Journal of Theoretical Biology最新文献

The human gut microbiota relies on complex carbohydrates (glycans) for energy and growth, primarily dietary fiber and host-derived mucins. We introduce a mathematical model of a glycan generalist and a mucin specialist in a two-compartment chemostat model of the human colon. Our objective is to characterize the influence of dietary fiber and mucin supply on the abundance of mucin-degrading species within the gut ecosystem. Current mathematical gut reactor models that include the enzymatic degradation of glycans do not differentiate between glycan types and their degraders. The model we present distinguishes between a generalist that can degrade both dietary fiber and mucin, and a specialist species that can only degrade mucin. The integrity of the colonic mucus barrier is essential for overall human health and well-being, with the mucin specialist Akkermanisa muciniphila being associated with a healthy mucus layer. Competition, particularly between the specialist and generalists like Bacteroides thetaiotaomicron, may lead to mucus layer erosion, especially during periods of dietary fiber deprivation. Our model treats the colon as a gut reactor system, dividing it into two compartments that represent the lumen and the mucus of the gut, resulting in a complex system of ordinary differential equations with a large and uncertain parameter space. To understand the influence of model parameters on long-term behavior, we employ a random forest classifier, a supervised machine learning method. Additionally, a variance-based sensitivity analysis is utilized to determine the sensitivity of steady-state values to changes in model parameter inputs. By constructing this model, we can investigate the underlying mechanisms that control gut microbiota composition and function, free from confounding factors.

This paper introduces a new model to simulate the progression of senile plaques, focusing on scenarios where concentrations of amyloid beta (Aβ) monomers and aggregates vary between neurons. Extracellular variations in these concentrations may arise due to limited diffusivity of Aβ monomers and a high rate of Aβ monomer production at lipid membranes, requiring a substantial concentration gradient for diffusion-driven transport of Aβ monomers. The dimensionless formulation of the model is presented, which identifies four key dimensionless parameters governing the solutions for Aβ monomer and aggregate concentrations, as well as the radius of a growing Aβ plaque within the control volume. These parameters include the dimensionless diffusivity of Aβ monomers, the dimensionless rate of Aβ monomer production, and the dimensionless half-lives of Aβ monomers and aggregates. A dimensionless parameter is then introduced to evaluate the validity of the lumped capacitance approximation. An approximate solution is derived for the scenario involving large diffusivity of Aβ monomers and dysfunctional protein degradation machinery, resulting in infinitely long half-lives for Aβ monomers and aggregates. In this scenario, the concentrations of Aβ aggregates and the radius of the Aβ plaque depend solely on a single dimensionless parameter that characterizes the rate of Aβ monomer production. According to the approximate solution, the concentration of Aβ aggregates is linearly dependent on the rate of monomer production, and the radius of an Aβ plaque is directly proportional to the cube root of the rate of monomer production. However, when departing from the conditions of the approximate solution (e.g., finite half-lives), the concentrations of Aβ monomers and aggregates, along with the plaque radius, exhibit complex dependencies on all four dimensionless parameters. For instance, under physiological half-life conditions, the plaque radius reaches a maximum value and stabilizes thereafter.

Chemotaxis, cell migration in response to chemical gradients, is known to promote self-organization of microbiological populations. However, the modeling of chemotaxis in heterogeneous environments is still limited. This study analyzes a decentralized gathering process in environments with physical as well as chemical barriers, using a multi-agent model for Disctyostelium discoideum colonies. Employing a topology-independent metric to quantify the system evolution, we study dynamical features emerging from complex social interactions. The results show that obstacles may hamper the gathering process by altering the flux of chemical signals among amoebas, acting as local topological perturbations. We also find that a minimal set of agent’s rules for robust gathering does not require explicit mechanisms for obstacle sensing and avoidance; moreover, random cell movements concur in preventing multiple stable clusters and improve the gathering efficacy. Hence, we speculate that chemotactic cells can avoid obstacles without needing specialized mechanisms: tradeoffs of social interactions and individual fluctuations are sufficient to guarantee the aggregation of the whole colony past numerous obstacles.

The recent global COVID-19 pandemic resulted in governments enacting non-pharmaceutical interventions (NPIs) targeted at reducing transmission of SARS-CoV-2. But the NPIs also affected the transmission of viruses causing non-target seasonal respiratory diseases, including influenza and respiratory syncytial virus (RSV). In many countries, the NPIs were found to reduce cases of such seasonal respiratory diseases, but there is also evidence that subsequent relaxation of NPIs led to outbreaks of these diseases that were larger than pre-pandemic ones, due to the accumulation of susceptible individuals prior to relaxation. Therefore, the net long-term effects of NPIs on the total disease burden of non-target diseases remain unclear. Knowledge of this is important for infectious disease management and maintenance of public health. In this study, we shed light on this issue for the simplified scenario of a set of NPIs that prevent or reduce transmission of a seasonal respiratory disease for about a year and are then removed, using mathematical analyses and numerical simulations of a suite of four epidemiological models with varying complexity and generality. The model parameters were estimated using empirical data pertaining to seasonal respiratory diseases and covered a wide range. Our results showed that NPIs reduced the total disease burden of a non-target seasonal respiratory disease in the long-term. Expressed as a percentage of population size, the reduction was greater for larger values of the basic reproduction number and the immunity loss rate, reflecting larger outbreaks and hence more infections averted by imposition of NPIs. Our study provides a foundation for exploring the effects of NPIs on total disease burden in more-complex scenarios.

Obesity and diabetes are a progressively more and more deleterious hallmark of modern, well fed societies. In order to study the potential impact of strategies designed to obviate the pathological consequences of detrimental lifestyles, a model for the development of Type 2 diabetes geared towards large population simulations would be useful. The present work introduces such a model, representing in simplified fashion the interplay between average glycemia, average insulinemia and functional beta-cell mass, and incorporating the effects of excess food intake or, conversely, of physical activity levels. Qualitative properties of the model are formally established and simulations are shown as examples of its use.

Adaptive radiation is a major source of biodiversity but the way in which known components of ecological opportunity, ecological differentiation, and reproductive isolation underpin such biodiversity patterns remains elusive. Much is known about the evolution of ecological differentiation and reproductive isolation during single speciation events, but exactly how those processes scale up to complete adaptive radiations is less understood. Do we expect complete reproductive barriers between newly formed species before the ecological differentiation continues, or does proper species formation occur much later, long after the ecological diversification? Our goal is to improve our mechanistic understanding of adaptive radiations by analyzing an individual-based model that includes a suite of mechanisms that are known to contribute to biodiversity. The model includes variable biogeographic settings, ecological opportunities, and types of mate choice, which makes several different scenarios of an adaptive radiation possible. We find that evolving clades tend to exploit ecological opportunities early whereas reproductive barriers evolve later, demonstrating a decoupling of ecological differentiation and species formation. In many cases, we also find a long-term trend where assortative mating associated with ecological traits is replaced by sexual selection of neutral display traits as the primary mechanism for reproductive isolation. Our results propose that reticulate phylogenies are likely common and stem from initially low reproductive barriers, rather than the previously suggested idea of repeated hybridization events between well-separated species.

Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respond to ICT. To predict ICT response, we developed a multi-modal feature fusion model based on deep learning (MFMDL). This model utilizes graph neural networks to map gene-gene relationships in gene networks to low dimensional vector spaces, and then fuses biological pathway features and immune cell infiltration features to make robust predictions of ICT. We used five datasets to validate the predictive performance of the MFMDL. These five datasets span multiple types of cancer, including melanoma, lung cancer, and gastric cancer. We found that the prediction performance of multi-modal feature fusion model based on deep learning is superior to other traditional ICT biomarkers, such as ICT targets or tumor microenvironment-associated markers. In addition, we also conducted ablation experiments to demonstrate the necessity of fusing different modal features, which can improve the prediction accuracy of the model.

Cancer therapy often leads to the selective elimination of drug-sensitive cells from the tumour. This can favour the growth of cells resistant to the therapeutic agent, ultimately causing a tumour relapse. Castration-resistant prostate cancer (CRPC) is a well-characterised instance of this phenomenon. In CRPC, after systemic androgen deprivation therapy (ADT), a subset of drug-resistant cancer cells autonomously produce testosterone, thus enabling tumour regrowth. A previous theoretical study has shown that such a tumour relapse can be delayed by inhibiting the growth of drug-resistant cells using biotic competition from drug-sensitive cells. In this context, the centrality of resource dynamics to intra-tumour competition in the CRPC system indicates clear scope for the construction of theoretical models that can explicitly incorporate the underlying mechanisms of tumour ecology. In the current study, we use a modified logistic framework to model cell–cell interactions in terms of the production and consumption of resources. Our results show that steady state composition of CRPC can be understood as a composite function of the availability and utilisation efficiency of two resources-oxygen and testosterone. In particular, we show that the effect of changing resource availability or use efficiency is conditioned by their general abundance regimes. Testosterone typically functions in trace amounts and thus affects steady state behaviour of the CRPC system differently from oxygen, which is usually available at higher levels. Our data thus indicate that explicit consideration of resource dynamics can produce novel and useful mechanistic understanding of CRPC. Furthermore, such a modelling approach also incorporates variables into the system’s description that can be directly measured in a clinical context. This is therefore a promising avenue of research in cancer ecology that could lead to therapeutic approaches that are more clearly rooted in the biology of CRPC.

Lymphatic filariasis (LF) has been targeted for elimination as a public health concern by 2030 with a goal to keep the prevalence of LF infections under the 1% threshold. While mass drug administration (MDA) is a primary strategy recommended by WHO, the use of insecticide treated nets (ITN) plays a crucial role as an alternative strategy when MDA cannot be used. In this paper, we use imitation dynamics to incorporate human behavior and voluntary use of ITN into the compartmental epidemiological model of LF transmission. We find the equilibrium states of the dynamics and the ITN usage as it depends on epidemiological parameters and the cost of ITNs. We investigate the conditions under which the voluntary use of ITNs can keep the LF prevalence under the 1% threshold. We found that when the cost of using the ITNs is about $10^5$ smaller than the perceived cost of LF, then the voluntary use of ITNs will eliminate LF as a public health concern. Furthermore, when the ITNs are given away for free, our model predicts that over 80% of the population will use them which would eliminate LF completely in regions where Anopheles are the primary vectors.

The success of individual species under a change to the environment is dependent on a number of factors, which include the changes to habitat, competition with other species and adaptability. Here we investigate the impact of differing dispersal characteristics of two competing species responding to the change using an idealized spatio-temporal model. The rate of dispersion is given by a combination of the growth term and the form of the diffusion term, which is set to give either normal diffusion or anomalous (super) diffusion. The later is brought about by employing fractional diffusion and we characterize the population as being more adventurous than the population undergoing normal diffusion. The more adventurous population is found, not surprisingly, to reach and occupy uninhabited ground before the population undergoing normal diffusion can get there. Interbreeding is found to be important in that it can aid the spread of the less adventurous population preventing its extinction. The response to an abrupt environment change, taken here to be a change in the distribution of the growth rate, is dependent on the initial conditions, the dispersion characteristics, and the level of interbreeding, leading to very different intermediate and final states. Our results highlight instances when a particular dispersal strategy gives a population an edge over another. In the cases considered here we find states where the more adventurous population can dominate across the domain, the two populations exist in separate parts of the domain separated by fronts, and both populations coexist across the domain in the medium term with one or other of the populations dominating across the domain in the long term. Given the long time to reach equilibrium where one or other of the populations dominate, consideration needs to be put to the time scale of change, as sufficiently frequent change can allow coexistence. We demonstrate the need to include dispersion characteristics when considering the factors affecting the response of species to a change in the environment.