Acta Biotheoretica最新文献

To bring clarity, the term ‘information’ is resolved into three distinct meanings: physical pattern, statistical relations and knowledge about things. In parallel, three kinds of ’causation’ are resolved: the action of physical force constrained by physical pattern (efficient cause), cybernetic (formal cause) and statistical inference. Cybernetic causation is an expression of fundamental (necessary) logical relations, statistical inference is phenomenological, but physical information and causation are proposed as what actually happens in the physical world. Examples of the latter are given to illustrate the underlying material dynamics in a range of biological systems from the appearance of ‘synergistic information’ among multiple variables (mainly in neuroscience); positional information in multicellular development; and the organisational structure of ecological communities, especially incorporating niche construction theory. A rigorous treatment of multi-level causation is provided as well as an explanation of the causal power of non-physical information structure, especially of interaction networks. The focus on physical information as particular pattern, echoing the insights of Howard Pattee, provides a more physically grounded view of emergence, downward causation and the concept of ‘closure to efficient causation’, all now prevalent in the organisational approach to biology.

Biologists are increasingly documenting anthropogenic disruptions, both at the organism and ecosystem levels, indicating that these disruptions are a fundamental, qualitative component of the Anthropocene. Nonetheless, the notion of disruption has yet to be theorized. Informally, disruptions are direct or indirect consequences of specific causes that impair the contribution of parts of living systems to their ability to last over time. To progress in this theorization, we work here on a particular case. Even relatively minor temperature changes can significantly impact plant-pollinator synchrony, disrupting mutualistic interaction networks. Understanding this phenomenon requires a specific rationale since models describing it use both historical and systemic reasoning. Specifically, history justifies that the ecosystem initially exists in a very narrow part of the possibility space where all its populations are viable, and the disruption leads to a more generic configuration where some populations are not viable. Building on this rationale, we develop a mathematical schema inspired by Boltzmann’s entropy, apply it to this situation, and provide a technical definition of disruption.

Osteosarcoma is the most common primary bone cancer. According to medical and biological studies, it has a high genetic complexity, thus, to differentiate the mechanisms of appearance and evolution of this disease is a difficult task. In this paper, we use three simplest and well known mathematical models to describe the behavior of several cell lines of osteosarcoma. First, we use a potential law to describe the tumor growth in immunosuppressed mice; with it we show that the variation of tumor growth has a sublinear behavior without the blow-up phenomenon. Second, the logistic model is used to obtain a good aproximation to the rates of proliferation in cell confluency in in vitro experiments. Third, we use a linear reaction-diffusion model; with it, we describe the diffusion behavior for some cell lines. These three models allow us to give a classification of cell lines according to the rates of tumor growth and proliferation and to the diffusion coefficient. A relationship is found between the rates of the tumor growth, the diffusion coefficient and tumorigenicity. Experimental data are extracted from Lauvrak et al. (British Journal of Cancer 109(8):2228–2236, 2013).

Understanding the relationship between adult fish populations (the "stock") and the number of new fish entering the population (the "recruits") is essential for effective fisheries management. Traditionally, this relationship is represented by a stock-recruitment (SR) function, which is a simplified mathematical model that directly links stock size to recruitment. However, fish populations pass through several life stages, each stage influenced by unique population dynamic factors. Current SR functions often overlook these complexities, assuming that recruitment depends solely on the adult population size. In this study, we use a multi-stage, age-structured discrete-time population dynamic model that accounts for all life stages and the transitions between them. We demonstrate that, in general, a closed-form, univariate SR function may not accurately represent the recruitment process when these life stages are considered. Instead, we identify specific mathematical conditions under which a SR function is equivalent to our multi-stage model. Our findings suggest a re-evaluation of conventional SR models, advocating for multi-stage approaches to support fisheries management decisions.

This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county’s wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model’s prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model’s projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function—the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

Conflicts within the tsetse fly belt revealed a strong correlation between the dynamics of bovine trypanosomosis and the insurgency involving farmers and herders in Nigeria and parts of West Africa. This study examined the history, causes and influence of farmers-herdsmen conflicts on banditry, terrorism and food security as it relates to the epidemiology of African animal trypanosomosis (AAT). A combination of literature database searches, semi-structured questionnaires, and mathematical modeling was employed. The study found that transhumance contributes significantly to conflicts between farmers and herdsmen. An average of 6.46 persons per attack were reported between 2005 and 2021. Only 8.4(%) (95(%) CI: 5.0(-)12.9) of farmers and 18.2(%) (95(%) CI: 12.4(-)25.4) of herdsmen have engaged in conflict resolution efforts. The study shows that both conflict and the spread of trypanosomosis can be effectively controlled when (R_0 < 1), ensuring that the sub-population remains in the basin of attraction of the trypanosomosis-conflict-free equilibrium ((T_{0c})). The partial derivative of the basic reproduction number, (R_0), with respect to improved conflict resolution, suggests that halting transhumance can prevent a portion of the cattle recruitment rate ((Lambda_c)) from becoming infected with AAT. Climate change exacerbates these issues, leading to settlement and resettlement strategies within the fly belt regions. The model indicates that the basic reproduction number can only be reduced to less than one ((R_0 < 1)) to become globally asymptotically stable if there is effective conflict resolution involving both farmers and herders. The study advocates for the establishment of ranching in tsetse-free zones with adequate social amenities, improved marketing strategies for animals and animal products led by government agencies through public-private partnerships, the banning of open grazing, and strict enforcement of policies against violators.

Coarse-grain models are essential to understand the biological function of DNA molecules because the length and time scales of the sequence-dependent physical properties of DNA are often beyond the reach of experimental and all-atom computational methods. Simulating coarse-grain models of DNA, e.g. using Langevin dynamics, requires the parametrization of both potential and kinetic energy functions. Many studies have shown that the flexibility (i.e., potential energy) of a DNA molecule depends on its sequence. In contrast, little is known about the sequence-dependence of DNA mass parameters required to model its kinetic energy. In this paper, an algebraic expression is derived for the kinetic energy as a function of linear and angular velocities of each DNA base parameterized by its mass, center of mass, and rotational inertia tensor. The parameters of this function are then approximated from a set of fine-grain molecular dynamics simulations representing all combinations of the four DNA base pairs AT, TA, GC, and CG, in different sequence contexts. Compatibility conditions associated with the assumption of each base being modeled as a rigid body were verified to be good approximations. The kinetic parameters were found to be significantly different between the four G, C, A, and T bases, and to not be dependent on the sequence context. This suggests that the effective kinetic parameters of a DNA base may depend only on the base itself, not on its neighbors.

Accurate prediction of epidemic evolution faces challenges such as understanding disease dynamics and inadequate epidemiological data. A recent approach faced these issues by modeling susceptible-infectious-susceptible (SIS) dynamics based on the first two statistical moments. Here, we improve this approach by including finite-size populations and analyzing the stability of the resulting model. Results underscore the influence of uncertainties and population size in the natural history of the epidemic.