Behavior Of Biological Systems Research Articles

The finding and researching algorithm for potentially oscillating enzymatic systems.

Many processes in living organisms are subject to periodic oscillations at different hierarchical levels of their organization: from molecular-genetic to population and ecological. Oscillatory processes are responsible for cell cycles in both prokaryotes and eukaryotes, for circadian rhythms, for synchronous coupling of respiration with cardiac contractions, etc. Fluctuations in the numbers of organisms in natural populations can be caused by the populations’ own properties, their age structure, and ecological relationships with other species. Along with experimental approaches, mathematical and computer modeling is widely used to study oscillating biological systems. This paper presents classical mathematical models that describe oscillatory behavior in biological systems. Methods for the search for oscillatory molecular-genetic systems are presented by the example of their special case – oscillatory enzymatic systems. Factors influencing the cyclic dynamics in living systems, typical not only of the molecular-genetic level, but of higher levels of organization as well, are considered. Application of different ways to describe gene networks for modeling oscillatory molecular-genetic systems is considered, where the most important factor for the emergence of cyclic behavior is the presence of feedback. Techniques for finding potentially oscillatory enzymatic systems are presented. Using the method described in the article, we present and analyze, in a step-by-step manner, first the structural models (graphs) of gene networks and then the reconstruction of the mathematical models and computational experiments with them. Structural models are ideally suited for the tasks of an automatic search for potential oscillating contours (linked subgraphs), whose structure can correspond to the mathematical model of the molecular-genetic system that demonstrates oscillatory behavior in dynamics. At the same time, it is the numerical study of mathematical models for the selected contours that makes it possible to confirm the presence of stable limit cycles in them. As an example of application of the technology, a network of 300 metabolic reactions of the bacterium Escherichia coli was analyzed using mathematical and computer modeling tools. In particular, oscillatory behavior was shown for a loop whose reactions are part of the tryptophan biosynthesis pathway

Characterizing persistence and pause dynamical behaviors in biological systems

This paper analyzed motion that randomly switches between the persistent motion runs and pause periods. A two-state continuous-time Markov chain is used to model the motion, which led to a system with coupled differential equations. Using a combined Fourier–Laplace transform, an analytical expression for calculating the mean-squared displacement is derived. The overall motion is investigated and identified from the obtained mean-squared displacement. The mean-squared displacement is a nonlinear function in time that is dependent on the phase transition rate, the direction switching rate, the average speed, and the initial state. It decays and grows with increasing the direction switching and average speed, respectively. The effective diffusivity descents exponentially in short times and remains constant in long times. The waiting time in each phase decayed exponentially. The probability density function for the position of a particle at a given time tends to be Gaussian in long times. The motion can be interpreted as a super-diffusion in short times and a standard diffusion in long times with a diffusion coefficient dependent on the phase transition rates, the direction switching rate and the average speed. Persistence influences the dynamical behavior for short times while for long times diffusive behavior is exhibited.

The Challenges of Inferring Organic Function from Structure and Its Emulation in Biomechanics and Biomimetics

The discipline called biomimetics attempts to create synthetic systems that model the behavior and functions of biological systems. At a very basic level, this approach incorporates a philosophy grounded in modeling either the behavior or properties of organic systems based on inferences of structure–function relationships. This approach has achieved extraordinary scientific accomplishments, both in fabricating new materials and structures. However, it is also prone to misstep because (1) many organic structures are multifunctional that have reconciled conflicting individual functional demands (rather than maximize the performance of any one task) over evolutionary time, and (2) some structures are ancillary or entirely superfluous to the functions their associated systems perform. The important point is that we must typically infer function from structure, and that is not always easy to do even when behavioral characteristics are available (e.g., the delivery of venom by the fangs of a snake, or cytoplasmic toxins by the leaf hairs of the stinging nettle). Here, we discuss both of these potential pitfalls by comparing and contrasting how engineered and organic systems are operationally analyzed. We also address the challenges that emerge when an organic system is modeled and suggest a few methods to evaluate the validity of models in general.

Numerical approximations on the transient analysis of bioelectric phenomena at long time scales via the Mittag-Leffler function

• Fractional-order based model for long-tailed transient behavior of biological systems. • Approximations to achieve a “pseudo-steady-state” in non-ideal relaxation processes. • Analysis of rational functions, asymptotically equivalent to Mittag-Leffler pattern for long times. • Application to safe and efficacious protocols of stimulation and electrical characterization. This paper discusses the complexity of distributed relaxation processes in biological systems, particularly with regard to the slowest timescale phenomena that influence the modeling of physiological events. Specifically, our main interest is to determine the optimal excitation time at which the transient response , described in terms of relatively slow decays and memory effects, can be considered negligible. We estimate the time scale required for the Mittag-Leffler function to reach and stay within a range about the final value (dc “pseudo-steady state”). From numerical computations, we consider the problem of approximating holding times with common and rational (Padé-type) asymptotic approximations for comparative purposes. It is important to understand the physiological processes and to explore new mathematical models, based on efficient approximations, in order to design safe, controllable, and effective protocols for the electrical stimulation of excitable cells and the characterization of biological tissues.

Parallel One-Step Control of Parametrised Boolean Networks

Boolean network (BN) is a simple model widely used to study complex dynamic behaviour of biological systems. Nonetheless, it might be difficult to gather enough data to precisely capture the behavior of a biological system into a set of Boolean functions. These issues can be dealt with to some extent using parametrised Boolean networks (ParBNs), as this model allows leaving some update functions unspecified. In our work, we attack the control problem for ParBNs with asynchronous semantics. While there is an extensive work on controlling BNs without parameters, the problem of control for ParBNs has not been in fact addressed yet. The goal of control is to ensure the stabilisation of a system in a given state using as few interventions as possible. There are many ways to control BN dynamics. Here, we consider the one-step approach in which the system is instantaneously perturbed out of its actual state. A naïve approach to handle control of ParBNs is using parameter scan and solve the control problem for each parameter valuation separately using known techniques for non-parametrised BNs. This approach is however highly inefficient as the parameter space of ParBNs grows doubly exponentially in the worst case. We propose a novel semi-symbolic algorithm for the one-step control problem of ParBNs, that builds on symbolic data structures to avoid scanning individual parameters. We evaluate the performance of our approach on real biological models.

Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering.

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains.

Calorimetric and spectroscopic investigation of the interaction of chemotherapeutic agent carboplatin with sphingomyelin lipids

Carboplatin (CAR) is a chemo-therapeutic agent which interacts with DNA to interfer its repair, and thereby to induce apoptosis. Since CAR acts inside cell, its inevitable interaction with membrane lipids directly affects its efficacy. In the current work, we investigated the interaction profile of CAR with sphingomyelin (SM) lipids, which plays key role in different cellular functions, particularly important in cancer. By utilizing Differential Scanning Calorimetry (DSC) and Fourier transform infrared (FT-IR) spectroscopy, the effects of different concentrations of CAR (1-5-10-20-30 mol%) on phase transition temperature (Tm), enthalpy (ΔH), cooperativity (CU), lipid order, lipid fluidity and the strength of hydrogen bonding of SM lipids were monitored. The results revealed that CAR caused a decrease in Tm, ΔH, CU, lipid order, but an increase in hydrogen state of hydrophilic region of SM lipids. On the other hand, a slight decrement in fluidity solely at 30 mol% was monitored. The findings revealed that CAR incorporates into SM bilayers by binding to acyl chains and hydrophilic parts of lipids. In sum, the results may represent valuable information about the effect of CAR on biophysical properties of SM lipids, which may reflect its behavior and action in real biological systems.

Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments.

Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous method of incorporating information from multiple experiments into a single analysis and finding the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.

System dynamics modeling of agricultural land conversion in Aceh Besar District

The purpose of this study is to design a sub-model as a policy strategy alternative in controlling agricultural land conversion. Method applied in this study is system dynamics, a modelling approach based on systems thinking which uses the perspectives of information feedback and delay to understand the complex behavior of physical, biological, and social systems in agricultural land conversion. The study utilized Vensim DSS software to perform the model formulation and the simulation. The computation obtained six main sub-models applicable in the design process of control models for the transfer of functions of agricultural land in Aceh Besar District. The sub-models concern population, settlements, industry, rice fields, forests and fields, and rice stock.

Phenol-triggered supramolecular transformation of titanium–oxo cluster based coordination capsules

Supramolecular transformations of coordination cage or capsule have received much attention recently, which help elucidate this natural self-assembly behavior in biological systems. The current study describes the first supramolecular transformation of titanium–organic coordination capsule triggered by phenol (and H3PO3). The structural alterations are accompanied by the reconstruction of 5-coordinated Ti centers to 6-coordinated ones. Meanwhile, different amounts of encapsulated phenol guest molecules can be identified, dependent on the sizes of the obtained cavities. In addition, they display much better visible light absorption and air stability than the isopropanol stabilized ones.

Mreža protein-protein interakcija i protein-ligand doking - trenutno stanje i perspektive

Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.

If deep learning is the answer, what is the question?

Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains. This approach has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, and not endowed by the researcher. If so, how can neuroscientists use deep networks to model and understand biological brains? What is the outlook for neuroscientists who seek to characterize computations or neural codes, or who wish to understand perception, attention, memory and executive functions? In this Perspective, our goal is to offer a road map for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics and neural representations in artificial and biological systems, and we highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning.

Langevin Dynamic Simulation of self-propelled particles in two-dimensional systems

Collective motion of self-propelled particles is one of basic phenomenon observed in large spectra of biological system behavior due to the correlated process evolution in space and time. In this manuscript, we study numerically the kinetic properties and the correlation process in complex systems evolves out equilibrium phase by employing the Langevin dynamics. In this model, we have adopted one zone of orientation where the system evolves spontaneously in presence of quenched stochastic noise. The results show that the system evolves to its equilibrium phase by reaching one orientation. Hence, this evolution is characterized by a correlation process increasing in time but with decelerate profile. However the obtained profile of the correlation function per time unity shows that the collective motion in complex system, can be characterized by a characteristic time when the system change the acceleration of the correlation process. Our result shows that this characteristic time decreases exponentially with the quenched noise. In the additional crossover time at when the system reaches its equilibrium phase, scales with quenched noise as power law. This result is more consisting with the one of Vicsek model

Training of pharmaceutical personnel: the fundamentals and current trends

The development of biomedical science in recent decades (a personalized approach to the creation of medicines, the emergence of biotechnological and biosimilar drugs, the transition from empirical to directed drug development using drug design methods - information technologies for their modeling and studying their behavior in biological systems) has a significant impact on the formation of the pharmaceutical education system worldwide. New drugs are being developed such as gene therapeutics and biomedical cell products, and biotechnological production of medicines is developing [1-3]. One of the most important tasks of modern pharmaceutical education is the training of highly qualified personnel in the pharmaceutical industry [4-6]. Global trends in pharmaceutical education are aimed at improving educational programs, introducing new disciplines to the educational process in the light of scientific and technological progress in the industry, developing scientific activities, using digital technologies, interacting actively with industry organizations, and internationalizing education. The state policy of the Russian Federation in the field of medical and pharmaceutical education strives to meet these challenges.

A modified Ising model of Barab\xe1si\u2013Albert network with gene-type spins

The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to the emerging behavior of biological systems. Here we systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. We focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose a mean-field solution of a modified Ising model of a network type that closely resembles a real-world network, the Barabási–Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with the external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented herein can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. The method proposed here is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size.

Vortices as Brownian particles in turbulent flows.

Brownian motion of particles in fluid is the most common form of collective behavior in physical and biological systems. Here, we demonstrate through both experiment and numerical simulation that the movement of vortices in a rotating turbulent convective flow resembles that of inertial Brownian particles, i.e., they initially move ballistically and then diffusively after certain critical time. Moreover, the transition from ballistic to diffusive behaviors is direct, as predicted by Langevin, without first going through the hydrodynamic memory regime. The transitional timescale and the diffusivity of the vortices can be collapsed excellently onto a master curve for all explored parameters. In the spatial domain, however, the vortices exhibit organized structures, as if they are performing tethered random motion. Our results imply that the convective vortices have inertia-induced memory such that their short-term movement can be predicted and their motion can be well described in the framework of Brownian motions.

Permutation Entropy of State Transition Networks to Detect Synchronization

The dynamic behavior of many physical, biological, and other systems, are organized according to the synchronization of chaotic oscillators. In this paper, we have proposed a new method with low sensitivity to noise for detecting synchronization by mapping time series to complex networks, called the ordinal partition network, and calculating the permutation entropy of that structure. We show that this method can detect different kinds of synchronization such as complete synchronization, phase synchronization, and generalized synchronization. In all cases, the estimated permutation entropy decreases with increased synchronization. This method is also capable of estimating the topology of the network graph from the time series, without knowledge of the dynamical equations of individual nodes. This approach has been applied for the two identical and nonidentical coupled Rössler systems, two nonidentical coupled Lorenz systems, and a ring of coupled Lorenz96 oscillators.

Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment

Background and objectiveFor decades, mathematical models have been used to predict the behavior of physical and biological systems, as well as to define strategies aiming at the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of the novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the objective is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China. Two optimal control problems (mono- and multi-objective) to determine a strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment. MethodsAn inverse problem is formulated and solved in order to determine the parameters of the compartmental Susceptible-Infectious-Removed model. The solutions for both optimal control problems proposed are obtained by using Differential Evolution and Multi-objective Optimization Differential Evolution algorithms. ResultsA comparative analysis on the influence related to the inclusion of a control strategy in the population subject to the epidemic is carried out, in terms of the compartmental model and its control parameters. The results regarding the proposed optimal control problems provide information from which an optimal strategy for vaccine administration can be defined. ConclusionsThe solution of the optimal control problem can provide information about the effect of vaccination of a population in the face of an epidemic, as well as essential elements for decision making in the economic and governmental spheres.

Influence of the Turing instability on the motion of domain boundaries.

Turing's theory of pattern formation has provided crucial insights into the behavior of various biological, geographical, and chemical systems over the last few decades. Existing studies have focused on moving-boundary Turing systems for which the motion of the boundary is prescribed by an external agent. In this paper, we present an extension of this theory to a class of systems in which the front motion is governed by the physical processes that occur within the domain. Biological systems exhibiting apically dominant growth and corrosion of metals and alloys highlight some of the noteworthy examples of such systems. In this study, we characterize the nature of interaction between the moving front and the Turing-instability for both an activator-inhibitor and an activator-substrate model. Behavioral regimes of periodic, as well as nonperiodic (nonconstant), growth rates are obtained. Furthermore, the trends in the first show striking similarities with the cyclic-boundary-kinetics observed in experimental systems. In general, a stationary, periodic structure is also left behind the moving front. If the periodicity of the boundary kinetics agrees with the allowed range of the stable-periodic solutions, the pattern formed tends to persist. Otherwise, it evolves to a nearby energy-minimum either by peak-splitting, peak-decay, or by settling down to a spatially homogeneous state.

Discrete Type SIR Epidemic Model with Nonlinear Incidence Rate in Presence of Immunity

Mathematical modeling is very important to describe the dynamic behavior of biological and biomedical systems. The SIR model is the most common mathematical model of epidemics. An epidemic occurs if the number of people infected with a disease is increasing in a population. A numerical discretization for an SIR epidemic model is discussed, where the incidence rate is assumed to be Beddington-DeAngelis type. In particular, we reconsider a SIR epidemic model with Non Linear incidence and treatment rate derived by (Dubey et al. 2015) [1]. We applied Euler method to discretize this model. This discretization leads to a numerical scheme which can be considered as a discrete system. Then we analyzed the dynamics of the obtained discrete system. We developed the model with the focus on the concentration of the basic reproduction number and related stability analysis for the disease-free and endemic equilibrium points. Finally, We have performed numerical simulations to illustrate the disease behavior