Behavior Of Biological Systems Research Articles

Comparative Analysis of Freeze-Dried Pleurotus ostreatus Mushroom Powders on Probiotic and Harmful Bacteria and Its Bioactive Compounds

Pleurotus ostreatus (oyster mushroom) holds excellent promise worldwide, bringing several opportunities and augmenting the tool sets used in the biotechnology field, the food industry, and medicine. Our study explores the antimicrobial and probiotic growth stimulation benefits of freeze-dried P. ostreatus powders (OMP-TF, oyster mushroom powder from the total fresh sample; OMP-CSR, oyster mushroom powder from the cooked solid residue; OMP-CL, oyster mushroom powder from the cooked liquid), focusing on their bioactive compounds and associated activities. Our research examined polysaccharide fractions—specifically total glucans and α- and β-glucans—alongside secondary metabolites, including polyphenols and flavonoids, from freeze-dried mushroom powders. Additionally, carbon nanodots (CNDs) were also characterized. The growth inhibition was tested against Escherichia coli and Staphylococcus epidermidis, while the capacity for stimulating probiotic growth was evaluated using Lactobacillus plantarum and Lactobacillus casei. Evidence indicates that OMP-CL and OMP-CSR exhibit significant antimicrobial properties against S. epidermidis Gram-positive bacteria. OMP-CL notably promoted the growth of L. casei. OMP-CL, containing the most significant number of CNDs, has shown to be a valuable source for gut microbiota modulation, with its antimicrobial and probiotic-stimulating efficacy. However, further in vitro and in vivo studies should be performed to explore CNDs and their behavior in different biological systems.

Pendekatan Matematika yang Digunakan pada Biologi

Despite their apparent differences, mathematics and biology are increasingly being used in tandem to model and analyze a wide range of biological processes. This article addresses the use of multiple methods, including population modeling, differential equations, mathematical genetics, and epidemiology, to the study of biology, a field known as biomathematics. Genetic dynamics and the behavior of biological systems, such as population expansion and disease transmission, are understood through mathematical modeling. SIR and SEIR models are used in epidemiology to describe how infectious diseases spread throughout a population. This journal emphasizes the value of mathematical approaches in comprehending intricate biological systems and the necessity of interdisciplinary research between mathematics and biology to address both theoretical and practical issues through a review of the literature. The purpose of this article is to explain the relationship between mathematics and biology and the application of mathematical models in the analysis of biological phenomena. This article uses a literature study writing method by recording research problems, analyzing, and collaborating different thoughts.

CMINNs: Compartment model informed neural networks — Unlocking drug dynamics

In the field of pharmacokinetics and pharmacodynamics (PKPD) modeling, which plays a pivotal role in the drug development process, traditional models frequently encounter difficulties in fully encapsulating the complexities of drug absorption, distribution, and their impact on targets. Although multi-compartment models are frequently utilized to elucidate intricate drug dynamics, they can also be overly complex. To generalize modeling while maintaining simplicity, we propose an innovative approach that enhances PK and integrated PK–PD modeling by incorporating fractional calculus or time-varying parameter(s), combined with constant or piecewise constant parameters. These approaches effectively model anomalous diffusion, thereby capturing drug trapping and escape rates in heterogeneous tissues, which is a prevalent phenomenon in drug dynamics. Furthermore, this method provides insight into the dynamics of drug in cancer in multi-dose administrations. Our methodology employs a Physics-Informed Neural Network (PINN) and fractional Physics-Informed Neural Networks (fPINNs), integrating ordinary differential equations (ODEs) with integer/fractional derivative order from compartmental modeling with neural networks. This integration optimizes parameter estimation for variables that are time-variant, constant, piecewise constant, or related to the fractional derivative order. The results demonstrate that this methodology offers a robust framework that not only markedly enhances the model’s depiction of drug absorption rates and distributed delayed responses but also unlocks different drug–effect dynamics, providing new insights into absorption rates, anomalous diffusion, drug resistance, persistence, and pharmacokinetic tolerance, all within a system of just two (fractional) ODEs with explainable results. These findings have the potential to streamline drug development by improving the prediction of drug behavior in complex biological systems and shedding light on cancer cell death mechanisms, ultimately aiding in the design of more effective therapeutic strategies.

A workflow for the hybrid modelling and simulation of multi-timescale biological systems

With the steady advance of in-silico biological experimentation, model construction and simulation becomes a ubiquitous tool to understand and predict the behaviour of many biological systems. However, biological processes may contain components from different types of reaction networks, resulting in models with different (e.g., slow and fast) timescales. Hybrid simulation is one approach which can be employed to efficiently execute multi-timescale models. In this paper, we present a methodology and workflow utilizing (coloured) hybrid Petri nets to construct smaller and more complicated hybrid models. The presented workflow integrates algorithms and ideas from hybrid simulation of biochemical reaction networks as well as Petri nets. We also construct multi-timescale hybrid models and then show how these models can be efficiently executed using three different advanced hybrid simulation algorithms.

Optimal Paradigms for Quantitative Modeling in Systems Biology Demonstrated for Spinal Motor Neuron Synthesis

Since the 1990s, Petri nets have been used in systems biology for quantitative modeling. Despite the increasing number of models developed during this period, doubts remain about their biological relevance. Although biological systems predominantly exhibit intracellular or cellular structures, the models rely largely on deterministic predictions, failing to capture the inherent randomness and uncertainties of such systems. The question arises whether these models accurately describe the dynamic behavior of biological systems. This paper introduces a methodology for selecting the appropriate modeling paradigms in systems biology. Initially, we construct a Petri net model and perform deterministic, stochastic, and fuzzy stochastic simulations. Then we perform various statistical tests to measure the discrepancies between the simulation results. Based on scale-density analysis, we determine the modeling approach that best approximates the biological system. Finally, we compare the results of the statistical tests and the scale-density analysis to identify the optimal modeling approach. We applied the proposed methodology to the synthesis of spinal motor neuron protein from the spinal motor neuron-2 gene. Analysis revealed significant discrepancies between the simulation results of different modeling paradigms. Due to the sparse nature of the underlying drug-disease network, we conclude that the fuzzy stochastic paradigm provides the most biologically relevant results. We predict drug combinations that could lead to an up to 149-fold increase in spinal motor neuron protein levels, indicating a promising treatment for the disease. This methodology has the potential for application to other gene-drug-disease networks and broader biological systems.

A rigorously simple quantitative model for free radical behavior in aerobic biological systems

Background Human life is based on oxygen respiration and an enzymatic, free radical-dependent water chemistry, whose billions of parallel reactions take place at pH ∼7.4 and a temperature of 37°C, in accordance with the laws of chemistry. The cellular metabolic processes occur over time periods covered by the half-lives of ROS (reactive oxygen species) for °OH to over 10 s for LOS (lipid oxygen species), indicating that mixtures of free radicals form the basic components for these processes. Summary and Key Messages The main source of radicals is the mitochondrial conversion of 1-5 % oxygen into "primary" ROS and "secondary" LOS. Every endogenous and exogenous radical generation, triggered by "natural background radiation", "natural environment" or "solar radiation" leads to qualitatively similar mixtures of "primary" ROS and "secondary" LOS or RNS (reactive nitrogen species). A Multilevel Antioxidant Regulation, Repair and Protection System (MARRPS) keeps these radical mixtures in a steady state. Depending on the total number of free radicals, different areas of radical action are defined. The Free Radical Ground State (FRGS) with "homeostasis" and "adaptive homeostasis", the Free Radical Threshold Value (FRTV) and Free Radical Pathological Conditions (FRPC). The quantitative ratio ROS > LOS comprehensively characterizes the "homeostasis" and "adaptive homeostasis" area of the FRGS. The total number of free radicals cannot be measured directly in the "homeostasis" area. "Adaptive homeostasis" is achieved when excess radicals are stable produced beyond "homeostasis" of the FRGS. The quantity that remains controllable in this range is a maximum of ∼3.58 10¹² radicals/mg, the value of the body constant FRTV. The sensitized MARRPS provides "semi-stable homeostatic" states characterized by dual stability with ROS > LOS and a stable total ROS/LOS and RNS count beyond the basal FRGS "homeostasis". If the total number of all radicals exceeds the FRTV, where LOS > ROS, this initiates uncontrolled radical chain reactions. The partial failure of the MARRPS in the FRPC area leads to pathological processes which are the starting point for a hundred different diseases. The design principle described by this simple model applies universally to all aerobic life.

Investigating Model Form Error Estimation for Sparse Data

Computational simulations of dynamical systems involve the use of mathematical models and algorithms to mimic and analyze complex real-world phenomena. By leveraging computational power, simulations enable researchers to explore and understand systems that are otherwise challenging to study experimentally. They offer a cost-effective and efficient means to predict and analyze the behavior of physical, biological, and social systems. However, model form error arises in computational simulations from simplifications, assumptions, and limitations inherent in the mathematical model formulation. Several methods for addressing model form error have been proposed in the literature, but their robustness in the face of challenges inherent to real-world systems has not been thoroughly investigated. In this work, a data assimilation-based approach for model form error estimation is investigated in the presence of sparse observation data. Including physics-based domain knowledge to improve estimation performance is also explored. The Lotka-Volterra equations are employed as a simple computational simulation for demonstration.

Tuning collective behaviour in zebrafish with genetic modification.

Zebrafish collective behaviour is widely used to assess their physical and mental state, serving as a valuable tool to assess the impact of ageing, disease genetics, and the effect of drugs. The essence of these macroscopic phenomena can be represented by active matter models, where the individuals are abstracted as interactive self-propelling agents. The behaviour of these agents depends on a set of parameters in a manner reminiscent of those between the constituents of physical systems. In a few cases, the system may be controlled at the level of the individual constituents such as the interactions between colloidal particles, or the enzymatic behaviour of de novo proteins. Usually, however, while the collective behaviour may be influenced by environmental factors, it typically cannot be changed at will. Here, we challenge this scenario in a biological context by genetically modifying zebrafish. We thus demonstrate the potential of genetic modification in the context of controlling the collective behaviour of biological active matter systems at the level of the constituents, rather than externally. In particular, we probe the effect of the lack of col11a2 gene in zebrafish, which causes the early onset of osteoarthritis. The resulting col11a2 -/- zebrafish exhibited compromised vertebral column properties, bent their body less while swimming, and took longer to change their orientations. Surprisingly, a group of 25 mutant fish exhibited more orderly collective motion than the wildtype. We show that the collective behaviour of wildtype and col11a2 -/- zebrafish are captured with a simple active matter model, in which the mutant fish are modelled by self-propelling agents with a higher orientational noise on average. In this way, we demonstrate the possibility of tuning a biological system, changing the state space it occupies when interpreted with a simple active matter model.

Insights into the phase behavior at interfaces using vibrational sum frequency generation spectroscopy.

Phase separation is ubiquitous at the interface between two distinct phases. Physical transformation during phase separation often plays a crucial role in many important mechanisms, such as lipid phase separation, which is fundamental for transport through biological membranes. Phase separation can be complex, involving changes in the physical state and the reorganization of molecular structures, influencing the behavior and function of materials and biological systems. Surface-sensitive vibrational sum frequency generation (VSFG) spectroscopy provides a powerful tool for investigating these interfacial processes. As a non-linear optical technique, VSFG spectroscopy is sensitive to changes in molecular orientation and interactions at interfaces, making it an ideal method for studying phase separation processes. Here, we review the molecular interaction mechanisms underlying phase separation. We also explore the application of VSFG spectroscopy in studying phase separation processes at different interfaces. In particular, we focus on oil-water interfaces, which are relevant in environmental and industrial contexts; polymer and lipid surfaces, important for materials science and biological membranes; and intrinsically disordered protein systems, which play key roles in cellular function and disease.

Exploring the effects of simulated microgravity on esophageal cancer cells: insights into morphological, growth behavior, adhesion, and genetic damage.

The exploration of microgravity has garnered substantial scholarly attention due to its potential to offer unique insights into the behavior of biological systems. This study presents a preliminary investigation into the effects of simulated microgravity on esophageal cancer cells, examining various aspects such as morphology, growth behavior, adhesion, inhibition rate, and DNA damage. To achieve this, a novel microgravity simulator named "Gravity Challenge" was utilized for its effectiveness in minimizing external influences that could compromise microgravity conditions. The international cell line SK-GT-4 was utilized as the focal point of this investigation. Results revealed noticeable alterations in the growth behavior of cancer cells following exposure to simulated microgravity for 24 h, characterized by a loss of adhesion properties compared to control cells. Concurrently, cell viability exhibited a decline, as evidenced by cytotoxicity testing. Furthermore, the comet assay test demonstrated that cells subjected to microgravity simulation experienced a higher incidence of DNA damage compared to their control counterparts. In conclusion, this comprehensive examination of the impact of simulated microgravity on esophageal cancer cells extends beyond morphological changes, delving into genetic implications through observed DNA damage. The diminished vitality of cells under microgravity conditions underscores the multifaceted effects on cellular behavior in response to environmental variations. These findings represent a significant step towards understanding the dynamics of cancer cells, laying the groundwork for future research aimed at identifying potential therapeutic strategies for this disease.

Fractal-Dependent Growth of Solidlike Condensates.

The phenomenon of droplet growth occurs in various industrial and natural processes. Recently, the discovery of liquidlike condensates within cells has sparked an increasing interest in understanding their growth behaviors. These condensates exhibit varying material properties that are closely related to many cellular functions and diseases, particularly during the phase transition from liquidlike droplets to solidlike aggregates. However, how the liquid-to-solid phase transition affects the growth of condensates remains largely unknown. In this study, we investigate the growth of peptide-RNA condensates, which behave as either liquidlike droplets or solidlike aggregates depending on the RNA sequences. Dynamic light scattering experiments show that solidlike condensates grow surprisingly faster, with their hydrodynamic diameters increasing over time as d_{h}(t)∼t^{1/2}, contrasting with d_{h}(t)∼t^{1/3} for liquidlike droplets. By combining theoretical analysis and simulations, we demonstrate that this accelerated growth is caused by the noncoalescence aggregation of solidlike condensates and thus formation of percolated swollen structures with a decreased fractal dimension. Moreover, we demonstrate that the accelerated growth can be slowed down by introducing agents that can revert solidlike condensates back to their liquidlike states, such as urea or specific RNAs. Together, our work reveals a fractal-dependent growth mechanism of condensates, with useful insights for understanding the aging of condensates and modulating their aggregation behaviors in synthetic and biological systems.

Neurosimilator for Undergraduate Biophysics and Neurophysiology Courses.

Stringent animal welfare principles are forcing undergraduate instructors to avoid the use of animals. Therefore, many hands-on lab sessions using laboratory animals are progressively replaced by computer simulations. These versatile software simulations permit the observation of the behavior of biological systems under a great variety of experimental conditions. While this versatility is important, computer simulations often work even when a student makes wrong assumptions, a situation that poses its own pedagogical problem. Hands-on learning provides pupils with the opportunity to safely make mistakes and learn organically through trial and error and should therefore still be promoted. We propose an electronic model of an excitable cell composed of different modules representing different parts of a neuron - dendrites, soma, axon and node of Ranvier. We describe a series of experiments that allow students to better understand differences between passive and active cell responses and differences between myelinated and demyelinated axons. These circuits can also be used to demonstrate temporal and spatial summation of signals coming to the neuron via dendrites, as well as the neuron coding by firing frequency. Finally, they permit experimental determination along with theoretical calculations of important biophysical properties of excitable cells, such as rheobase, chronaxie and space constant. This open-source model has been successfully integrated into an undergraduate course of the physiology of excitable cells and student feedback assessment reveals that it helped students to understand important notions of the course. Thus, this neuromorphic circuit could be a valuable tool for biophysics and neuroscience courses in other universities.

Small-area population forecasting in a segregated city using density-functional fluctuation theory

Policy decisions concerning housing, transportation, and resource allocation would all benefit from accurate small-area population forecasts. However, despite the success of regional-scale migration models, developing neighborhood-scale forecasts remains a challenge due to the complex nature of residential choice. Here, we introduce an innovative approach to this challenge by extending density-functional fluctuation theory (DFFT), a proven approach for modeling group spatial behavior in biological systems, to predict small-area population shifts over time. The DFFT method uses observed fluctuations in small-area populations to disentangle and extract effective social and spatial drivers of segregation, and then uses this information to forecast intra-regional migration. To demonstrate the efficacy of our approach in a controlled setting, we consider a simulated city constructed from a Schelling-type model. Our findings indicate that even without direct access to the underlying agent preferences, DFFT accurately predicts how broader demographic changes at the city scale percolate to small-area populations. In particular, our results demonstrate the ability of DFFT to incorporate the impacts of segregation into small-area population forecasting using interactions inferred solely from steady-state population count data.

Constrained motion of self-propelling eccentric disks linked by a spring.

It has been supposed that the interplay of elasticity and activity plays a key role in triggering the non-equilibrium behaviors in biological systems. However, the experimental model system is missing to investigate the spatiotemporally dynamical phenomena. Here, a model system of an active chain, where active eccentric-disks are linked by a spring, is designed to study the interplay of activity, elasticity, and friction. Individual active chain exhibits longitudinal and transverse motions; however, it starts to self-rotate when pinning one end and self-beat when clamping one end. In addition, our eccentric-disk model can qualitatively reproduce such behaviors and explain the unusual self-rotation of the first disk around its geometric center. Furthermore, the structure and dynamics of long chains were studied via simulations without steric interactions. It was found that a hairpin conformation emerges in free motion, while in the constrained motions, the rotational and beating frequencies scale with the flexure number (the ratio of self-propelling force to bending rigidity), χ, as ∼(χ)4/3. Scaling analysis suggests that it results from the balance between activity and energy dissipation. Our findings show that topological constraints play a vital role in non-equilibrium synergy behaviors.

Dynamics and stability analysis of enzymatic cooperative chemical reactions in biological systems with time-delayed effects

The mathematical modeling and dynamic analysis of time-delayed enzymatic chemical reactions in biological systems are presented in this research. The objective is to examine the function of time lags in these reactions and to get a complete knowledge of the behavior of biological systems in a reaction to modifications in the quantity present of reactants and products. The model, which is based on delay differential equations, includes a time delay term to account for the lag between changes in the concentration of reactants, reaction rate constants and product responses. The findings give insight into how enzymatic processes behave dynamically and how stability is impacted by time lags, oscillation and general efficiency of the system. These results have significant importance for our comprehension of how biological processes are regulated and for the creation of biological control structures.

Protease-Driven Phase Separation of Elastin-Like Polypeptides.

Elastin-like polypeptides (ELPs) are a promising material platform for engineering stimuli-responsive biomaterials, as ELPs undergo phase separation above a tunable transition temperature. ELPs with phase behavior that is isothermally regulated by biological stimuli remain attractive for applications in biological systems. Herein, we report protease-driven phase separation of ELPs. Protease-responsive "cleavable" ELPs comprise a hydrophobic ELP block connected to a hydrophilic ELP block by a protease cleavage site linker. The hydrophilic ELP block acts as a solubility tag for the hydrophobic ELP block, creating a temperature window in which the cleavable ELP reactant is soluble and the proteolytically generated hydrophobic ELP block is insoluble. Within this temperature window, isothermal, protease-driven phase separation occurs when a critical concentration of hydrophobic cleavage product accumulates. Furthermore, protease-driven phase separation is generalizable to four compatible protease-cleavable ELP pairings. This work presents exciting opportunities to regulate ELP phase behavior in biological systems using proteases.

The Impact of Centrality Measures in Protein–Protein Interaction Networks: Tools, Databases, Challenges and Future Directions

Analyzing protein–protein interaction (PPI) networks using machine learning and deep learning algorithms, alongside centrality measures, holds paramount importance in understanding complex biological systems. These advanced computational techniques enable the extraction of valuable insights from intricate network structures, shedding light on the functional relationships between proteins. By leveraging AI-driven approaches, researchers can uncover key regulatory mechanisms, identify critical nodes within the network and predict novel protein interactions with high accuracy. Ultimately, this integration of computational methodologies enhances our ability to comprehend the dynamic behavior of biological systems at a molecular level, paving the way for advancements in drug discovery, disease understanding and personalized medicine. This review paper starts by outlining various popular available PPI network databases and network centrality calculation tools. A thorough classification of various centrality measures has been identified. It primarily delves into the centrality-driven discoveries within PPI networks in biological systems and suggests using edge centrality measures and a hybrid version of node and edge centrality measures in machine learning algorithms and deep learning algorithms to predict hidden knowledge much more effectively.

Bioelectronic Delivery of Potassium Ions Controls Membrane Voltage and Growth Dynamics in Bacteria Biofilms

AbstractBioelectrical signaling, or bioelectricity, is crucial in regulating cellular behavior in biological systems. This signaling, involving ion fluxes and changes in membrane potential (Vmem), is particularly important in the growth of bacterial biofilm. Current microfluidic-based methods for studying bacterial colonies are limited in achieving spatiotemporal control over ionic fluxes due to constant flow within the system. To address this limitation, we have developed a platform that integrates biofilm colonies with bioelectronic ion pumps that enable delivery of potassium (K+) ions, allowing for controlled manipulation of local potassium concentration. Our study examines the impact of controlled K+ delivery on bacterial biofilm growth patterns and dynamics. We observed significant changes in Vmem and coordination within the biofilms. Furthermore, we show that localized K + delivery is highly effective in controlling biofilm expansion in a spatially targeted manner. These findings offer insights into the mechanisms underlying bacterial signaling and growth, and suggest potential applications in bioengineering, synthetic biology, and regenerative medicine, where precise control over cellular signaling and subsequent tissue growth is required.

Past progress in environmental nanoanalysis and a future trajectory for atomic mass-spectrometry methods

The development of engineered nanotechnology has necessitated a commensurate maturation of nanoanalysis capabilities. Building off a legacy established by electron microscopy and light-scattering, environmental nanoanalysis has now benefited from ongoing advancements in instrumentation and data analysis, which enable a deeper understanding of nanomaterial properties, behavior, and impacts. Where once environmental nanoparticles and colloids were grouped into broad ‘dissolved or particulate’ classes that are dependent on a filter size cut-off, now size distributions of submicron particles can be separated and characterized providing a more comprehensive examination of the nanoscale. Inductively coupled plasma-quadrupole mass spectrometry (ICP-QMS), directly coupled to field flow fractionation (FFF-ICP-QMS) or operated in single particle mode (spICP-MS) have spearheaded a revolution in nanoanalysis, enabling research into nanomaterial behavior in environmental and biological systems at expected release concentrations. However, the complexity of the nanoparticle population drives a need to characterize and quantify the multi-element composition of nanoparticles, which has begun to be realized through the application of time-of-flight MS (spICP-TOFMS). Despite its relative infancy, this technique has begun to make significant strides in more fully characterizing particulate systems and expanding our understanding of nanoparticle behavior. Though there is still more work to be done with regards to improving instrumentation and data processing, it is possible we are on the cusp of a new nanoanalysis revolution, capable of broadening our understanding of the size regime between dissolved and bulk particulate compartments of the environment.

Design patterns for the construction of computational biological models.

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.