Journal of Theoretical Biology最新文献

Ecosystems face various emergent uncertainties owing to factors such as climate change and accelerating anthropogenic impacts. Uncertainty is a major challenge and a barrier that ecosystem management faces, because it is difficult to precisely predict a priori risks that can have significant impacts on ecosystems. Hence, management with adaptive capacity is recommended to deal with such uncertainties, and feedback structures are central mechanisms for such flexible management. This study used mathematical models to clarify the specific impacts of feedback structures on ecosystem management, such as resource and wildlife management. In particular, the impact of errors in estimating ecosystem status when providing feedback and the impact of the time lag before feedback effects were implemented into management were examined. Overestimation of ecosystem status or a large time lag led to undesirable temporal oscillations in ecosystem status. However, these scenarios can be avoided when combined with management practices that limit the impact of management on the ecosystem, such as input control. Ecosystem management tends to have a large spatiotemporal scale, and implementing highly accurate monitoring and sophisticated feedback structures is difficult. However, the results suggest that effective ecosystem management with a simple feedback structure can be achieved through such complementary institutional design.

Candida Auris is an emerging fungal pathogen flagged by CDC as a serious global health threat among nosocomial infections in the recent times. As an evolving pathogen that often goes misidentified or unidentified under standard laboratory tests, it has the ability to cause fatal infections among the target population involving patients with serious medical conditions admitted to intensive care facilities, due to its capacity to resist anti-fungal treatment and the ability to persist in the hospital environment for long periods. The subject of this paper is to develop a deterministic model to study the transmission nature of Candida Auris wherein measures like apt admission screening methods with weekly screening follow-ups, transmission prevention, proper treatment protocols and environmental disinfection procedures are introduced as constant mitigating controls into the model initially which are later redefined as variable control functions during the optimal control analysis. The theory of optimal control implemented into the model helps us to understand the sensitivity of each control strategy upon the behaviour of each state variable. Further, cost-effectiveness analysis is rigorously conducted using incremental cost-effectiveness ratio (ICER) to identify and rank the control strategies involved based on their economic efficiency. Numerical simulation for the optimal control analysis is performed in MATLAB using the Forward–Backward Sweep Method and the findings are illustrated graphically.

What conditions select flowering patterns within inflorescences, or variation in the anthesis interval within inflorescences among plants? Under what conditions are gradual blooming and simultaneous blooming, both traits related to floral display size, advantageous? We constructed a simulation model in which the opening times and longevities of individual flowers within inflorescences, the sizes of attractive structures of individual flowers, and the numbers of ovules and pollen grains produced by individual flowers evolve. Individual plants in the population compete for pollinators, and plants are selected by pollinators according to their floral display sizes and amounts of resources allocated to attractive structures. We found that, if the proportion of pollen on a pollinator deposited on a stigma was low, gradual blooming did not evolve even if inbreeding depression was greater than 0.5. This is because the amount of outcross-pollen on pollinators decreased at a low rate during flower visits within a single inflorescence, and the selfing rate was suppressed to a low level even if the floral display size was large. On the other hand, if the proportion of pollen deposition was high, gradual blooming evolved even if inbreeding depression was smaller than 0.5. This may be because gradual blooming can enhance pollen delivery to other plants by reducing the loss of self-pollen by geitonogamy. On the other hand, allocation ratios among floral organs (female and male organs and attractive structures) were independent of the degree of simultaneous and gradual blooming within inflorescences. We concluded that the evolution of gradual blooming is more strongly affected by the proportion of pollen on a pollinator deposited on a stigma than by inbreeding depression.

Viral coinfections are responsible for a significant portion of cases of patients hospitalized with influenza-like illness. As our awareness of viral coinfections has increased, researchers have started to experimentally examine some of the virus–virus interactions underlying these infections. One mechanism of interaction between viruses is through the innate immune response. This seems to occur primarily through the interferon response, which generates an antiviral state in nearby uninfected cells, a phenomenon know as the bystander effect. Here, we develop a mathematical model of two viruses interacting through the bystander effect. We find that when the rate of removal of cells to the protected state is high, growth of the first virus is suppressed, while the second virus enjoys sole access to the protected cells, enhancing its growth. Conversely, growth of the second virus can be fully suppressed if its ability to infect the protected cells is limited.

Prothrombinase complex, composed of coagulation factors Xa (FXa) and Va (FVa) is a major enzyme of the blood coagulation network that produces thrombin via activation of its inactive precursor prothrombin (FII) on the surface of phospholipid membranes. However, pathways and mechanisms of prothrombinase formation and substrate delivery are still discussed. Here we designed a novel mathematical model that considered different potential pathways of FXa or FII binding (from the membrane or from solution) and analyzed the kinetics of thrombin formation in the presence of a wide range of reactants concentrations. We observed the inhibitory effect of large FVa concentrations and this effect was phospholipid concentration-dependent. We predicted that efficient FII activation occurred via formation of the ternary complex, in which FVa, FXa and FII were in the membrane-bound state. Prothrombin delivery was mostly membrane-dependent, but delivery from solution was predominant under conditions of phospholipid deficiency or FXa/FVa excess. Likewise, FXa delivery from solution was predominant in the case of FVa excess, but high FII did not switch the FXa delivery to the solution-dependent one. Additionally, the FXa delivery pathway did not depend on the phospholipid concentration, being the membrane-dependent one even in case of the phospholipid deficiency. These results suggest a flexible mechanism of prothrombinase functioning which utilizes different complex formation and even inhibitory mechanisms depending on conditions.

We investigate an efficient computational tool to suggest useful treatment regimens for people infected with the human immunodeficiency virus (HIV). Structured treatment interruption (STI) is a regimen in which therapeutic drugs are periodically administered and withdrawn to give patients relief from an arduous drug therapy. Numerous studies have been conducted to find better STI treatment strategies using various computational tools with mathematical models of HIV infection. In this paper, we leverage a modified version of the double deep Q network with prioritized experience replay to improve the performance of classic deep learning algorithms. Numerical simulation results show that our methodology produces significantly more optimal cost values for shorter treatment periods compared to other recent studies. Furthermore, our proposed algorithm performs well in one-day segment scenarios, whereas previous studies only reported results for five-day segment scenarios.

In recent years, multi-cellular models, where cells are represented as individual interacting entities, are becoming ever popular. This has led to a proliferation of novel methods and simulation tools. The first aim of this paper is to review the numerical methods utilised by multi-cellular modelling tools and to demonstrate which numerical methods are appropriate for simulations of tissue and organ development, maintenance, and disease. The second aim is to introduce an adaptive time-stepping algorithm and to demonstrate it’s efficiency and accuracy. We focus on off-lattice, mechanics based, models where cell movement is defined by a series of first order ordinary differential equations, derived by assuming over-damped motion and balancing forces. We see that many numerical methods have been used, ranging from simple Forward Euler approaches through to higher order single-step methods like Runge–Kutta 4 and multi-step methods like Adams–Bashforth 2. Through a series of exemplar multi-cellular simulations, we see that if: care is taken to have events (births deaths and re-meshing/re-arrangements) occur on common time-steps; and boundaries are imposed on all sub-steps of numerical methods or implemented using forces, then all numerical methods can converge with the correct order. We introduce an adaptive time-stepping method and demonstrate that the best compromise between $L_{\infty }$ error and run-time is to use Runge–Kutta 4 with an increased time-step and moderate adaptivity. We see that a judicious choice of numerical method can speed the simulation up by a factor of 10–60 from the Forward Euler methods seen in Osborne et al. (2017), and a further speed up by a factor of 4 can be achieved by using an adaptive time-step.

Mutualism is considered a major driver of biodiversity, as it enables extensive codiversification in terrestrial communities. An important case is flowering plants and their pollinators, where convergent selection on plant and pollinator traits is combined with divergent selection to minimize niche overlap within each group. In this article, we study the emergence of polymorphisms in communities structured trophically: plants are the primary producers of resources required by the primary consumers, the servicing pollinators. We model natural selection on traits affecting mutualism between plants and pollinators and competition within these two trophic levels. We show that phenotypic diversification is favored by broad plant niches, suggesting that bottom-up trophic control leads to codiversification. Mutualistic generalism, i.e., tolerance to differences in plant and pollinator traits, promotes a cascade of evolutionary branching favored by bottom-up plant competition dependent on similarity and top-down mutualistic services that broaden plant niches. Our results predict a strong positive correlation between the diversity of plant and pollinator phenotypes, which previous work has partially attributed to the trophic dependence of pollinators on plants.

Labor division is a phenomenon observed across various biological contexts, including examples such as the differentiation between germ/somatic cells in multicellular organisms and the division between reproductive/worker individuals within social animal groups. In such cases, certain members contribute to tasks that enhance the viability of the entire group, even if this requires a reduction in their individual reproductive efforts. Given that group members have the potential to adopt varying contribution levels, a comprehensive analysis of the evolution becomes intricate due to the problem’s high dimensionality. In this paper, I introduce a novel method for analyzing the evolution of the distribution of contribution levels to group viability, with a particular formulation centered on the success of clonal strains. The analysis demonstrates that the curvature of the fecundity function in relation to contributions to the group plays a pivotal role in determining the occurrence of labor division between reproductive and non-reproductive tasks, aligning in part with results from prior research. Furthermore, I extend this analysis to encompass contributions to multiple categories of tasks for group viability. My findings indicate that investments in non-reproductive tasks are selected based on the average contributions for each task, with individual variation playing a less significant role as long as average values remain consistent. Additionally, I explore the impact of group size and relatedness within the group on labor division. The results highlight that increases in group size and relatedness have a positive influence on the evolution of cooperation, although their effects are not directly tied to labor division itself.