Refinement of cryo-EM 3D maps with a self-supervised denoising model: crefDenoiser.

Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the reconstructed 3D protein-density maps are often limited in quality due to residual noise, which in turn affects the accuracy of the macromolecular representation. Here, crefDenoiser is introduced, a denoising neural network model designed to enhance the signal in 3D cryo-EM maps produced with standard processing pipelines. The crefDenoiser model is trained without the need for `clean' ground-truth target maps. Instead, a custom dataset is employed, composed of real noisy protein half-maps sourced from the Electron Microscopy Data Bank repository. Competing with the current state-of-the-art, crefDenoiser is designed to optimize for the theoretical noise-free map during self-supervised training. We demonstrate that our model successfully amplifies the signal across a wide variety of protein maps, outperforming a classic map denoiser and following a network-based sharpening model. Without biasing the map, the proposed denoising method leads to improved visibility of protein structural features, including protein domains, secondary structure elements and modest high-resolution feature restoration.

Empagliflozin impact on experimentally induced acetaminophen toxicity: Imprint of mitochondrial dynamics, biogenesis, and cGAS/STING signal in amending liver insult.

Acetaminophen (APAP) is one of the most clinically relevant medications associated with acute liver damage. A prolific deal of research validated the hepatoprotective effect of empagliflozin (EMPA); however, its effect on APAP-induced hepatotoxicity has still not been investigated. In this study, the prospective hepatoprotective impact of EMPA against APAP-induced hepatotoxicity was investigated. Twenty-eight Balb-C mice were assigned to four groups: control, APAP, EMPA10/APAP, and EMPA25/APAP. At the end of the experiment, serum hepatotoxicity biomarkers, MDA level, and GSH content were estimated. Hepatic mitofusin-2 (MFN2), optic atrophy 1 (OPA1), dynamin-related protein 1 (Drp1), and mitochondrial fission 1 protein (FIS1) were immunoassayed. PGC-1α, cGAS, and STING mRNA expression were assessed by real-time PCR. Histopathological changes and immunohistochemistry of INF-ß, p-NF-κB, and iNOS were evaluated. APAP treatment caused significant hepatic functional impairment and increased hepatic MDA levels, as well as a concomitant decrease in GSH content. Marked elevation in Drp1 and FIS1 levels, INF-ß, p-NF-κB, and iNOS immunoreactivity, and reduction in MFN2 and OPA1 levels in the APAP-injected group, PGC-1α downregulation, and high expression of cGAS and STING were also documented. EMPA effectively ameliorated APAP-generated structural and functional changes in the liver, restored redox homeostasis and mitochondrial dynamics balance, and enhanced mitochondrial biogenesis, remarkably diminished hepatic expression of cGAS and STING, and elicited a reduction in hepatic inflammation. Moreover, the computational modeling data support the interaction of APAP with antioxidant system-related proteins as well as the interactions of EMPA against Drp1, cGAS, IKKA, and iNOS proteins. Our findings demonstrated for the first time that EMPA has an ameliorative impact against APAP-induced hepatotoxicity in mice via modulation of mitochondrial dynamics, biogenesis, and cGAS/STING-dependent inflammation. Thus, this study concluded that EMPA could be a promising therapeutic modality for acute liver toxicity.

Comparison of Left Ventricular Function Derived from Subject-Specific Inverse Finite Element Modeling Based on 3D ECHO and Magnetic Resonance Images.

Three-dimensional echocardiography (3D ECHO) and magnetic resonance (MR) imaging are frequently used in patients and animals to evaluate heart functions. Inverse finite element (FE) modeling is increasingly applied to MR images to quantify left ventricular (LV) function and estimate myocardial contractility and other cardiac biomarkers. It remains unclear, however, as to whether myocardial contractility derived from the inverse FE model based on 3D ECHO images is comparable to that derived from MR images. To address this issue, we developed a subject-specific inverse FE model based on 3D ECHO and MR images acquired from seven healthy swine models to investigate if there are differences in myocardial contractility and LV geometrical features derived using these two imaging modalities. We showed that end-systolic and end-diastolic volumes derived from 3D ECHO images are comparable to those derived from MR images (R2=0.805 and 0.969, respectively). As a result, ejection fraction from 3D ECHO and MR images are linearly correlated (R2=0.977) with the limit of agreement (LOA) ranging from -17.95% to 45.89%. Using an inverse FE modeling to fit pressure and volume waveforms in subject-specific LV geometry reconstructed from 3D ECHO and MR images, we found that myocardial contractility derived from these two imaging modalities are linearly correlated with an R2 value of 0.989, a gradient of 0.895, and LOA ranging from -6.11% to 36.66%. This finding supports using 3D ECHO images in image-based inverse FE modeling to estimate myocardial contractility.

pyCEPS: A cross-platform electroanatomic mapping data to computational model conversion platform for the calibration of digital twin models of cardiac electrophysiology.

BACKGROUND AND OBJECTIVE: Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be viewed and analyzed with commonly used cardiac simulation software tools such as openCARP remains challenging. We therefore developed an open-source platform, pyCEPS, for parsing and converting clinical EAM data conveniently to standard formats widely adopted within the cardiac modeling community. METHODS AND RESULTS: pyCEPS is an open-source Python-based platform providing the following functions: (i) access and interrogate the EAM data exported from clinical mapping systems; (ii) efficient browsing of EAM data to preview mapping procedures, electrograms (EGMs), and electro-cardiograms (ECGs); (iii) conversion to modeling formats according to the openCARP standard, to be amenable to analysis with standard tools and advanced workflows as used for in silico EAM data. Documentation and training material to facilitate access to this complementary research tool for new users is provided. We describe the technological underpinnings and demonstrate the capabilities of pyCEPS first, and showcase its use in an exemplary modeling application where we use clinical imaging data to build a patient-specific anatomical model. CONCLUSION: With pyCEPS we offer an open-source framework for accessing EAM data, and converting these to cardiac modeling standard formats. pyCEPS provides the core functionality needed to integrate EAM data in cardiac modeling research. We detail how pyCEPS could be integrated into model calibration workflows facilitating the calibration of a computational model based on EAM data.

Functional Assessment of Cerebral Capillaries using Single Capillary Reporters in Ultrasound Localization Microscopy.

The brain's microvascular cerebral capillary network plays a vital role in maintaining neuronal health, yet capillary dynamics are still not well understood due to limitations in existing imaging techniques. Here, we present Single Capillary Reporters (SCaRe) for transcranial Ultrasound Localization Microscopy (ULM), a novel approach enabling non-invasive, whole-brain mapping of single capillaries and estimates of their transit-time as a neurovascular biomarker. We accomplish this first through computational Monte Carlo and ultrasound simulations of microbubbles flowing through a fully-connected capillary network. We unveil distinct capillary flow behaviors which informs methodological changes to ULM acquisitions to better capture capillaries in vivo. Subsequently, applying SCaRe-ULM in vivo, we achieve unprecedented visualization of single capillary tracks across brain regions, analysis of layer-specific capillary heterogeneous transit times (CHT), and characterization of whole microbubble trajectories from arterioles to venules. Lastly, we evaluate capillary biomarkers using injected lipopolysaccharide to induce systemic neuroinflammation and track the increase in SCaRe-ULM CHT, demonstrating the capability to detect subtle capillary functional changes. SCaRe-ULM represents a significant advance in studying microvascular dynamics, offering novel avenues for investigating capillary patterns in neurological disorders and potential diagnostic applications.

Computational Modeling Interpretation Underlying Elevated Risk-Taking Propensity in the Dynamic Risky Investment Process of Non-Labor Income.

Introduction: Money source influences risk-taking behaviors. Although studies consistently indicated that individuals demonstrate a higher propensity to make risky investments when utilizing non-labor income as opposed to labor income, explanations as to why non-labor income leads to continuously blowing money into risky investments are scarce. Methods: The current study leverages a computational modeling approach to compare the differences in the dynamic risk investment process among individuals endowed with income from different sources (ie, non-labor income vs labor income) to understand the shaping force of higher risk-taking propensity in individuals with non-labor income. A total of 103 participants were recruited and completed the Balloon Analogue Risk Task (BART) with an equal monetary endowment, either as a token for completion of survey questionnaires (representing labor income) or as a prize from a lucky draw game (representing non-labor income). Results: We found that individuals endowed with non-labor income made more risky investments in BART compared to those with labor income. With computational modeling, we further identified two key differences in the dynamic risk investment processes between individuals endowed with labor and those with non-labor income. Specifically, individuals endowed with non-labor income had a higher preset expectation for risk-taking and displayed desensitization towards losses during risk investments, in contrast to individuals with labor income. Discussion: This study contributed to a better understanding of the psychological mechanisms of why individuals make more risk-taking behaviors with non-labor income, namely higher preset expectations of risk-taking and desensitization towards losses. Future research could validate these findings across diverse samples with varying backgrounds and adopt different manipulations of labor and non-labor income to enhance the external validity of our study.

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space.

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

Standard models of spatial vision mispredict edge sensitivity at low spatial frequencies.

One well-established characteristic of early visual processing is the contrast sensitivity function (CSF) which describes how sensitivity varies with the spatial frequency (SF) content of the visual input. The CSF prompted the development of a now standard model of spatial vision. It represents the visual input by activity in orientation- and SF selective channels which are nonlinearly recombined to predict a perceptual decision. The standard spatial vision model has been extensively tested with sinusoidal gratings at low contrast because their narrow SF spectra isolate the underlying SF selective mechanisms. It is less studied how well these mechanisms account for sensitivity to more behaviourally relevant stimuli such as sharp edges at high contrast (i.e. object boundaries) which abound in the natural environment and have broader SF spectra. Here, we probe sensitivity to edges (2-AFC, edge localization) in the presence of broadband and narrowband noises. We use Cornsweet luminance profiles with peak frequencies at 0.5, 3 and 9 cpd as edge stimuli. To test how well mechanisms underlying sinusoidal contrast sensitivity can account for edge sensitivity, we implement a single- and a multi-scale model building upon standard spatial vision model components. Both models account for most of the data but also systematically deviate in their predictions, particularly in the presence of pink noise and for the lowest SF edge. These deviations might indicate a transition from contrast- to luminance-based detection at low SFs. Alternatively, they might point to a missing component in current spatial vision models.

Texture tomography, a versatile framework to study crystalline texture in 3D.

Crystallographic texture is a key organization feature of many technical and biological materials. In these materials, especially hierarchically structured ones, the preferential alignment of the nano constituents heavily influences the macroscopic behavior of the material. To study local crystallographic texture with both high spatial and angular resolution, we developed Texture Tomography (TexTOM). This approach allows the user to model the diffraction data of polycrystalline materials using the full reciprocal space of the crystal ensemble and describe the texture in each voxel via an orientation distribution function, hence it provides 3D reconstructions of the local texture by measuring the probabilities of all crystal orientations. The TexTOM approach addresses limitations associated with existing models: it correlates the intensities from several Bragg reflections, thus reducing ambiguities resulting from symmetry. Further, it yields quantitative probability distributions of local real space crystal orientations without further assumptions about the sample structure. Finally, its efficient mathematical formulation enables reconstructions faster than the time scale of the experiment. This manuscript presents the mathematical model, the inversion strategy and its current experimental implementation. We show characterizations of simulated data as well as experimental data obtained from a synthetic, inorganic model sample: the silica-witherite biomorph. TexTOM provides a versatile framework to reconstruct 3D quantitative texture information for polycrystalline samples; it opens the door for unprecedented insights into the nanostructural makeup of natural and technical materials.

Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases.

Disease risk prediction based on genomic sequence and transcriptional profile can improve disease screening and prevention. Despite identifying many disease-associated DNA variants, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. In this study, we designed in vitro experiments to uncover the significance of occupancy and competitive binding between P53 and cMYC on common target genes. Analyzing publicly available ChIP-seq data for P53 and cMYC in embryonic stem cells showed that ~344-366 regions are co-occupied, and on average, two cis-overlapping motifs (CisOMs) per region were identified, suggesting that co-occupancy is evolutionarily conserved. Using U2OS and Raji cells untreated and treated with doxorubicin to increase P53 protein level while potentially reducing cMYC level, ChIP-seq analysis illustrated that around 16 to 922 genomic regions were co-occupied by P53 and cMYC, and substitutions of cMYC signals by P53 were detected post doxorubicin treatment. Around 187 expressed genes near co-occupied regions were altered at mRNA level according to RNA-seq data analysis. We utilized a computational motif-matching approach to illustrate that changes in predicted P53 binding affinity in CisOMs of co-occupied elements significantly correlate with alterations in reporter gene expression. We performed a similar analysis using SNPs mapped in CisOMs for P53 and cMYC from ChIP-seq data, and expression of target genes from GTEx portal. We found significant correlation between change in cMYC-motif binding affinity in CisOMs and altered expression. Our study brings us closer to developing a generally applicable approach to filter etiological non-coding variations associated with common diseases.

Concurrent behavioral modeling and multimodal neuroimaging reveals how feedback affects the performance of decision making in internet gaming disorder.

Internet gaming disorder (IGD) prompts inquiry into how feedback from prior gaming rounds influences subsequent risk-taking behavior and potential neural mechanisms. Forty-two participants, including 15 with IGD and 27 health controls (HCs), underwent a sequential risk-taking task. Hierarchy Bayesian modeling was adopted to measure risky propensity, behavioral consistence, and affection by emotion ratings from last trial. Concurrent electroencephalogram and functional near-infrared spectroscopy (EEG-fNIRS) recordings were performed to demonstrate when, where and how the previous-round feedback affects the decision making to the next round. We discovered that the IGD illustrated heightened risk-taking propensity as compared to the HCs, indicating by the computational modeling (p = 0.028). EEG results also showed significant time window differences in univariate and multivariate pattern analysis between the IGD and HCs after the loss of the game. Further, reduced brain activation in the prefrontal cortex during the task was detected in IGD as compared to that of the control group. The findings underscore the importance of understanding the aberrant decision-making processes in IGD and suggest potential implications for future interventions and treatments aimed at addressing this behavioral addiction.

Identification of Distinct, Quantitative Pattern Classes from Emergent Tissue-Scale hiPSC Bioelectric Properties.

Bioelectric signals possess the ability to robustly control and manipulate patterning during embryogenesis and tissue-level regeneration. Endogenous local and global electric fields function as a spatial 'pre-pattern', controlling cell fates and tissue-scale anatomical boundaries; however, the mechanisms facilitating these robust multiscale outcomes are poorly characterized. Computational modeling addresses the need to predict in vitro patterning behavior and further elucidate the roles of cellular bioelectric signaling components in patterning outcomes. Here, we modified a previously designed image pattern recognition algorithm to distinguish unique spatial features of simulated non-excitable bioelectric patterns under distinct cell culture conditions. This algorithm was applied to comparisons between simulated patterns and experimental microscopy images of membrane potential (Vmem) across cultured human iPSC colonies. Furthermore, we extended the prediction to a novel co-culture condition in which cell sub-populations possessing different ionic fluxes were simulated; the defining spatial features were recapitulated in vitro with genetically modified colonies. These results collectively inform strategies for modeling multiscale spatial characteristics that emerge in multicellular systems, characterizing the molecular contributions to heterogeneity of membrane potential in non-excitable cells, and enabling downstream engineered bioelectrical tissue design.

The relation between aortic morphology and transcatheter aortic heart valve thrombosis: Particle tracing and platelet activation in larger aortic roots with and without neo-sinus.

Transcatheter aortic heart valve thrombosis (THVT) affects long-term valve durability, transvalvular pressure gradient and leaflet mobility. In this study, we conduct high-fidelity fluid-structure interaction simulations to perform Lagrangian particle tracing in a generic model with larger aortic diameters (THVT model) with and without neo-sinus which is compared to a model of unaffected TAVI patients (control model). Platelet activation indices are computed for each particle to assess the risk of thrombus formation induced by high shear stresses followed by flow stagnation. Particle tracing indicates that fewer particles contribute to sinus washout of the THVT model with and without neo-sinus compared to the control model (-34.9%/-34.1%). Stagnating particles in the native sinus of the THVT model show higher platelet activation indices than for the control model (+39.6% without neo-sinus, +45.3% with neo-sinus). Highest activation indices are present for particles stagnating in the neo-sinus of the larger aorta representing THVT patients (+80.2% compared to control). This fluid-structure interaction (FSI) study suggests that larger aortas lead to less efficient sinus washout in combination with higher risk of platelet activation among stagnating particles, especially within the neo-sinus. This could explain (a) a higher occurrence of thrombus formation in transcatheter valves compared to surgical valves without neo-sinus and (b) the neo-sinus as the prevalent region for thrombi in TAV. Pre-procedural identification of larger aortic roots could contribute to better risk assessment of patients and improved selection of a patient-specific anti-coagulation therapy.

Directed exploration is elevated in affective disorders but reduced by an aversive interoceptive state induction.

Elevated anxiety and uncertainty avoidance are known to exacerbate maladaptive choice in individuals with affective disorders. However, the differential roles of state vs. trait anxiety remain unclear, and underlying computational mechanisms have not been thoroughly characterized. In the present study, we investigated how a somatic (interoceptive) state anxiety induction influences learning and decision-making under uncertainty in individuals with clinically significant levels of trait anxiety. A sample of 58 healthy comparisons (HCs) and 61 individuals with affective disorders (iADs; i.e., depression and/or anxiety) completed a previously validated explore-exploit decision task, with and without an added breathing resistance manipulation designed to induce state anxiety. Computational modeling revealed a pattern in which iADs showed greater information-seeking (i.e., directed exploration; Cohen's d=.39, p=.039) in resting conditions, but that this was reduced by the anxiety induction. The affective disorders group also showed slower learning rates across conditions (Cohen's d=.52, p=.003), suggesting more persistent uncertainty. These findings highlight a complex interplay between trait anxiety and state anxiety. Specifically, while elevated trait anxiety is associated with persistent uncertainty, acute somatic anxiety can paradoxically curtail exploratory behaviors, potentially reinforcing maladaptive decision-making patterns in affective disorders.

Systems-level computational modeling in ischemic stroke: from cells to patients.

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Revealing Detailed Cartilage Function Through Nanoparticle Diffusion Imaging: A Computed Tomography & Finite Element Study.

The ability of articular cartilage to withstand significant mechanical stresses during activities, such as walking or running, relies on its distinctive structure. Integrating detailed tissue properties into subject-specific biomechanical models is challenging due to the complexity of analyzing these characteristics. This limitation compromises the accuracy of models in replicating cartilage function and impacts predictive capabilities. To address this, methods revealing cartilage function at the constituent-specific level are essential. In this study, we demonstrated that computational modeling derived individual constituent-specific biomechanical properties could be predicted by a novel nanoparticle contrast-enhanced computer tomography (CECT) method. We imaged articular cartilage samples collected from the equine stifle joint (n = 60) using contrast-enhanced micro-computed tomography (µCECT) to determine contrast agents' intake within the samples, and compared those to cartilage functional properties, derived from a fibril-reinforced poroelastic finite element model. Two distinct imaging techniques were investigated: conventional energy-integrating µCECT employing a cationic tantalum oxide nanoparticle (Ta2O5-cNP) contrast agent and novel photon-counting µCECT utilizing a dual-contrast agent, comprising Ta2O5-cNP and neutral iodixanol. The results demonstrate the capacity to evaluate fibrillar and non-fibrillar functionality of cartilage, along with permeability-affected fluid flow in cartilage. This finding indicates the feasibility of incorporating these specific functional properties into biomechanical computational models, holding potential for personalized approaches to cartilage diagnostics and treatment.

Modeling cardiac microcirculation for the simulation of coronary flow and 3D myocardial perfusion.

Accurate modeling of blood dynamics in the coronary microcirculation is a crucial step toward the clinical application of in silico methods for the diagnosis of coronary artery disease. In this work, we present a new mathematical model of microcirculatory hemodynamics accounting for microvasculature compliance and cardiac contraction; we also present its application to a full simulation of hyperemic coronary blood flow and 3D myocardial perfusion in real clinical cases. Microvasculature hemodynamics is modeled with a compliant multi-compartment Darcy formulation, with the new compliance terms depending on the local intramyocardial pressure generated by cardiac contraction. Nonlinear analytical relationships for vessels distensibility are included based on experimental data, and all the parameters of the model are reformulated based on histologically relevant quantities, allowing a deeper model personalization. Phasic flow patterns of high arterial inflow in diastole and venous outflow in systole are obtained, with flow waveforms morphology and pressure distribution along the microcirculation reproduced in accordance with experimental and in vivo measures. Phasic diameter change for arterioles and capillaries is also obtained with relevant differences depending on the depth location. Coronary blood dynamics exhibits a disturbed flow at the systolic onset, while the obtained 3D perfusion maps reproduce the systolic impediment effect and show relevant regional and transmural heterogeneities in myocardial blood flow (MBF). The proposed model successfully reproduces microvasculature hemodynamics over the whole heartbeat and along the entire intramural vessels. Quantification of phasic flow patterns, diameter changes, regional and transmural heterogeneities in MBF represent key steps ahead in the direction of the predictive simulation of cardiac perfusion.

In Silico Electrophysiological Investigation of Transient Receptor Potential Melastatin-4 Ion Channel Biophysics to Study Detrusor Overactivity.

Enhanced electrical activity in detrusor smooth muscle (DSM) cells is a key factor in detrusor overactivity which causes overactive bladder pathological disorders. Transient receptor potential melastatin-4 (TRPM4) channels, which are calcium-activated cation channels, play a role in regulating DSM electrical activities. These channels likely contribute to depolarizing the DSM cell membrane, leading to bladder overactivity. Our research focuses on understanding TRPM4 channel function in the DSM cells of mice, using computational modeling. We aimed to create a detailed computational model of the TRPM4 channel based on existing electrophysiological data. We employed a modified Hodgkin-Huxley model with an incorporated TRP-like current to simulate action potential firing in response to current and synaptic stimulus inputs. Validation against experimental data showed close agreement with our simulations. Our model is the first to analyze the TRPM4 channel's role in DSM electrical activity, potentially revealing insights into bladder overactivity. In conclusion, TRPM4 channels are pivotal in regulating human DSM function, and TRPM4 channel inhibitors could be promising targets for treating overactive bladder.