexity evolution.

The interaction of infectious diseases and behavioural responses to them has been the subject of widespread study. However, limited attention has been given to how broader social context shapes behavioural response. In this work, we propose a novel framework which combines two well-studied dynamic processes into a 'social risk appraisal' mechanism. Our proposed framework has both theoretical and empirical support, occupying an important middle ground in the interacting contagions literature. Results indicate that a risk appraisal framework can express a wide range of epidemic outcomes, driven by simple interaction rules. This framework has implications for designing containment strategies in disease outbreaks, as well as equity considerations. Finally, the risk appraisal approach is well-posed to engage with a broad set of literature in epidemic management, decision-making and the adoption of social behaviours.

Influenza forecasts could aid public health response as shown for temperate regions, but such efforts are more challenging in the tropics and subtropics due to more irregular influenza activities. Here, we built six forecast approaches for influenza in the (sub)tropics, with six model forms designed to model seasonal infection risk (i.e. seasonality) based on the dependence of virus survival on climate conditions and to flexibly account for immunity waning. We ran the models jointly with the ensemble adjustment Kalman filter to generate retrospective forecasts of influenza incidence in subtropical Hong Kong from January 1999 to December 2019 including the 2009 A(H1N1)pdm09 pandemic. In addition to short-term targets (one to four weeks ahead predictions), we also tested mid-range (one to three months) and long-range (four to six months) forecasts, which could be valuable for long-term planning. The largest improvement came from the inclusion of climate-modulated seasonality modelling, particularly for the mid- and long-range forecasts. The best-performing approach included a seasonal-trend-based climate modulation and assumed mixed immunity waning; the forecast accuracies, including peak week and intensity, were comparable to that reported for temperate regions including the USA. These findings demonstrate that incorporating mechanisms of climate modulation on influenza transmission can substantially improve forecast performance in the (sub)tropics.

The positive impact of cooperative bots on cooperation within evolutionary game theory is well-documented. However, prior studies predominantly use discrete strategic frameworks with deterministic actions. This article explores continuous and mixed strategic approaches. Continuous strategies use intermediate probabilities for varying degrees of cooperation and focus on expected payoffs, while mixed strategies calculate immediate payoffs from actions taken within these probabilities. Using the prisoner's dilemma game, this study examines the effects of cooperative bots on human cooperation in both well-mixed and structured populations across these strategic approaches. Our findings reveal that cooperative bots significantly enhance cooperation in both population types under weak imitation scenarios, where players are less concerned with material gains. Conversely, under strong imitation scenarios, cooperative bots do not alter the defective equilibrium in well-mixed populations but have varied impacts in structured populations. Specifically, they disrupt cooperation under discrete and continuous strategies but facilitate it under mixed strategies. These results highlight the nuanced effects of cooperative bots within different strategic frameworks and underscore the need for careful deployment, as their effectiveness is highly sensitive to how humans update their actions and their chosen strategic approach.

This article presents the first implementation of a proportional-integral-derivative (PID) biomolecular controller within a consortium of different cell populations, aimed at robust regulation of biological processes. By leveraging the modularity and cooperative dynamics of multiple engineered cell populations, we develop a comprehensive in silico analysis of the performance and robustness of P, PD, PI and PID control architectures. Our theoretical findings, validated through in silico experiments using the BSim agent-based simulation platform for bacterial populations, demonstrate the robustness and effectiveness of our multicellular PID control strategy. This innovative approach addresses critical limitations in current control methods, offering significant potential for applications in metabolic engineering, therapeutic contexts and industrial biotechnology. Future work will focus on experimental validation in vivo and further refinement of the control models.