Biological Model Research Articles

Biased versus unbiased numerical methods for stochastic simulations

Approximate numerical methods are one of the most used strategies to extract information from many-interacting-agents systems. In particular, numerical approximations are of extended use to deal with epidemic, ecological and biological models, since unbiased methods like the Gillespie algorithm can become unpractical due to high CPU time usage required. However, the use of approximations has been debated and there is no clear consensus about whether unbiased methods or biased approach is the best option. In this work, we derive scaling relations for the errors in approximations based on binomial extractions. This finding allows us to build rules to compute the optimal values of both the discretization time and number of realizations needed to compute averages with the biased method with a target precision and minimum CPU-time usage. Furthermore, we also present another rule to discern whether the unbiased method or biased approach is more efficient. Ultimately, we will show that the choice of the method should depend on the desired precision for the estimation of averages.

Transcriptional control of a metabolic switch regulating cellular methylation reactions is part of a common response to stress in divergent bee species.

Recent global declines in bee health have elevated the need for a more complete understanding of the cellular stress mechanisms employed by diverse bee species. We recently uncovered the biomarker lethal (2) essential for life (l(2)efl) genes as part of a shared transcriptional program in response to a number of cell stressors in the western honey bee (Apis mellifera). Here we describe another shared stress responsive gene, Glycine N-methyl transferase (Gnmt), which is known as a key metabolic switch controlling cellular methylation reactions. We observed Gnmt induction by both abiotic and biotic stressors. We also found increased levels of the GNMT reaction product sarcosine in the midgut after stress linking metabolic changes with the observed changes in gene regulation. Prior to this study, Gnmt upregulation has not previously been associated with cellular stress responses in other organisms. To determine whether this novel stress-responsive gene would behave similarly in other bee species, we first characterized the cellular response to ER stress in lab-reared adults of the solitary alfalfa leafcutting bee (Megachile rotundata) and compared with age-matched honey bees. The novel stress gene Gnmt was induced in addition to a number of canonical gene targets induced in both bee species upon UPR activation, suggesting that stress-induced regulation of cellular methylation reactions is a common feature of bees. Therefore, this study suggests that honey bee can serve as an important model for bee biology more broadly, although studies on diverse bee species will be required to fully understand global declines in bee populations.

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases.

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

Embryology of the fat-tailed dunnart (Sminthopsis crassicaudata): A marsupial model for comparative mammalian developmental and evolutionary biology.

Marsupials are a diverse and unique group of mammals, but remain underutilized in developmental biology studies, hindering our understanding of mammalian diversity. This study focuses on establishing the fat-tailed dunnart (Sminthopsis crassicaudata) as an emerging laboratory model, providing reproductive monitoring methods and a detailed atlas of its embryonic development. We monitored the reproductive cycles of female dunnarts and established methods to confirm pregnancy and generate timed embryos. With this, we characterized dunnart embryo development from cleavage to birth, and provided detailed descriptions of its organogenesis and heterochronic growth patterns. Drawing stage-matched comparisons with other species, we highlight the dunnarts accelerated craniofacial and limb development, characteristic of marsupials. The fat-tailed dunnart is an exceptional marsupial model for developmental studies, where our detailed practices for reproductive monitoring and embryo collection enhance its accessibility in other laboratories. The accelerated developmental patterns observed in the Dunnart provide a valuable system for investigating molecular mechanisms underlying heterochrony. This study not only contributes to our understanding of marsupial development but also equips the scientific community with new resources for addressing biodiversity challenges and developing effective conservation strategies in marsupials.

Numerical approximation of the typhoid disease model via Genocchi wavelet collocation method

AbstractIn this paper, we have considered the fractional typhoid disease model and obtained the numerical approximation of the model via the innovative wavelet scheme called the Genocchi wavelet collocation method (GWCM) with the help of Caputo fractional derivative for the fractional order. The approach under consideration is a powerful tool for obtaining numerical solutions to fractional-order nonlinear differential equations. The GWCM approach yields accurate solutions that are very close to exact solutions for highly nonlinear problems by avoiding data rounding and just computing a few terms. The Genocchi wavelet basis functions possess remarkable properties, including compact support, making them well-suited for approximating solutions to differential equations. The main benefit of this method lies in its capability to reduce the computational complexity associated with solving systems of ODEs, resulting in accurate and efficient solutions. The results of the developed technique, the RK4 method, and the ND solver have been compared. The numerical outcomes demonstrate that the implemented technique is incredibly effective and precise for solving the Typhoid model of fractional order. This paper contributes to numerical analysis by introducing the Genocchi wavelet method as a robust tool for solving biological models.

Analyzing solitary wave solutions of the nonlinear Murray equation for blood flow in vessels with non-uniform wall properties

Solitary wave solutions are of great interest to bio-mathematicians and other scientists because they provide a basic description of nonlinear phenomena with many practical applications. They provide a strong foundation for the development of novel biological and medical models and therapies because of their remarkable behavior and persistence. They have the potential to improve our comprehension of intricate biological systems and help us create novel therapeutic approaches, which is something that researchers are actively investigating. In this study, solitary wave solutions of the nonlinear Murray equation will be discovered using a modified extended direct algebraic method. These solutions represent a uniform variation in blood vessel shape and diameter that can be used to stimulate blood flow in patients with cardiovascular disease. These solutions are newly in the literature, and give researchers an important tool for grasping complex biological systems. To see how the solitary wave solutions behave, graphs are displayed using Matlab.

Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks

We present a novel set of quantitative measures for “likeness” (error function) designed to alleviate the time-consuming and subjective nature of manually comparing biological recordings from electrophysiological experiments with the outcomes of their mathematical models. Our innovative “blended” system approach offers an objective, high-throughput, and computationally efficient method for comparing biological and mathematical models. This approach involves using voltage recordings of biological neurons to drive and train mathematical models, facilitating the derivation of the error function for further parameter optimization. Our calibration process incorporates measurements such as action potential (AP) frequency, voltage moving average, voltage envelopes, and the probability of post-synaptic channels. To assess the effectiveness of our method, we utilized the sea slug Melibe leonina swim central pattern generator (CPG) as our model circuit and conducted electrophysiological experiments with TTX to isolate CPG interneurons. During the comparison of biological recordings and mathematically simulated neurons, we performed a grid search of inhibitory and excitatory synapse conductance. Our findings indicate that a weighted sum of simple functions is essential for comprehensively capturing a neuron’s rhythmic activity. Overall, our study suggests that our blended system approach holds promise for enabling objective and high-throughput comparisons between biological and mathematical models, offering significant potential for advancing research in neural circuitry and related fields.

Super-resolution radial fluctuations microscopy for optimal resolution and fidelity.

Fluorescence fluctuation super-resolution microscopy (FF-SRM) has emerged as a promising method for the fast, low-cost, and uncomplicated imaging of biological specimens beyond the diffraction limit. Among FF-SRM techniques, super-resolution radial fluctuation (SRRF) microscopy is a popular technique but is prone to artifacts, resulting in low fidelity, especially under conditions of high-density fluorophores. In this Letter, we developed a novel, to the best of our knowledge, combinatory computational super-resolution microscopy method, namely VeSRRF, that demonstrated superior performance in SRRF microscopy. VeSRRF combined intensity and gradient variance reweighted radial fluctuations (VRRF) and enhanced-SRRF (eSRRF) algorithms, leveraging the enhanced resolution achieved through intensity and gradient variance analysis in VRRF and the improved fidelity obtained from the radial gradient convergence transform in eSRRF. Our method was validated using microtubules in mammalian cells as a standard biological model system. Our results demonstrated that VeSRRF consistently achieved the highest resolution and exceptional fidelity compared to those obtained from other algorithms in both single-molecule localization microscopy (SMLM) and FF-SRM. Moreover, we developed the VeSRRF software package that is freely available on the open-source ImageJ/Fiji software platform to facilitate the use of VeSRRF in the broader community of biomedical researchers. VeSRRF is an exemplary method in which complementary microscopy techniques are integrated holistically, creating superior imaging performance and capabilities.

Environmental Homeopathy: Homeopathic Potencies Regulate the Toxicity and Growth of Raphidiopsis raciborskii (cyanobacteria) and can be Tracked Physico-Chemically. Part 1: Biological Results.

Cyanobacteria are microorganisms found in many parts of the world and several genera, such as Raphidiopsis raciborskii, are producers of cyanotoxins. Homeopathic potencies have been found to modulate toxicity in different biological models, and the present study endeavors to discover whether this might also be the case with cyanobacteria. Our objective was to investigate the possible effects of homeopathic potencies on the resilience of Artemia franciscana (brine shrimp) embryos to saxitoxin (STX; cyanotoxin) and on controlling the growth of R. raciborskii in vitro. A. franciscana cysts were cultivated in seawater in 96-well plates to evaluate the hatching rate and vitality, plus the gene expression of heat shock proteins (HSPs), after being challenged with R. raciborskii extract containing 2.5 µg/L of STX and treated with different homeopathic potencies. Untreated wells were used as controls ("base-line"). Potencies were chosen from a screening process based on seven selected homeopathic preparations according to the similitude of STX symptoms (Sulphur, Zincum metallicum, Nitric acidum, Plumbum metallicum, Mercurius solubilis, Phosphoric acidum, Isotherapic from R. raciborskii extract; all at 6cH, 30cH and 200cH). Cultures of R. raciborskii maintained in an artificial seawater medium were equally treated with screened homeopathic potencies selected from the same list but specifically for their growth control as a function of time. A 15% lower rate of hatching of A. franciscana cysts was observed after treatment with Nitric acidum 6cH in comparison with baseline (p = 0.05). A complete toxicity reversal was seen after treatment with Isotherapic 200cH, with a 23-fold increase of Hsp 26 gene expression (p = 0.023) and a 24-fold increase of p26 gene expression (p ≤ 0.001) in relation to baseline. Nitric acidum 200cH and Mercurius solubilis 30cH limited the exponential growth of cyanobacteria up to 95% and 85% respectively (p ≤ 0.003) in relation to baseline. Succussed water presented only a transitory 50% inhibition effect. Isotherapic 200cH improved A. franciscana bioresilience to STX; Nitric acidum 200cH and Mercurius solubilis 30cH showed the optimal performance on limiting R. raciborskii growth. The results point to the potential of homeopathic potencies to mitigate environmental problems related to water quality.

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries.

Atherosclerosis is the primary cause of cardiovascular disease, resulting in mortality, elevated healthcare costs, diminished productivity, and reduced quality of life for individuals and their communities. This is exacerbated by the limited understanding of its underlying causes and limitations in current therapeutic interventions, highlighting the need for sophisticated models of atherosclerosis. This review critically evaluates the computational and biological models of atherosclerosis, focusing on the study of hemodynamics in atherosclerotic coronary arteries. Computational models account for the geometrical complexities and hemodynamics of the blood vessels and stenoses, but they fail to capture the complex biological processes involved in atherosclerosis. Different in vitro and in vivo biological models can capture aspects of the biological complexity of healthy and stenosed vessels, but rarely mimic the human anatomy and physiological hemodynamics, and require significantly more time, cost, and resources. Therefore, emerging strategies are examined that integrate computational and biological models, and the potential of advances in imaging, biofabrication, and machine learning is explored in developing more effective models of atherosclerosis.

Classification of 2-node excitatory–inhibitory networks

We classify connected 2-node excitatory–inhibitory networks under various conditions. We assume that, as well as for connections, there are two distinct node-types, excitatory and inhibitory. In our classification we consider four different types of excitatory–inhibitory networks: restricted, partially restricted, unrestricted and completely unrestricted. For each type we give two different classifications. Using results on ODE-equivalence and minimality, we classify the ODE-classes and present a minimal representative for each ODE-class. We also classify all the networks with valence ≤2. These classifications are up to renumbering of nodes and the interchange of ‘excitatory’ and ‘inhibitory’ on nodes and arrows. These classifications constitute a first step towards analysing dynamics and bifurcations of excitatory–inhibitory networks. The results have potential applications to biological network models, especially neuronal networks, gene regulatory networks, and synthetic gene networks.

A weighted combination of reproducing kernel particle shape functions with cardinal functions of scalable polyharmonic spline radial kernel utilized in Galerkin weak form of a mathematical model related to anti-angiogenic therapy

In this manuscript, a new localized meshfree (meshless) approximation, i.e., a combination of the reproducing kernel particle (RKP) shape functions with the cardinal functions based on the scalable polyharmonic spline (PHS) radial function is introduced. It is called the RKP+PHS+poly approximation, and its convergence rate is of order O(hm+1), where m is the total degree of polynomials. We apply this method to construct the spaces of trial and test functions in a Galerkin scheme of an extended version of the biological mathematical model in two dimensions describing the interactions between endothelial cells, fibronectin, angiogenic growth factors, and fasentin concentrations. By considering the row-sum method, the eigenvalue stability is also numerically carried out for the discrete equations corresponding to the obtained weak formulation. Accordingly, a semi-implicit form of the backward difference method of order 1 (SBDF1) has been utilized to approximate the weak form in time. We complete our numerical algorithm by solving the obtained full-discretized problem overtime via the biconjugate gradient stabilized (BiCGSTAB) solver with a proper preconditioner. Some simulation results are investigated by estimating the maximum velocity parameter of an enzymatic reaction from the experimental dose–response curve of fasentin to demonstrate the effect of using fasentin drug.

Development of Functional Foods with Enhanced Health Benefits

Purpose: This study aimed to explore the development of functional foods with enhanced health benefits. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the development of functional foods with enhanced health benefits. Preliminary empirical review revealed that these innovative food products offer promising avenues for improving health outcomes and preventing chronic diseases. By formulating functional foods with targeted bioactive compounds, researchers tailored interventions to address prevalent health concerns such as gut dysbiosis, hyperlipidemia, oxidative stress, and nutrient deficiencies. The study emphasized the need for continued research and innovation to optimize formulations, mechanisms of action, and delivery systems. Collaboration between researchers, food scientists, healthcare professionals, policymakers, and the food industry was highlighted as essential for driving innovation and translating scientific findings into tangible interventions that benefit public health. Overall, the study highlighted the potential of functional foods to revolutionize nutrition and disease prevention strategies. Unique Contribution to Theory, Practice and Policy: The Nutritional Epidemiology, Nutrigenomics and Systems Biology model may be used to anchor future studies on the development of functional foods with enhanced health benefits. The contributed significantly to theory, practice, and policy in the field of nutrition and public health. It provided valuable insights into the complex interactions between bioactive compounds and physiological processes, advancing theoretical frameworks in nutrition science. By emphasizing evidence-based formulation and product optimization, the study offered practical guidance for food scientists and manufacturers in creating functional foods with demonstrable health benefits. Moreover, the study advocated for transparent labeling and regulatory oversight to ensure consumer safety and quality control. Overall, its recommendations facilitated the integration of functional foods into dietary guidelines and public health initiatives, promoting improved health outcomes and reduced chronic disease burden.

Determination of biochemically essential sulfhydryl proteins in selenol derivatives of chloro-diphenyl diselenide formation in simple biological model: Implications of its glutathione peroxidase mimicry therapeutic potentials

Determination of biochemically essential sulfhydryl proteins in selenol derivatives of chloro-diphenyl diselenide formation in simple biological model: Implications of its glutathione peroxidase mimicry therapeutic potentials

Mathematical modeling of foreign bodies with different density in biological and non-biological models in the experiment

Background. Modeling allows investigating both existing and predicted processes and is widely used in basic science and in many industries. The aim is to develop a mathematical model for determining the size of the foreign bodies (FB) and their radiographic density in non-biological and biological models to improve the results of diagnosis for gunshot ricochet wounds. Materials and methods. In the biological non-living model (a piece of pork) and non-biological models (polystyrene, foam rubber), we place the FB made of paper, leather, rubber, plastic, and lithium-ion batteries. The number of the FB is 9 of each type. Number of models is 3 each: pork, polystyrene, foam rubber. We measure the dimensions of the FB and models with a metric ruler. For each model, we select the FB, which we label with the study number. We immerse the FB to the same depth using a Billroth general surgical medium hemostatic clamp in the following sequence: paper, leather, rubber, plastic, and lithium-ion battery. Multislice computed tomography (MSCT) of the models is performed on the Revolution EVO (2021) apparatus with measurement of the sizes and radiographic density of the FB and models. Radiographic density was measured in conventional units on the Hounsfield scale. For each study group, the ratio of the actual sizes of the removed FB and according to MSCT data was determined in the MathCad 15 computer math software, depending on the radiological density of the FB and the model. Results. According to MSCT data, the radiographic density of the models on the Hounsfield scale is as follows: polystyrene — –990.0 ± 0.3 units; foam rubber — –985.0 ± 0.2 units; pork — 62.0 ± 0.3 units; radiographic density of the foreign bodies: paper — –743.0 ± 10.3 units, leather — –258.0 ± 14.2 units, rubber — –12.0 ± 2.6 units, plastic — 183.0 ± 14.6 units, lithium-ion batteries — 3071 units. Visualization of paper in non-biological and biological models and leather in non-biological models is problematic due to the similar radiographic density of the models and the inability to measure the dimensions. When the FB (rubber, plastic, battery) is immersed in polystyrene, the coefficient of length (CL) is 1.0612, the coefficient of width (CW) is 1.928; in foam rubber: CL is 0.9926, CW is 1.9641; in pork: CL is 0.8394, CW is 1.534. Comparing the average coefficients of the ratio (CL and CW), we find that the coefficient in a biological model is closest to 1. This means that the FB from rubber, plastic, and batteries are best detected in pork. Conclusions. The actual dimensions of the FB placed in biological and non-biological models differ from those obtained by MSCT. Data correction is performed through calculated coefficients for length and width. The radiographic density of the model affects the radial visualization of the FB. The use of mathematical modeling in determining the sizes and radiographic density allows reducing the measurement error and determine the structure of the FB.

Recent Advances in Bioinspired Soft Robots: Fabrication, Actuation, Tracking, and Applications

AbstractNatural organisms offer a rich source for the construction of soft robots exhibiting autonomous and intelligent behaviors, encompassing attributes like motion, perception, and adaptability to environmental shifts. Drawing inspiration from these biological models, a multitude of soft robots have emerged, each distinguished by unique structures and functionalities enabling diverse actions, including swimming, crawling, swinging, walking, and tumbling. In this review, several soft robots and their motion modes from the perspective of specific native species are addressed. The actuation methods of soft robots are discussed, encompassing chemical, electrical, ultrasonic, optical, and magnetic actuation mechanisms. Furthermore, the application domains of soft robots, encompassing areas such as vessel recanalization, targeted drug delivery, cargo manipulation, and sensing are explored, providing a concise summary of their roles and potentials. The current challenges encountered in this research field are highlighted, and promising directions pertaining to soft robots are emphasized.

Most Probable Dynamics of the Single-Species with Allee Effect under Jump-Diffusion Noise

We explore the most probable phase portrait (MPPP) of a stochastic single-species model incorporating the Allee effect by utilizing the nonlocal Fokker–Planck equation (FPE). This stochastic model incorporates both non-Gaussian and Gaussian noise sources. It has three fixed points in the deterministic case. One is the unstable state, which lies between the two stable equilibria. Our primary focus is on elucidating the transition pathways from extinction to the upper stable state in this single-species model, particularly under the influence of jump-diffusion noise. This helps us to study the biological behavior of species. The identification of the most probable path relies on solving the nonlocal FPE tailored to the population dynamics of the single-species model. This enables us to pinpoint the corresponding maximum possible stable equilibrium state. Additionally, we derive the Onsager–Machlup function for the stochastic model and employ it to determine the corresponding most probable paths. Numerical simulations manifest three key insights: (i) when non-Gaussian noise is present in the system, the peak of the stationary density function aligns with the most probable stable equilibrium state; (ii) if the initial value rises from extinction to the upper stable state, then the most probable trajectory converges towards the maximally probable equilibrium state, situated approximately between 9 and 10; and (iii) the most probable paths exhibit a rapid ascent towards the stable state, then maintain a sustained near-constant level, gradually approaching the upper stable equilibrium as time goes on. These numerical findings pave the way for further experimental investigations aiming to deepen our comprehension of dynamical systems within the context of biological modeling.

Informing policy via dynamic models: Cholera in Haiti.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

Application of Recurrence Plot Analysis to Examine Dynamics of Biological Molecules on the Example of Aggregation of Seed Mucilage Components

The goal of the research is to describe the aggregation process inside the mucilage produced by plant seeds using molecular dynamics (MD) combined with time series algorithmic analysis based on the recurrence plots. The studied biological molecules model is seed mucilage composed of three main polysaccharides, i.e. pectins, hemicellulose, and cellulose. The modeling of biological molecules is based on the assumption that a classical–quantum passage underlies the aggregation process in the mucilage, resulting from non-covalent interactions, as they affect the macroscopic properties of the system. The applied recurrence plot approach is an important tool for time series analysis and data mining dedicated to analyzing time series data originating from complex, chaotic systems. In the current research, we demonstrated that advanced algorithmic analysis of seed mucilage data can reveal some features of the dynamics of the system, namely temperature-dependent regions with different dynamics of increments of a number of hydrogen bonds and regions of stable oscillation of increments of a number of hydrophobic–polar interactions. Henceforth, we pave the path for automatic data-mining methods for the analysis of biological molecules with the intermediate step of the application of recurrence plot analysis, as the generalization of recurrence plot applications to other (biological molecules) datasets is straightforward.

Histomorphological changes in gills and hepatopancreas of crabs: an integrative review

Crustaceans are a biological model of ecotoxicology and consequently are widely used to assess the impact of xenobiotics. The toxicity of xenobiotics starts in the interaction of biomolecules and can even cause changes in an entire ecosystem. This paper aims to compile the histomorphological changes caused by xenobiotics in the gills and hepatopancreas of crabs through an integrative review. The literature search was done using PubMed with keywords in English and the filters "full text" and "publications from 2001-21". A total of 299 studies were found and 35 of them were selected based on the title and abstract. Later, 17 were included in the study. There were 18 categories of histomorphological changes in the gills and 22 in the hepatopancreas. We noticed that there is no consensus on the terminology used for histomorphological changes in this group. Furthermore, it was observed that the quality of the histological slides was quite diverse, making the identification and comparison of changes in the images problematic. The aforementioned considerations justify the need for this study to address a gap and encourage future research to develop a guide of histomorphological changes in crabs and its use as a tool for environmental biomonitoring.