Search Results (58)

Cell membrane capacitance (C_m) is a potential biomarker that reflects the structural and functional integrity of cell membranes. It is essential for physiological processes such as signal transduction, ion transport, and cellular homeostasis. In clinical practice, C_m can be determined using bioimpedance spectroscopy (BIS), a non-invasive technique for analysing the intrinsic electrical properties of biological tissues across a range of frequencies. C_m may be relevant in various clinical fields, where high capacitance is associated with healthy and intact membranes, while low capacitance indicates cellular damage or disease. Despite its promise as a prognostic indicator, several knowledge gaps limit the broader clinical application of C_m. These include variability in measurement techniques (e.g., electrode placement, frequency selection), the lack of standardised measurement protocols, uncertainty on how C_m is related to pathology, and the relatively low amount of C_m research. By addressing these gaps, C_m may become a valuable tool for examining cellular health, early disease detection, and evaluating treatment efficacy in clinical practice. This review explores the fundamental principles of C_m measured with the BIS technique, its mathematical basis and relationship to the biophysical Cole model, and its potential clinical applications. It identifies current gaps in our knowledge and outlines future research directions to enhance the understanding and use of C_m. For example, C_m has shown promise in identifying membrane degradation in sepsis, predicting malnutrition in anorexia nervosa, and as a prognostic factor in cancer. Full article

Cell membranes contain a variety of biomolecules, especially various kinds of lipids and proteins, which constantly change with fluidity and environmental stimuli. Though Helfrich curvature elastic energy has successfully explained many phenomena for single-component membranes, a new theoretical framework for multicomponent membranes is still a challenge. In this work, we propose a generalized Helfrich free-energy functional describe equilibrium shapes and phase behaviors related to membrane heterogeneity with via curvature-component coupling in a unified framework. For multicomponent membranes, a new but important Laplace–Beltrami operator is derived from the variational calculation on the integral of Gaussian curvature and applied to explain the spontaneous nanotube formation of an asymmetric glycolipid vesicle. Therefore, our general mathematical framework shows a predictive capabilities beyond the existing multicomponent membrane models. The set of new curvature-component coupling EL equations have been derived for global vesicle shapes associated with the composition redistribution of multicomponent membranes for the first time and specified into several typical geometric shape equations. The equilibrium radii of isotonic vesicles for both spherical and cylindrical geometries are calculated. The analytical solution for isotonic vesicles reveals that membrane stability requires distinct elastic moduli among components ($k_{\mathrm{A}}\ne k_{\mathrm{B}}$, ${\overline{k}}_{\mathrm{A}}\ne {\overline{k}}_{\mathrm{B}}$), which is consistent with experimental observations of coexisting lipid domains. Furthermore, we elucidate the biophysical implications of the derived shape equations, linking them to experimentally observed membrane remodeling processes. Our new free-energy framework provides a baseline for more detailed microscopic membrane models. Full article

Introduction: Intermittent claudication, an early symptom of peripheral artery disease, can be treated by cilostazol to alleviate symptoms and improve walking distance. Our previous investigation focused on cilostazol-induced alterations in the thermodynamic properties of plasma, utilizing differential scanning calorimetry (DSC) as a potential monitoring tool. The current proof-of-concept study aimed to enhance the interpretation of DSC data through deconvolution techniques, specifically examining protein transitions within the plasma proteome during cilostazol therapy. Results: Notable differences in thermal unfolding profiles were found between cilostazol-treated patients and healthy controls. The fibrinogen-associated transition exhibited a downward shift in denaturation temperature and decreased enthalpy by the third month. The albumin-related transition shifted to higher temperatures, accompanied by lower enthalpy. Transitions associated with globulins showed changes in thermal stability, while the transferrin-related peak demonstrated increased structural rigidity in treated patients compared to controls. Discussion: These observations suggest that cilostazol induces systemic changes in the thermodynamic behavior of plasma proteins. DSC, when combined with deconvolution methods, presents a promising approach for detecting subtle, therapy-related alterations in plasma protein stability. Materials and methods: Ten patients (median age: 58.6 years) received 100 milligrams of cilostazol twice daily. Blood samples were collected at the baseline and after 2 weeks, 1 month, 2 months, and 3 months of therapy. Walking distances were also assessed. The DSC curves were retrieved from the thermal analysis investigated by deconvolution mathematical methods. Conclusions: Although the exact functional consequences remain unclear, the observed biophysical changes may reflect broader molecular adaptations involving protein–protein interactions, post-translational modifications, or acute phase response elements. Full article

The development of ion exchange chromatography to polish biopharmaceuticals requires extensive experimental benchmarking. As part of the Design of Experiments (DoE), statistical models increased efficiency somewhat and are still state of the art; however, the capability to predict process conditions is limited due to their nature as interpolating models. Applying the DoE still requires numerous experiments and is constrained to the design space, posing a risk of missing the potential optimum. To make a leap in model-based process development, applying extrapolating models can tremendously extend the design space and also allow for process understanding and knowledge transfer. While existing chromatography modeling software explains experimental data, it often lacks predictive power for new conditions. In academic–industrial cooperation, we demonstrate a new high-fidelity model based on biophysics for developing ion-exchange chromatography in biomanufacturing, making it a general tool in rationalizing process development for the present demand of recombinant proteins and monoclonal antibodies and the emerging demand of new modalities. Using the new computational tool, we achieved predictability and attained high accuracy; with minimal experimental effort to calibrate the system, the mathematical model predicted sensitive process conditions, and even described product-related impurities, antibody charge variants. Thus, the computational tool can be deployed for process-by-design and material-by-design approaches. Full article

Motivated by important biophysical applications, we study the stochastic version of a mathematical model of calcium oscillations. For the deterministic model proposed by Li and Rinzel, a parametric zone of tristability, where two stable equilibria and a limit cycle coexist, is found for the first time. In this zone, and also in adjacent bi- and monostability zones, different scenarios of noise-induced generation and suppression of complex calcium oscillations are studied in detail. In these studies, along with the traditional direct numerical simulation and statistical processing, a new analytical apparatus of the stochastic sensitivity technique and confidence domains is effectively used. Full article

Cardiovascular diseases have become the leading cause of death in developed countries. Among these, some are related to disruptions in the electrical synchronization of cardiac tissue leading to arrhythmias such as atrial flutter, ventricular tachycardia, or ventricular fibrillation. Their origin is diverse and involves several spatial and temporal scales, ranging from nanoscale ion channel dysfunctions to tissue-level fibrosis and ischemia. Mathematical models play a crucial role in elucidating the mechanisms underlying cardiac arrhythmias by simulating the electrical and physiological properties of cardiac tissue across different spatial scales. These models investigate the effects of genetic mutations, pathological conditions, and anti-arrhythmic interventions on heart dynamics. Despite their varying levels of complexity, they have proven to be important in understanding the triggers of arrhythmia, optimizing defibrillation protocols, and exploring the nonlinear dynamics of cardiac electrophysiology. In this work, we present diverse modeling approaches to the electrophysiology of cardiac cells and share examples from our own research where these approaches have significantly contributed to understanding cardiac arrhythmias. Although computational modeling of the electrical properties of cardiac tissue faces challenges in integrating data across multiple spatial and temporal scales, it remains an indispensable tool for advancing knowledge in cardiac biophysics and improving therapeutic strategies. Full article

This study presents a mathematical analysis of the collective effect of chemical reactions, variable fluid properties, and thermal stability of a hydromagnetic couple-stress fluid flowing through a microchannel driven by electro-osmosis and a pressure gradient. The viscosity of the biofluid is assumed to depend on the temperature, while the electrical conductivity is assumed to be a linear function of the drift velocity. The governing equations are derived non-dimensionalized, and numerical solutions are obtained using the spectral Chebyshev collocation method. The numerical solution is validated using the shooting Runge–Kutta method. The effects of varying the parameters on the thermal stability, temperature, velocity, and entropy profiles are discussed with adequate interpretations using tables and graphs. The results reveal that the chemical reactions and viscosity parameter increase the fluid temperature, while the Hartmann number decreases the temperature and increases the flow velocity and entropy generation. It was also observed that the chemical reactions and viscosity parameter increased the entropy at the channel walls, while the Hartmann number decreased the entropy at the core center of the channel. This study has tremendous empirical significance, including but not limited to biophysical applications of devices, engineering applications such as control systems, and thermo-fluidic transport. Full article

Cells compartmentalize biochemical processes using physical barriers in the form of membranes. Eukaryotes have a wide diversity of membrane-based compartments that can be used in this context, with the main ones being the extracellular membrane, which separates the inside from the outside of the cell, and the nuclear membrane, which separates the nucleus from the cytoplasm. The nuclear membrane not only isolates and protects the DNA and the transcription and replication processes from the other processes that are occurring in the cytoplasm but also has an active role in the regulation of cellular signaling. The TGF-$\beta$ pathway is one of the most important and conserved signaling cascades, and it achieves compartmentalization using a well-tuned balance between the import and export rates of the active and inactive forms of key proteins. Thus, compartmentalization serves as an additional regulatory mechanism, physically isolating transcription factors from their targets, influencing the dynamics and strength of signal transduction. This contribution focuses on this biophysical layer of regulation, using the TGF-$\beta$ pathway to illustrate the molecular mechanisms underlying this process, as well as the biological consequences of this compartmentalization. We also introduce a simplified mathematical formulation for studying the dynamics of this process using a generalized approach. Full article

Recent studies of the spread of substances penetrating the disrupted blood–brain barrier have revealed that the spread in the parenchyma surrounding a vessel has a complex character. In particular, a flow-like motion occurred for a short time that exhibits a smooth transition to diffusional spread. To address the possible physical background of such behavior, we created a system formed by a hydrogel medium with a channel filled by a marker solution, which can serve as a physical model mimicking the process of a substance passively spreading to the brain’s parenchyma when the blood–brain barrier is disrupted. The key result obtained in this work consists of the conclusion that the above-mentioned two-stage character of the spread process discovered in a previous biophysical experiment on the blood–brain opening in a living mouse may originate from the specificity of transport in porous soft matter with relaxation. We propose a mathematical model based on the extended Cattaneo equation, which reproduces our experimental data; determines the crossover time coinciding with that found in the biological system; and, therefore, provides a means of interpretation of this phenomenon. Full article

The mathematical modeling of multicellular systems is an important branch of biophysics, which focuses on how the system properties emerge from the elementary interaction between the constituent elements. Recently, mathematical structures have been proposed within the thermostatted kinetic theory for the modeling of complex living systems and have been profitably employed for the modeling of various complex biological systems at the cellular scale. This paper deals with a class of generalized thermostatted kinetic theory frameworks that can stand in as background paradigms for the derivation of specific models in biophysics. Specifically, the fundamental homogeneous thermostatted kinetic theory structures of the recent literature are recovered and generalized in order to take into consideration further phenomena in biology. The generalizations concern the conservative, the nonconservative, and the mutative interactions between the inner system and the outer environment. In order to sustain the strength of the new structures, some specific models of the literature are reset into the style of the new frameworks of the thermostatted kinetic theory. The selected models deal with breast cancer, genetic mutations, immune system response, and skin fibrosis. Future research directions from the theoretical and modeling viewpoints are discussed in the whole paper and are mainly devoted to the well-posedness in the Hadamard sense of the related initial boundary value problems, to the spatial–velocity dynamics and to the derivation of macroscopic-scale dynamics. Full article

Elevated blood glucose levels, known as hyperglycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment computational model of the human pacemaker action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on pacemaker action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the pacemaker action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death. Full article

During early development of biopharmaceuticals, suboptimal producing clones and production conditions can result in limited quantities of high-purity products. Here we describe a systematic approach, which requires minimal amounts of protein (~10 mg) to assess critical quality attributes of a monoclonal antibody (mAb). A commercial anti-PCSK9 IgG2 (evolocumab, Repatha^®) and an early-stage biosimilar candidate were compared head-to-head using a range of high-throughput physicochemical and in-vitro binding analytical methods. Overall, both mAbs were shown to be highly pure and primarily monomeric, to share an identical primary structure, and to have similar higher-order structural integrity, apparent solubility, aggregation propensity, and physical stability profiles under temperature and pH stress conditions. Low levels of dimers were detected for the innovator (1.2%) and the biosimilar candidate mAb (0.3%), which also presented fragments (1.2%). Regarding charge heterogeneity, the amount of the main charge isoform was 53.6% for the innovator and 61.6% for the biosimilar candidate mAb. Acidic species were 38% for the innovator and 30% for the biosimilar candidate. Variations in the relative content of a few N-glycan species were found. The in-vitro binding affinity to PCSK9 was monitored, and no differences were detected. The mathematical approach called “error spectral difference” (ESD), proposed herein, enabled a quantitative comparison of the biophysical datasets. The workflow used in the present work to characterize CQAs at early stages is helpful in supporting the development of biosimilar mAb candidates. Full article

Mathematical models such as Fitzhugh–Nagoma and Hodgkin–Huxley models have been used to understand complex nervous systems. Still, due to their complexity, these models have made it challenging to analyze neural function. The discrete Rulkov model allows the analysis of neural function to facilitate the investigation of neuronal dynamics or others. This paper introduces a fractional memristor Rulkov neuron model and analyzes its dynamic effects, investigating how to improve neuron models by combining discrete memristors and fractional derivatives. These improvements include the more accurate generation of heritable properties compared to full-order models, the treatment of dynamic firing activity at multiple time scales for a single neuron, and the better performance of firing frequency responses in fractional designs compared to integer models. Initially, we combined a Rulkov neuron model with a memristor and evaluated all system parameters using bifurcation diagrams and the 0–1 chaos test. Subsequently, we applied a discrete fractional-order approach to the Rulkov memristor map. We investigated the impact of all parameters and the fractional order on the model and observed that the system exhibited various behaviors, including tonic firing, periodic firing, and chaotic firing. We also found that the more I tend towards the correct order, the more chaotic modes in the range of parameters. Following this, we coupled the proposed model with a similar one and assessed how the fractional order influences synchronization. Our results demonstrated that the fractional order significantly improves synchronization. The results of this research emphasize that the combination of memristor and discrete neurons provides an effective tool for modeling and estimating biophysical effects in neurons and artificial neural networks. Full article

The closed-loop control of pathological brain activity is a challenging task. In this study, we investigated the sensitivity of continuous epileptiform short discharge generation to electrical stimulation applied at different phases between the discharges using an in vitro 4-AP-based model of epilepsy in rat hippocampal slices. As a measure of stimulation effectiveness, we introduced a sensitivity function, which we then measured in experiments and analyzed with different biophysical and abstract mathematical models, namely, (i) the two-order subsystem of our previous Epileptor-2 model, describing short discharge generation governed by synaptic resource dynamics; (ii) a similar model governed by shunting conductance dynamics (Epileptor-2B); (iii) the stochastic leaky integrate-and-fire (LIF)-like model applied for the network; (iv) the LIF model with potassium M-channels (LIF+KM), belonging to Class II of excitability; and (v) the Epileptor-2B model with after-spike depolarization. A semi-analytic method was proposed for calculating the interspike interval (ISI) distribution and the sensitivity function in LIF and LIF+KM models, which provided parametric analysis. Sensitivity was found to increase with phase for all models except the last one. The Epileptor-2B model is favored over other models for subthreshold oscillations in the presence of large noise, based on the comparison of ISI statistics and sensitivity functions with experimental data. This study also emphasizes the stochastic nature of epileptiform discharge generation and the greater effectiveness of closed-loop stimulation in later phases of ISIs. Full article

Ketoprofen (KTF) and ketorolac (KTL) are among the most primarily used non-steroidal anti-inflammatory drugs (NSAIDs) in humans to alleviate moderate pain and to treat inflammation. Their binding affinity with albumin (the main globular protein responsible for the biodistribution of drugs in the bloodstream) was previously determined by spectroscopy without considering some conventional pitfalls. Thus, the present work updates the biophysical characterization of the interactions of HSA:KTF and HSA:KTL by ¹H saturation-transfer difference nuclear magnetic resonance (¹H STD-NMR), ultraviolet (UV) absorption, circular dichroism (CD), steady-state, and time-resolved fluorescence spectroscopies combined with in silico calculations. The binding of HSA:NSAIDs is spontaneous, endothermic, and entropically driven, leading to a conformational rearrangement of HSA with a slight decrease in the α-helix content (7.1% to 7.6%). The predominance of the static quenching mechanism (ground-state association) was identified. Thus, both Stern–Volmer quenching constant (K_SV) and binding constant (K_b) values enabled the determination of the binding affinity. In this sense, the K_SV and K_b values were found in the order of 10⁴ M⁻¹ at human body temperature, indicating moderate binding affinity with differences in the range of 0.7- and 3.4-fold between KTF and KTL, which agree with the previously reported experimental pharmacokinetic profile. According to ¹H STD-NMR data combined with in silico calculations, the aromatic groups in relation to the aliphatic moiety of the drugs interact preferentially with HSA into subdomain IIIA (site II) and are stabilized by interactions via hydrogen bonding and hydrophobic forces. In general, the data obtained in this study have been revised and updated in comparison to those previously reported by other authors who did not account for inner filter corrections, spectral backgrounds, or the identification of the primary mathematical approach for determining the binding affinity of HSA:KTF and HSA:KTL. Full article