Journal of Theoretical Biology最新文献

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.

Predictive models of signaling pathways have proven to be difficult to develop. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. To meet this need, we developed a method for generating artificial signaling networks that were reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. We defined the reaction degree and reaction distance to measure the topology of reaction networks, especially to consider enzymes. To determine whether our generated signaling networks displayed meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModels signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModels and thus could be useful for building network evaluation tools.

The CD8＋ T cell response is the main determinant of viral clearance during influenza infection. However, influenza viral dynamics and the respective immune responses are affected by the host’s age. To investigate age-related differences in the CD8＋ T cell immune response dynamics, we propose 16 ordinary differential equation models of existing experimental data. These data consist of viral titer and CD8＋ T cell counts collected periodically over a period of 19 days from adult and aged mice infected with influenza A/Puerto Rico/8/34 (H1N1). We use the corrected Akaike Information Criterion to identify the models which best represent the considered data. Our model selection process indicates differences in mechanisms which reduce the CD8＋ T cell response: linear downregulation is favored for adult mice, while baseline exponential decay is favored for aged mice. Parameter fitting of the top ranked models suggests that the aged population has reduced CD8＋ T cell proliferation compared to the adult population. More experimental work is needed to determine the specific immunological features through which age might cause these differences. A better understanding of the immunological mechanisms by which aging leads to discrepant CD8＋ T cell dynamics may inform future treatment strategies.

In this paper, we explore the effects of biological (pathological) and mechanical damage on bone tissue within a benchmark model. Using the Finite Element Methodology, we analyze and numerically test the model’s components, capabilities, and performance under physiologically and pathologically relevant conditions. Our findings demonstrate the model’s effectiveness in simulating bone remodeling processes and self-repair mechanisms for micro-damage induced by biological internal conditions and mechanical external ones within bone tissue. This article is the second part of a series, where the first part presented the mathematical model and the biological and physical significance of the terms used in a simplified benchmark model. It explored the bone remodeling model’s application, implementation, and results under physiological conditions.

The formation of amyloid beta (Aβ) deposits (senile plaques) is one of the hallmarks of Alzheimer’s disease (AD). This study investigates what processes are primarily responsible for their formation. A model is developed to simulate the diffusion of amyloid beta (Aβ) monomers, the production of free Aβ aggregates through nucleation and autocatalytic processes, and the deposition of these aggregates into senile plaques. The model suggests that efficient degradation of Aβ monomers alone may suffice to prevent the growth of senile plaques, even without degrading Aβ aggregates and existing plaques. This is because the degradation of Aβ monomers interrupts the supply of reactants needed for plaque formation. The impact of Aβ monomer diffusivity is demonstrated to be small, enabling the application of the lumped capacitance approximation and the derivation of approximate analytical solutions for limiting cases with both small and large rates of Aβ aggregate deposition into plaques. It is found that the rate of plaque growth is governed by two competing processes. One is the deposition rate of free Aβ aggregates into senile plaques. If this rate is small, the plaque grows slowly. However, if the rate of deposition of Aβ aggregates into senile plaques is very large, the free Aβ aggregates are removed from the intracellular fluid by deposition into the plaques, leaving insufficient free Aβ aggregates to catalyze the production of new aggregates. This suggests that under certain conditions, Aβ plaques may offer neuroprotection and impede their own growth. Additionally, it indicates that there exists an optimal rate of deposition of free Aβ aggregates into the plaques, at which the plaques attain their maximum size.

The aerial flocking of birds, or murmurations, has fascinated observers while presenting many challenges to behavioral study and simulation. We examine how the periphery of murmurations remain well bounded and cohesive. We also investigate agitation waves, which occur when a flock is disturbed, developing a plausible model for how they might emerge spontaneously. To understand these behaviors a new model is presented for orientation-based social flocking. Previous methods model inter-bird dynamics by considering the neighborhood around each bird, and introducing forces for avoidance, alignment, and cohesion as three dimensional vectors that alter acceleration. Our method introduces orientation-based social flocking that treats social influences from neighbors more realistically as a desire to turn, indirectly controlling the heading in an aerodynamic model. While our model can be applied to any flocking social bird we simulate flocks of starlings, Sturnus vulgaris, and demonstrate the possibility of orientation waves in the absence of predators. Our model exhibits spherical and ovoidal flock shapes matching observation. Comparisons of our model to Reynolds’ on energy consumption and frequency analysis demonstrates more realistic motions, significantly less energy use in turning, and a plausible mechanism for emergent orientation waves.

I present a method to allocate a given number of vaccines to members of a population who differ in their susceptibility to the disease so that the final size of the epidemic is minimised. I consider an arbitrary distribution of protection that the vaccine confers, including the extreme cases of leaky and all-or-none vaccines. The optimal vaccination policy depends on the distribution of protection. While for low values of the basic reproduction number $R_0$ the optimal policy prioritises the most susceptible hosts, I show that for almost any distribution the order of priority reverses and the least susceptible hosts should be vaccinated when $R_0$ is high. The exception where this does not happen is the all-or-none vaccine. However, even a small deviation from the ideal all-or-none distribution can imply that the limited number of vaccines should be given to less susceptible hosts already at realistic values of $R_0$.