Acta Biotheoretica最新文献

In 1917, over a century ago, D’Arcy Wentworth Thompson published his groundbreaking book “On Growth and Form”, in which he proposed various mathematical transformations between the shapes of organisms. This paper explores one of the transformations he suggested the conformal pattern-specifically in the context of the ontogenetic growth of human skulls. We applied two alternative algorithms to construct conformal transformations and used them to investigate the growth of human skulls. Our findings indicate that the conformal transformation cannot be dismissed as a potential model for the growth of human skulls.

Koala populations in some regions of eastern Australia are in critical condition. Our research aims to develop effective conservation strategies for these declining koalas threatened by chlamydia infection, predation, and climate change. To achieve this, we developed a mathematical model that includes populations of dingoes and koalas categorized as susceptible, infected, and confined. We conducted a bifurcation analysis within the ordinary differential equations (ODE) model to explore the occurrence of a Hopf bifurcation. This analysis aimed to identify conditions under which the system undergoes qualitative changes in its dynamics, specifically transitions from stable equilibrium points to periodic oscillations. By examining how the system’s behavior shifts as parameters are varied, we could determine the thresholds at which these bifurcations occur, providing insights into the potential for oscillatory patterns in koala populations and disease dynamics. Additionally, we performed a global sensitivity analysis using the partial rank correlation coefficient (PRCC) method. This approach helped us evaluate the relative importance of different parameters on disease prevalence and koala mortality. Extensive numerical simulations allowed us to compare the outcomes of deterministic, stochastic, and diffusive models. Our research indicates that the survival of koala populations is significantly influenced by several key factors: the presence of dingoes, vaccination efforts, and temporary quarantining. Simulations of spatially explicit systems show that increased diffusion among dingoes leads to a more significant clustering of the infected koala population. Our study offers theoretical evidence that vaccination and temporary isolation strategies can significantly improve health outcomes for koalas infected with Chlamydia.

While RNA folding prediction remains challenging, even with machine and deep learning methods, it can also be approached from a topological mathematics perspective. The purpose of the present paper is to elucidate this problem for students and researchers in both the mathematical physics and biology fields, fostering interest in developing novel theoretical and applied solutions that could propel RNA research forward. With this intention, the mathematical method, based on matrix field theory, to compute the topological classification of RNA structures is reviewed. Similarly, McGenus, a computational software that exploits matrix field theory for topological and folding predictions, is examined. To further illustrate the outcomes of this mathematical approach, two types of analyses are performed: the prediction results from McGenus are compared with topological information extracted from experimentally-determined RNA structures, and the topology of RNA structures is investigated for biological significance, both in evolutionary and functional terms. Lastly, we advocate for more research efforts to be conducted at the intersection between physics, mathematics and biology, with a particular focus on the potential contributions that topology can make to the study of RNA folding and structure.

This paper deals with the computational modelling of the bioluminescence pattern formation in suspensions of luminous Escherichia coli bacteria. The aim of this work is to improve the reaction–diffusion–chemotaxis model by introducing modulation functions applied to the rates of the bacterial growth, the chemoattractant production and the oxygen consumption as well as to investigate the influence of the function form on the spatiotemporal pattern formation in an E. coli colony. The nonlinear two-dimensional-in-space model was used to simulate the pattern formation in aqueous cultures of bacteria along the inner lateral surface and along the three-phase contact line of a cylindrical micro-container. The simulated patterns are analysed in order to determine the form of the modulation functions and values of the model parameters closely matching patterns experimentally observed in a luminous E. coli colony. A linear stability analysis of the corresponding one-dimensional-in-space model is applied to determine values of the parameters triggering the self-organisation of the bacterial colony. The numerical simulation at the transient conditions was carried out using the finite difference technique.

The dynamics of directly transmitted infectious diseases may be chaotic or have high degrees of unpredictability, making it difficult to assess the long-term evolution. Chaotic dynamics has been observed in several infectious diseases. The COVID-19 pandemic might follow nonlinear and chaotic dynamics, explaining its low predictability. We study the unpredictability of the dynamics generated by COVID-19 2020-22 pandemic in a sample of eleven countries that have generated very good quality data according to WHO reports. We compute the Maximum Lyapunov Exponent (MLE) profile in moving windows of 250 days along the epidemic for the series of the incidence of reported cases per 100,000 inhabitants. The MLE profile for incidence of reported cases reveals that, regardless of local measures to mitigate the virus, this exponent becomes extremely high (above 0,4 Bits/month on average), close to one order of magnitude above the values ​​previously reported for other infectious diseases. When evaluating the evolution of the MLEs profiles, after a transient that is country dependent, this index becomes very stable and similar in clusters of countries somehow independently of their own sanitary measures. In periods of strong pharmacological and non-pharmacological interventions, the MLE values were reduced probably due to the restriction of the degrees of freedom of the trajectories, recovering in a very short period the pandemic values when the interventions decreased. This behavior suggests a highly chaotic type of dynamics that requires a fine synchronization of classical epidemiological measures with the processes of vaccination and immunity gain.

The concept of ‘population thinking’ was introduced by Ernst Mayr in the mid-twentieth century and it has since become one of the most pervasive notions in the philosophy of biology. Despite its influence, however, the term has been widely misunderstood, even by those who have done the most to champion it. Population thinking today is often confused with population-level thinking (i.e., the idea of treating populations as units of analysis), which, ironically, is the opposite of what Mayr intended to convey when he coined the term. For Mayr, population thinking was a way of emphasizing the variation among individuals in a population, as well as the importance of recognizing their differences and uniqueness. In this paper, I recover the original meaning of ‘population thinking’ and elucidate its central role in evolutionary theory. I also demonstrate its surprising relevance to many other areas of contemporary biology. In particular, I show how the recent introduction of novel methodologies in molecular biology has led to a number of unexpected discoveries that are best understood through the lens of population thinking. Finally, I examine the historical origins and philosophical foundations of population thinking, and I show how it lies at the heart of what makes biology different from physics.

The tools of mathematical modelling offer promising possibilities in our attempts to deepen our knowledge of insufficiently well-understood diseases such as multiple sclerosis. In this article we formulate a mathematical model, mainly based on differential equations, while gradually incorporating the needed modifications to the model with the aim of more accurately simulating the typical qualitative behaviours of the different types of MS. The agents active in the disease process are grouped in two main categories—beneficial cells and harmful cells, based on their influence on the nervous system. In this text a deterministic as well as a stochastic model are proposed, with the goal of the stochastic model being a better representation of the observed irregularities in the occurrence of relapses and remissions in MS. The dynamics of the cell types’ concentrations are modelled through the utilization of appropriate periodic functions. The analytical properties of the models are studied and the connection between parameter values, initial conditions and end results is utilized to produce models capable of simulating disease courses with quantitative characteristics in accordance with real-world clinical data.

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).