Computational Model Research Articles

Paradoxes in magnesium transport in Type 1 Bartter's syndrome and Gitelman's syndrome: a modeling analysis.

Type 1 Bartter's syndrome and Gitelman's syndrome are characterized by mutations in two key renal Na+ transporters, Na-K-2Cl cotransporter (NKCC2) and Na-Cl cotransporter (NCC). Since these two transporters play an important role in regulating magnesium (Mg2+) and calcium (Ca2+) transport in the kidney, significant alterations in the transport of these two electrolytes is observed in Type 1 Bartter's syndrome and Gitelman's syndrome. In this study, we used our sex-specific computational models of renal electrolyte transport in rat to understand the complex compensatory mechanisms, in terms of alterations in tubular dimensions and ion transporter activities, that lead to Mg2+ and Ca2+ preservation or wasting in these two genetic disorders. Given the sexual dimorphism in renal transporter pattern, we also assessed how the magnitude of these alterations may differ between males and females. Model simulations showed that in Type 1 Bartter's syndrome, nephron adaptations prevent salt wasting and favor Mg2+ preservation but not Ca2+, whereas in Gitelman's syndrome, those adaptations favor Ca2+ preservation over Mg2+. In addition, our models predicted that the compensatory alterations in tubular dimensions and ion transporter activities are stronger in females than in males.

Engineering hyaluronic acid-based nanoassemblies for monoclonal antibody delivery – design, characterization, and biological insights

AbstractThe current spotlight of cancer therapeutics is shifting towards personalized medicine with the widespread use of monoclonal antibodies (mAbs). Despite their increasing potential, mAbs have an intrinsic limitation related to their inability to cross cell membranes and reach intracellular targets. Nanotechnology offers promising solutions to overcome this limitation, however, formulation challenges remain. These challenges are the limited loading capacity (often insufficient to achieve clinical dosing), the complex formulation methods, and the insufficient characterization of mAb-loaded nanocarriers. Here, we present a new nanocarrier consisting of hyaluronic acid-based nanoassemblies (HANAs) specifically designed to entrap mAbs with a high efficiency and an outstanding loading capacity (50%, w/w). HANAs composed by an mAb, modified HA and phosphatidylcholine (PC) resulted in sizes of ~ 100 nm and neutral surface charge. Computational modeling identified the principal factors governing the high affinity of mAbs with the amphiphilic HA and PC. HANAs composition and structural configuration were analyzed using the orthogonal techniques cryogenic transmission electron microscopy (cryo-TEM), asymmetrical flow field-flow fractionation (AF4), and small-angle X-ray scattering (SAXS). These techniques provided evidence of the formation of core-shell nanostructures comprising an aqueous core surrounded by a bilayer consisting of phospholipids and amphiphilic HA. In vitro experiments in cancer cell lines and macrophages confirmed HANAs’ low toxicity and ability to transport mAbs to the intracellular space. The reproducibility of this assembling process at industrial-scale batch sizes and the long-term stability was assessed. In conclusion, these results underscore the suitability of HANAs technology to load and deliver biologicals, which holds promise for future clinical translation.

Human head-neck model and its application thresholds: a narrative review.

There had been many studies on human head-neck biomechanical models in the last two decades, and the associated modelling techniques were constantly evolving at the same time. Computational approaches had been widely leveraged, in parallel to conventional physical tests, to investigate biomechanics and injuries of the head-neck system in the fields like automotive industry, orthopaedic, sports medicine, etc. The purpose of this manuscript is to provide a global review of the existing knowledge related to the modelling approaches, structural and biomechanical characteristics, validation and application of the present head-neck models. This endeavor aims to support further enhancements and validations in modeling practices, particularly addressing the lack of data for model validation, as well as to prospect the future advances in terms of the topics. Seventy-four models subject to the proposed selection criteria are considered. Based on previously established and validated head-neck computational models, most of the studies performed in-depth investigations of included cases, which revolved around four specific subjects: physio-pathology, treatment evaluation, collision condition, and sports injury. Through the review of the recent 20 years research, the summarized modeling information indicated existing deficiencies and future research topics, as well as provided references for subsequent head-neck model development and application.

Computationally Hard Problems for Logic Programs under Answer Set Semantics

Showing that a problem is hard for a model of computation is one of the most challenging tasks in theoretical computer science, logic and mathematics. For example, it remains beyond reach to find an explicit problem that cannot be computed by polynomial size propositional formulas (PF). As a model of computation, logic programs (LP) under answer set semantics are as expressive as PF, and also \(\mathtt{NP}\) -complete for satisfiability checking. In this paper, we show that the PAR problem is hard for LP, i.e., deciding whether a binary string contains an odd number of \(1\) ’s requires exponential size logic programs. The proof idea is first to transform logic programs into equivalent boolean circuits, and then apply a probabilistic method known as random restriction to obtain an exponential lower bound. Based on the main result, we generalize a sufficient condition for identifying hard problems for LP, and give a separation map for a logic program family from a computational point of view, whose members are all equally expressive and share the same reasoning complexity.

Hygrothermal Performance of Enclosures in a Storage Room and Energy Efficiency in Environmental Control

ABSTRACT This study aims to provide energy-efficient strategies for collections care in the face of financial and environmental challenges. Focusing on paper-based enclosures in storage rooms, the research proposes strategies to enhance energy efficiency by relaxing tight environmental controls. The five-stage process began with a standard test of hygrothermal properties and buffering capacity of the enclosures. In the second stage, a computer model was developed using the first stage results. This model was used in a series of specially designed tests simulating the transient heat, air, and moisture transfer between the room and enclosure to determine an acceptable room environment in which the enclosure conditions could meet collections care standards. The modelling results were used, in the third stage, to develop an AI model that predicted the enclosure conditions using room temperature and relative humidity (RH) inputs. In the final stage, a model for energy consumption of air-conditioning system operations was developed to predict across various control accuracies. Results indicate that the tight maintenance of room RH levels can be relaxed from 47–53% to 33–65%, with a corresponding reduction in energy consumption of approximately 16–18%. This relaxation, while maintaining enclosure conditions in line with conservation standards, was demonstrated through a simulation case at the National Library of Scotland. This study underscores the broader applicability of the developed procedure, asserting its relevance for cultural institutions seeking energy-saving potential through customized enclosure models.

Investigation of the dynamic characteristics of the pneumatic-mechanical drive of the installation movements of machine mechanisms

The creation of new and improvement of existing machines and mechanisms requires the development of drive engineering and the creation of new drive systems capable of competing with currently known circuit solutions. Under these conditions, the creation of combined pneumatic-mechanical drives is an important scientific and technical task. The purpose of this work is to increase the speed at a given accuracy of auxiliary movements of the target mechanisms of technological machines by creating a pneumatic-mechanical drive with improved energy and dynamic characteristics. The technical solution of a positioning pneumatic-mechanical drive of executive movements of technological equipment is presented, mathematical and computer models are developed. In this case, the method of solving the differential equation in the SimInTech program is used. As a result, an oscillogram of transients is obtained. The developed models allow for dynamic analysis of pneumatic-mechanical systems.

Hygrothermal Performance of Enclosures in a Storage Room and Energy Efficiency in Environmental Control

ABSTRACT This study aims to provide energy-efficient strategies for collections care in the face of financial and environmental challenges. Focusing on paper-based enclosures in storage rooms, the research proposes strategies to enhance energy efficiency by relaxing tight environmental controls. The five-stage process began with a standard test of hygrothermal properties and buffering capacity of the enclosures. In the second stage, a computer model was developed using the first stage results. This model was used in a series of specially designed tests simulating the transient heat, air, and moisture transfer between the room and enclosure to determine an acceptable room environment in which the enclosure conditions could meet collections care standards. The modelling results were used, in the third stage, to develop an AI model that predicted the enclosure conditions using room temperature and relative humidity (RH) inputs. In the final stage, a model for energy consumption of air-conditioning system operations was developed to predict across various control accuracies. Results indicate that the tight maintenance of room RH levels can be relaxed from 47–53% to 33–65%, with a corresponding reduction in energy consumption of approximately 16–18%. This relaxation, while maintaining enclosure conditions in line with conservation standards, was demonstrated through a simulation case at the National Library of Scotland. This study underscores the broader applicability of the developed procedure, asserting its relevance for cultural institutions seeking energy-saving potential through customized enclosure models.

Optimizing Production Stability while Safeguarding Information

The goal of Finite Time Stabilization is to finish a particular thing in a fixed time. Regardless of the system's original state, finite-time stabilisation refers to the regulation of a system so that it reaches a desired equilibrium or setpoint in a finite length of time. Finite-time stabilisation is essential for providing quick and effective control over a variety of variables in industrial processes, such as temperature, pressure, flow rate, or composition. By layering materials based on a computer model, 3D printing, sometimes referred to as additive manufacturing, creates three-dimensional items. Even though 3D printing technology has advanced significantly in recent years, manufacturing them still presents a number of difficulties. Some of the typical difficulties include: Cost: Due to the intricate parts and high level of precision needed when manufacturing 3D printers, the cost might be high. Quality parts, such as motors, electronics, and extruders, can be expensive to source. As a result, manufacturers may find it difficult to strike a balance between price and performance. So, as part of our process, we analyse the data and forecast the pricing to make things simple for the client. To make this prediction, we employed logistic regression. More than that those client data is secured through fernet algorithm.

Flow Analysis of Central Venous Outflow Tract: A New Approach to Understanding Pulse-Synchronous Tinnitus.

Pulse-synchronous tinnitus (PST) has been linked to multiple anatomical variants of the central venous outflow tract (CVOT) including sigmoid sinus (SS) dehiscence and diverticulum. This study investigates flow turbulence, pressure, and wall shear stress along the CVOT and proposes a mechanism that results in SS dehiscence and PST. Case series. Tertiary Academic Center. Venous models were reconstructed from computed tomography scans of 3 patients with unilateral PST. Two models for each patient are obtained: a symptomatic and contralateral asymptomatic side. A turbulent model-enabled commercial flow solver was used to simulate the pulsatile blood flow over the cardiac cycle through the models. Fluid flow through the transverse and SS junction was analyzed to observe the velocity, pressure, turbulent kinetic energy (TKE), and shear stress over a simulated cardiac cycle. Fluid flow on the symptomatic side showed increased vorticity in the presence of an SS diverticulum. Higher TKE with periodicity following the cardiac cycle was observed on the symptomatic side, and a sharp increase was observed if SS diverticulum was present. Shear stress was highest near the narrowest segments of the vessel. Pressure was observed to be lower on the symptomatic side at the transverse-SS junction for all 3 patients. Computational fluid dynamics modeling of blood flow through the CVOT in PST suggests that low pressure may be the cause of dehiscence, and tinnitus may result from periodic increases in TKE.

Optimizing heat pump unit selection for recirculating aquaculture workshops through computational fluid dynamics.

The selection of water temperature regulation equipment plays a crucial role in the design of workshops. At present, the choice of water temperature control equipment is usually based on the volume of the fish pond and thermal parameter calculation, combined with aquaculture experience. Empirical formulas only work in specific conditions due to factors like the environment, climate, and fish types,resulting in inaccurate equipment selection outcomes. Recognizing this limitation, this paper proposes to apply CFD simulation of the temperature field to accurately calculate the heat exchange value between indoor air and water, thereby predicting the heat exchange values during aquaculture activities in the aquaculture workshop. providing a new approach for equipment selection. This paper selects a puffer fish breeding workshop in Dalian as the simulation object, establishing a 3D unsteady-state Computational Fluid Dynamics model. The model considers outdoor temperature, solar radiation, and phase-change heat transfer in water. Comparison with experimental data reveals a root mean square error of 0.46°C for the simulated results. During summer, the highest cooling load occurs at 16:00, reaching 94.6 kW. It is recommended to employ the Daikin GCHP-40MAH ground source heat pump as the water temperature control equipment. CFD simulation validates its effectiveness in shaping the indoor temperature field post-installation. the investment in water temperature control equipment can be reduced to a certain degree. This provides a reference value for the selection of water temperature equipment in aquaculture workshops.

Flow Analysis of Central Venous Outflow Tract: A New Approach to Understanding Pulse-Synchronous Tinnitus.

Pulse-synchronous tinnitus (PST) has been linked to multiple anatomical variants of the central venous outflow tract (CVOT) including sigmoid sinus (SS) dehiscence and diverticulum. This study investigates flow turbulence, pressure, and wall shear stress along the CVOT and proposes a mechanism that results in SS dehiscence and PST. Case series. Tertiary Academic Center. Venous models were reconstructed from computed tomography scans of 3 patients with unilateral PST. Two models for each patient are obtained: a symptomatic and contralateral asymptomatic side. A turbulent model-enabled commercial flow solver was used to simulate the pulsatile blood flow over the cardiac cycle through the models. Fluid flow through the transverse and SS junction was analyzed to observe the velocity, pressure, turbulent kinetic energy (TKE), and shear stress over a simulated cardiac cycle. Fluid flow on the symptomatic side showed increased vorticity in the presence of an SS diverticulum. Higher TKE with periodicity following the cardiac cycle was observed on the symptomatic side, and a sharp increase was observed if SS diverticulum was present. Shear stress was highest near the narrowest segments of the vessel. Pressure was observed to be lower on the symptomatic side at the transverse-SS junction for all 3 patients. Computational fluid dynamics modeling of blood flow through the CVOT in PST suggests that low pressure may be the cause of dehiscence, and tinnitus may result from periodic increases in TKE.

Hemodynamic evaluation of biomaterial-based surgery for Tetralogy of Fallot using a biorobotic heart, in silico, and ovine models.

Tetralogy of Fallot is a congenital heart disease affecting newborns and involves stenosis of the right ventricular outflow tract (RVOT). Surgical correction often widens the RVOT with a transannular enlargement patch, but this causes issues including pulmonary valve insufficiency and progressive right ventricle failure. A monocusp valve can prevent pulmonary regurgitation; however, valve failure resulting from factors including leaflet design, morphology, and immune response can occur, ultimately resulting in pulmonary insufficiency. A multimodal platform to quantitatively evaluate the effect of shape, size, and material on clinical outcomes could optimize monocusp design. This study introduces a benchtop soft biorobotic heart model, a computational fluid model of the RVOT, and a monocusp valve made from an entirely biological cell-assembled extracellular matrix (CAM) to tackle the multifaceted issue of monocusp failure. The hydrodynamic and mechanical performance of RVOT repair strategies was assessed in biorobotic and computational platforms. The monocusp valve design was validated in vivo in ovine models through echocardiography, cardiac magnetic resonance, and catheterization. These models supported assessment of surgical feasibility, handling, suturability, and hemodynamic and mechanical monocusp capabilities. The CAM-based monocusp offered a competent pulmonary valve with regurgitation of 4.6 ± 0.9% and a transvalvular pressure gradient of 4.3 ± 1.4 millimeters of mercury after 7 days of implantation in sheep. The biorobotic heart model, in silico analysis, and in vivo RVOT modeling allowed iteration in monocusp design not now feasible in a clinical environment and will support future surgical testing of biomaterials for complex congenital heart malformations.

Modeling of Future Streamflow Hazards in Interior Alaska River Systems and Implications for Applied Planning

There is a growing need for proactive planning for natural hazards in a changing climate. Computational modeling of climate hazards provides an opportunity to inform planning, particularly in areas approaching ecosystem state changes, such as Interior Alaska, where future hazards are expected to differ significantly from historical events in frequency and severity. This paper considers improved modeling approaches from a physical process perspective and contextualizes the results within the complexities and limitations of hazard planning efforts and management concerns. Therefore, the aim is not only to improve the understanding of potential climate impacts on streamflow within this region but also to further explore the steps needed to evaluate local-scale hazards from global drivers and the potential challenges that may be present. This study used dynamically downscaled climate forcing data from ERA-Interim reanalysis datasets and projected climate scenarios from two General Circulation Models under a single Representative Concentration Pathway (RCP 8.5) to simulate an observational gage-calibrated WRF-Hydro model to assess shifts in streamflow and flooding potential in three Interior Alaska rivers over a historical period (2008–2017) and two future periods (2038–2047 and 2068–2077). Outputs were assessed for seasonality, streamflow, extreme events, and the comparison between existing flood control infrastructure in the region. The results indicate that streamflow in this region is likely to experience increases in seasonal length and baseflow, while the potential for extreme events and variable short-term streamflow behavior is likely to see greater uncertainty, based on the divergence between the models.

In silico-guided discovery and in vitro validation of novel sugar-tethered lysinated carbon nanotubes for targeted drug delivery of doxorubicin

ContextMultiwalled carbon nanotubes (MWCNTs) functionalized with lysine via 1,3-dipolar cycloaddition and conjugated to galactose or mannose are potential nanocarriers that can effectively bind to the lectin receptor in MDA-MB-231 or MCF-7 breast cancer cells. In this work, a method based on molecular dynamics (MD) simulation was used to predict the interaction of these functionalized MWCNTs with doxorubicin and obtain structural evidence that allows a better understanding of the drug loading and release process. The MD simulations showed that while doxorubicin only interacted with pristine MWCNTs through π-π stacking interactions, functionalized MWCNTs were also able to establish hydrogen bonds, suggesting that the functionalized groups improve doxorubicin loading. Moreover, the elevated adsorption levels observed for functionalized nanotubes further support this enhancement in loading efficiency. MD simulations also shed light on the intratumoral pH-specific release of doxorubicin from functionalized MWCNTs, which is induced by protonation of the daunosamine moiety. The simulations show that this change in protonation leads to a lower absorption of doxorubicin to the MWCNTs. The MD studies were then experimentally validated, where functionalized MWCNTs showed improved dispersion in aqueous medium compared to pristine MWCNTs and, in agreement with the computational predictions, increased drug loading capacity. Doxorubicin-loaded functionalized MWCNTs demonstrated specific release of doxorubicin in tumor microenvironment (pH = 5.0) with negligible release in the physiological pH (pH = 7.4). Furthermore, doxorubicin-free MWNCT nanoformulations exhibited insignificant cytotoxicity. The experimental studies yielded nearly identical results to the MD studies, underlining the usefulness of the method. Our functionalized MWCNTs represent promising non-toxic nanoplatforms with enhanced aqueous dispersibility and the potential for conjugation with ligands for targeted delivery of anti-cancer drugs to breast cancer cells.MethodsThe computational model of a pristine carbon nanotube was created with the buildCstruct 1.2 Python script. The lysinated functionalized groups were added with PyMOL and VMD. The carbon nanotubes and doxorubicin molecules were parameterized using the general AMBER force field, and RESP charges were determined using Gaussian 09. Molecular dynamics simulations were carried out with the AMBER 20 software package. Adsorption levels were calculated using the water-shell function of cpptraj. Cytotoxicity was evaluated via a MTT assay using MDA-MB-231 and MCF-7 breast cancer cells. Drug uptake of doxorubicin and doxorubicin-loaded MWCNTs was measured by fluorescence microscopy.

Space-Based Passive Orbital Maneuver Detection Algorithm for High-Altitude Situational Awareness

Orbital maneuver detection for non-cooperative targets in space is a key task in space situational awareness. This study develops a passive maneuver detection algorithm using line-of-sight angles measured by a space-based optical sensor, especially for targets in high-altitude orbit. Emphasis is placed on constructing a new characterization for maneuvers as well as the corresponding detection method. First, the concept of relative angular momentum is introduced to characterize the orbital maneuver of the target quantitatively, and the sensitivity of the proposed characterization is analyzed mathematically. Second, a maneuver detection algorithm based on the new characterization is designed in which sliding windows and correlations are utilized to determine the mutation of the maneuver characterization. Subsequently, a numerical simulation system composed of error models, reference missions and trajectories, and computation models for estimating errors is established. Then, the proposed algorithm is verified through numerical simulations for both long-range and close-range targets. The results indicate that the proposed algorithm is effective. Additionally, the sensitivity of the proposed algorithm to the width of the sliding window, accuracy of the optical sensor, magnitude and number of maneuvers, and different relative orbit types is analyzed, and the sensitivity of the new characterization is verified using simulations.

Building and Breaking the Chain: A Model of Reward Prediction Error Integration and Segmentation of Memory.

Prediction errors drive reinforcement learning and organize episodic memory into distinct contexts, but do these effects interact? Here, we review the roles of midbrain dopamine, the locus coeruleus, and the hippocampus in event cognition to propose and simulate the theoretical influence of two prediction error signals in integrating versus segmenting events in memory. We suggest that signed reward prediction errors can build mental models of reward environments, increasing the contextual similarity (integration) of experiences with stronger, more stable reward expectations. On the other hand, unsigned reward prediction errors can signal a new model of the environment, generating a contextual shift (segmentation) between experiences that crossed them. We moreover predicted that these differences in contextual similarity give rise to distinct patterns of temporal-order memory. We combined these ideas in a computational model to account for a seemingly paradoxical pattern of temporal-order memory where greater representational distance helps order memory within context but impairs it across contexts. We found that simulating signed reward prediction error integration and unsigned reward prediction error segmentation differentially enabled the model to perform associative chaining, which involved reactivating items between two tested probes to assist with sequential retrieval. In summary, our simulations provide a unifying explanation for the varied ways that neuromodulatory systems alter event cognition and memory.

In silico-guided discovery and in vitro validation of novel sugar-tethered lysinated carbon nanotubes for targeted drug delivery of doxorubicin

ContextMultiwalled carbon nanotubes (MWCNTs) functionalized with lysine via 1,3-dipolar cycloaddition and conjugated to galactose or mannose are potential nanocarriers that can effectively bind to the lectin receptor in MDA-MB-231 or MCF-7 breast cancer cells. In this work, a method based on molecular dynamics (MD) simulation was used to predict the interaction of these functionalized MWCNTs with doxorubicin and obtain structural evidence that allows a better understanding of the drug loading and release process. The MD simulations showed that while doxorubicin only interacted with pristine MWCNTs through π-π stacking interactions, functionalized MWCNTs were also able to establish hydrogen bonds, suggesting that the functionalized groups improve doxorubicin loading. Moreover, the elevated adsorption levels observed for functionalized nanotubes further support this enhancement in loading efficiency. MD simulations also shed light on the intratumoral pH-specific release of doxorubicin from functionalized MWCNTs, which is induced by protonation of the daunosamine moiety. The simulations show that this change in protonation leads to a lower absorption of doxorubicin to the MWCNTs. The MD studies were then experimentally validated, where functionalized MWCNTs showed improved dispersion in aqueous medium compared to pristine MWCNTs and, in agreement with the computational predictions, increased drug loading capacity. Doxorubicin-loaded functionalized MWCNTs demonstrated specific release of doxorubicin in tumor microenvironment (pH = 5.0) with negligible release in the physiological pH (pH = 7.4). Furthermore, doxorubicin-free MWNCT nanoformulations exhibited insignificant cytotoxicity. The experimental studies yielded nearly identical results to the MD studies, underlining the usefulness of the method. Our functionalized MWCNTs represent promising non-toxic nanoplatforms with enhanced aqueous dispersibility and the potential for conjugation with ligands for targeted delivery of anti-cancer drugs to breast cancer cells.MethodsThe computational model of a pristine carbon nanotube was created with the buildCstruct 1.2 Python script. The lysinated functionalized groups were added with PyMOL and VMD. The carbon nanotubes and doxorubicin molecules were parameterized using the general AMBER force field, and RESP charges were determined using Gaussian 09. Molecular dynamics simulations were carried out with the AMBER 20 software package. Adsorption levels were calculated using the water-shell function of cpptraj. Cytotoxicity was evaluated via a MTT assay using MDA-MB-231 and MCF-7 breast cancer cells. Drug uptake of doxorubicin and doxorubicin-loaded MWCNTs was measured by fluorescence microscopy.

Cost-benefit Tradeoff Mediates the Rule- to Memory-based Processing Transition during Practice.

Practice not only improves task performance, but also changes task execution from rule- to memory-based processing by incorporating experiences from practice. However, how and when this change occurs is unclear. We tested the hypothesis that strategy transition in task learning results from cost-benefit analysis. Participants learned two task sequences and were then queried about the task type at a cued sequence and position. Behavioral improvement with practice can be accounted for by a computational model implementing cost-benefit analysis. Model-predicted strategy transition points are related to behavioral slowing and changes in fMRI activation patterns in the dorsolateral prefrontal cortex. Strategy transition is also related to increased pattern separation in the ventromedial prefrontal cortex. The cost-benefit analysis model outperforms alternative models (e.g., both strategies racing for being expressed in behavior) in accounting for empirical data. These findings support cost-benefit analysis as a mechanism of practice-induced strategy shift.

Computational Model of Two-Phase Mass Transport Dynamics for pH-Buffered Hydrogen Evolution Reactions in Porous Electrodes

Computational Model of Two-Phase Mass Transport Dynamics for pH-Buffered Hydrogen Evolution Reactions in Porous Electrodes

Purkinje cell models: past, present and future

The investigation of the dynamics of Purkinje cell (PC) activity is crucial to unravel the role of the cerebellum in motor control, learning and cognitive processes. Within the cerebellar cortex (CC), these neurons receive all the incoming sensory and motor information, transform it and generate the entire cerebellar output. The relatively homogenous and repetitive structure of the CC, common to all vertebrate species, suggests a single computation mechanism shared across all PCs. While PC models have been developed since the 70′s, a comprehensive review of contemporary models is currently lacking. Here, we provide an overview of PC models, ranging from the ones focused on single cell intracellular PC dynamics, through complex models which include synaptic and extrasynaptic inputs. We review how PC models can reproduce physiological activity of the neuron, including firing patterns, current and multistable dynamics, plateau potentials, calcium signaling, intrinsic and synaptic plasticity and input/output computations. We consider models focusing both on somatic and on dendritic computations. Our review provides a critical performance analysis of PC models with respect to known physiological data. We expect our synthesis to be useful in guiding future development of computational models that capture real-life PC dynamics in the context of cerebellar computations.