Acta Biotheoretica最新文献

We extend the comparatively simple processes of group symmetry-breaking in physical systems to groupoid/equivalence class phase transitions characterizing adiabatically, piecewise stationary, information transmission in prebiotic, biological, and social phenomena: High vs. Low probability paths (rightarrow) Interior and Exterior Interact (rightarrow) Multiple Interacting Tunable Workspaces Application to nonstationary processes seems possible via generalizations of the symmetry algebra, for example, to semigroupoids. The dynamic probability models explored here can be transformed into statistical tools for the analysis of real-time and other data across a spectrum of important disciplines confronted by biological and other forms of cognition and their dysfunctions.

In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between computing units for any implementation. We need a different mathematical method to consider this limitation: classic mathematics can only describe infinitely fast and small computing system implementations. The difference between mathematical handling methods leads to different descriptions of the computing features of the systems. The proposed handling also explains why biological implementations can have lifelong learning and technological ones cannot. Our conclusion about learning matches published experimental evidence, both in biological and technological computing.

In this work, we study and analyze the aggregate death counts of COVID-19 reported by the United States Centers for Disease Control and Prevention (CDC) for the fifty states in the United States. To do this, we derive a stochastic model describing the cumulative number of deaths reported daily by CDC from the first time Covid-19 death is recorded to June 20, 2021 in the United States, and provide a forecast for the death cases. The stochastic model derived in this work performs better than existing deterministic logistic models because it is able to capture irregularities in the sample path of the aggregate death counts. The probability distribution of the aggregate death counts is derived, analyzed, and used to estimate the count’s per capita initial growth rate, carrying capacity, and the expected value for each given day as at the time this research is conducted. Using this distribution, we estimate the expected first passage time when the aggregate death count is slowing down. Our result shows that the expected aggregate death count is slowing down in all states as at the time this analysis is conducted (June 2021). A formula for predicting the end of Covid-19 deaths is derived. The daily expected death count for each states is plotted as a function of time. The probability density function for the current day, together with the forecast and its confidence interval for the next four days, and the root mean square error for our simulation results are estimated.

Causal pluralism can be defended not only in respect to causal concepts and methodological guidelines, but also at the finer-grained level of causal inference from a particular source of evidence for causation. An argument for this last variety of pluralism is made based on an analysis of causal inference from randomized experiments (RCTs). Here, the causal interpretation of a statistically significant association can be established via multiple paths of reasoning, each relying on different assumptions and providing distinct elements of information in favour of a causal interpretation.

Much of the current research in regenerative medicine concentrates on stem-cell therapy that exploits the regenerative capacities of stem cells when injected into different types of human tissues. Although new therapeutic paths have been opened up by induced pluripotent cells and human mesenchymal cells, the rate of success is still low and mainly due to the difficulties of managing cell proliferation and differentiation, giving rise to non-controlled stem cell differentiation that ultimately leads to cancer. Despite being still far from becoming a reality, these studies highlight the role of physical and biological constraints (e.g., cues and morphogenetic fields) placed by tissue microenvironment on stem cell fate. This asks for a clarification of the coupling of stem cells and microenvironmental factors in regenerative medicine. We argue that extracellular matrix and stem cells have a causal reciprocal and asymmetric relationship in that the 3D organization and composition of the extracellular matrix establish a spatial, temporal, and mechanical control over the fate of stem cells, which enable them to interact and control (as well as be controlled by) the cellular components and soluble factors of microenvironment. Such an account clarifies the notions of stemness and stem cell regeneration consistently with that of microenvironment.

The interplay between the virus, infected cells and immune responses to SARS-CoV-2 is still under debate. By extending the basic model of viral dynamics, we propose here a formal approach to describe neutralisation versus weak (or non-)neutralisation scenarios and compare them with the possible effects of antibody-dependent enhancement (ADE). The theoretical model is consistent with the data available in the literature; we show that both weakly neutralising antibodies and ADE can result in final viral clearance or disease progression, but that the immunodynamics are different in each case. As a significant proportion of the world’s population is already naturally immune or vaccinated, we also discuss the implications for secondary infections after vaccination or in the presence of immune system dysfunctions.

Here, an epidemiological model considering pro and anti-vaccination groups is proposed and analyzed. In this model, susceptible individuals can migrate between these two groups due to the influence of false and true news about safety and efficacy of vaccines. From this model, written as a set of three ordinary differential equations, analytical expressions for the disease-free steady state, the endemic steady state, and the basic reproduction number are derived. It is analytically shown that low vaccination rate and no influx to the pro-vaccination group have similar impacts on the long-term amount of infected individuals. Numerical simulations are performed with parameter values of the COVID-19 pandemic to illustrate the analytical results. The possible relevance of this work is discussed from a public health perspective.

Radopholus Similis (R. Similis) or burrowing nematode, is one of the most damaging and widespread nematodes attacking bananas, causing toppling or blackhead disease. A mathematical model for the population dynamics of R. Similis is considered, with the aim of investigating the impact of climatic factors on the growth of R. Similis. In this paper, based on the life cycle of R. Similis, we first propose a mathematical model to study and control the population dynamics of this banana pest. We show also how control terms based on biological and chemical controls can be integrated to reduce the population of R. Similis within banana-plantain roots. Sensitivity analysis was performed to show the most important parameters of the model. We present the theoretical analysis of the model. More precisely, we derive a threshold parameter ({mathcal{N}}_0), called the basic offspring number and show that the trivial equilibrium is globally asymptotically stable whenever ({mathcal{N}}_0le 1), while when ({mathcal{N}}_0> 1), the non trivial equilibrium is globally asymptotically stable. After, we extend the proposed model by taking account climatic factors that influence the growth of this pest. Biological and chemical controls are now introduced through impulsive equations. Threshold and equilibria are obtained and global stabilities have been studied. The theoretical results are supported by numerical simulations. Numerical results of model with biological and chemical controls reveal that biological methods are more effective than chemical methods. We also found that the month February is the best time to apply these controls.

Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.

Ecologists are challenged by the need to bridge and synthesize different approaches and theories to obtain a coherent understanding of ecosystems in a changing world. Both food web theory and regime shift theory shine light on mechanisms that confer stability to ecosystems, but from different angles. Empirical food web models are developed to analyze how equilibria in real multi-trophic ecosystems are shaped by species interactions, and often include linear functional response terms for simple estimation of interaction strengths from observations. Models of regime shifts focus on qualitative changes of equilibrium points in a slowly changing environment, and typically include non-linear functional response terms. Currently, it is unclear how the stability of an empirical food web model, expressed as the rate of system recovery after a small perturbation, relates to the vulnerability of the ecosystem to collapse. Here, we conduct structural sensitivity analyses of classical consumer-resource models in equilibrium along an environmental gradient. Specifically, we change non-proportional interaction terms into proportional ones, while maintaining the equilibrium biomass densities and material flux rates, to analyze how alternative model formulations shape the stability properties of the equilibria. The results reveal no consistent relationship between the stability of the original models and the proportionalized versions, even though they describe the same biomass values and material flows. We use these findings to critically discuss whether stability analysis of observed equilibria by empirical food web models can provide insight into regime shift dynamics, and highlight the challenge of bridging alternative modelling approaches in ecology and beyond.