Computational Model Research Articles

Heterozygous expression of a Kcnt1 gain-of-function variant has differential effects on SST- and PV-expressing cortical GABAergic neurons.

More than twenty recurrent missense gain-of-function (GOF) mutations have been identified in the sodium-activated potassium (KNa) channel gene KCNT1 in patients with severe developmental and epileptic encephalopathies (DEEs), most of which are resistant to current therapies. Defining the neuron types most vulnerable to KCNT1 GOF will advance our understanding of disease mechanisms and provide refined targets for precision therapy efforts. Here, we assessed the effects of heterozygous expression of a Kcnt1 GOF variant (Y777H) on KNa currents and neuronal physiology among cortical glutamatergic and GABAergic neurons in mice, including those expressing vasoactive intestinal polypeptide (VIP), somatostatin (SST), and parvalbumin (PV), to identify and model the pathogenic mechanisms of autosomal dominant KCNT1 GOF variants in DEEs. Although the Kcnt1-Y777H variant had no effects on glutamatergic or VIP neuron function, it increased subthreshold KNa currents in both SST and PV neurons but with opposite effects on neuronal output; SST neurons became hypoexcitable with a higher rheobase current and lower action potential (AP) firing frequency, whereas PV neurons became hyperexcitable with a lower rheobase current and higher AP firing frequency. Further neurophysiological and computational modeling experiments showed that the differential effects of the Y777H variant on SST and PV neurons are not likely due to inherent differences in these neuron types, but to an increased persistent sodium current in PV, but not SST, neurons. The Y777H variant also increased excitatory input onto, and chemical and electrical synaptic connectivity between, SST neurons. Together, these data suggest differential pathogenic mechanisms, both direct and compensatory, contribute to disease phenotypes, and provide a salient example of how a pathogenic ion channel variant can cause opposite functional effects in closely related neuron subtypes due to interactions with other ionic conductances.

Applying 3D Models of Giant Salamanders to Explore Form-function Relationships in Early Digit-bearing Tetrapods.

Extant salamanders are used as modern analogs of early digit-bearing tetrapods due to general similarities in morphology and ecology but the study species have been primarily terrestrial and relatively small when the earliest digit-bearing tetrapods were aquatic and an order of magnitude larger. Thus, we created a 3D computational model of underwater walking in extant Japanese giant salamanders (Andrias japonicus) using 3D photogrammetry and open-access graphics software (Blender) to broaden the range of testable hypotheses about the incipient stages of terrestrial locomotion. Our 3D model and software protocol represent the initial stages of an open-access pipeline that could serve as a "one-stop-shop" for studying locomotor function, from creating 3D models to analyzing the mechanics of locomotor gaits. While other pipelines generally require multiple software programs to accomplish the different steps in creating and analyzing computational models of locomotion, our protocol is built entirely within Blender and fully customizable with its Python scripting so users can devote more time to creating and analyzing models instead of navigating the learning curves of several software programs. The main value of our approach is that key kinematic variables (e.g., speed, stride length, elbow flexion) can be easily altered on the 3D model, allowing scientists to test hypotheses about locomotor function and conduct manipulative experiments (e.g., lengthening bones) that are difficult to perform in vivo. The accurate 3D meshes (and animations) generated through photogrammetry also provide exciting opportunities to expand the abundance and diversity of 3D digital animals available for researchers, educators, artists, conservation biologists, etc. to maximize societal impacts.

GraphPro: An interpretable graph neural network-based model for identifying promoters in multiple species

Promoters are DNA sequences that bind with RNA polymerase to initiate transcription, regulating this process through interactions with transcription factors. Accurate identification of promoters is crucial for understanding gene expression regulation mechanisms and developing therapeutic approaches for various diseases. However, experimental techniques for promoter identification are often expensive, time-consuming, and inefficient, necessitating the development of accurate and efficient computational models for this task. Enhancing the model's ability to recognize promoters across multiple species and improving its interpretability pose significant challenges. In this study, we introduce a novel interpretable model based on graph neural networks, named GraphPro, for multi-species promoter identification. Initially, we encode the sequences using k-tuple nucleotide frequency pattern, dinucleotide physicochemical properties, and dna2vec. Subsequently, we construct two feature extraction modules based on convolutional neural networks and graph neural networks. These modules aim to extract specific motifs from the promoters, learn their dependencies, and capture the underlying structural features of the promoters, providing a more comprehensive representation. Finally, a fully connected neural network predicts whether the input sequence is a promoter. We conducted extensive experiments on promoter datasets from eight species, including Human, Mouse, and Escherichia coli. The experimental results show that the average Sn, Sp, Acc and MCC values of GraphPro are 0.9123, 0.9482, 0.8840 and 0.7984, respectively. Compared with previous promoter identification methods, GraphPro not only achieves better recognition accuracy on multiple species, but also outperforms all previous methods in cross-species prediction ability. Furthermore, by visualizing GraphPro's decision process and analyzing the sequences matching the transcription factor binding motifs captured by the model, we validate its significant advantages in biological interpretability. The source code for GraphPro is available at https://github.com/liuliwei1980/GraphPro.

Machine Learning Prediction of On/Off Target-driven Clinical Adverse Events.

Currently, 90% of clinical drug development fails, where 30% of these failures are due to clinical toxicity. The current extensive animal toxicity studies are not predictive of clinical adverse events (AEs) at clinical doses, while current computation models only consider very few factors with limited success in clinical toxicity prediction. We aimed to address these issues by developing a machine learning (ML) model to directly predict clinical AEs. Using a dataset with 759 FDA-approved drugs with known AEs, we first adapted the ConPLex ML model to predict IC values of these FDA-approved drugs against their on-target and off-target binding among 477 protein targets. Subsequently, we constructed a new ML model to predict clinical AEs using IC values of 759 drugs' primary on-target and off-target effects along with tissue-specific protein expression profiles. The adapted ConPLex model predicted drug-target interactions for both on- and off-target effects, as shown by co-localization of the 6 small molecule kinase inhibitors with their respective kinases. The coupled ML models demonstrated good predictive capability of clinical AEs, with accuracy over 75%. Our approach provides a new insight into the mechanistic understanding of in vivo drug toxicity in relationship with drug on-/off-target interactions. The coupled ML models, once validated with larger datasets, may offer advantages to directly predict clinical AEs using in vitro/ex vivo and preclinical data, which will help to reduce drug development failure due to clinical toxicity.

The 26 S proteasome in Entamoeba histolytica: divergence of the substrate binding pockets from host proteasomes

ObjectiveProteasomes are conserved proteases crucial for proteostasis in eukaryotes and are promising drug targets for protozoan parasites. Yet, the proteasomes of Entamoeba histolytica remain understudied. The study’s objective was to analyse the differences in the substrate binding pockets of amoeba proteasomes from those of host, and computational modelling of β5 catalytic subunit, with the goal of finding selective inhibitors.ResultsComparative sequence analysis revealed differences in substrate binding sites of E. histolytica proteasomes, especially in the S1 and S3 pockets of the catalytic beta subunits, implying differences in substrate preference and susceptibility to inhibitors from host proteasomes. This was strongly supported by significantly lower sensitivity to MG132 mediated inhibition of amoebic proteasome β5 subunit’s chymotryptic activity compared to human proteasomes, also reflected in lower sensitivity of E. histolytica to MG132 for inhibition of proliferation. Computational models of β4 and β5 subunits, and a docked β4-β5 model revealed a binding pocket between β4-β5, similar to that of Leishmania tarentolae. Selective inhibitors for visceral leishmaniasis, LXE408 and compound 8, docked well to this pocket. This functional and sequence-based analysis predicts differences between amoebic and host proteasomes that can be utilized to develop rationally designed, selective inhibitors against E. histolytica.

Neural mass modelling for the masses: Democratising access to whole-brain biophysical modelling with FastDMF

Abstract Different whole-brain computational models have been recently developed to investigate hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is particularly attractive, combining a biophysically realistic model that is scaled up via a mean-field approach and multimodal imaging data. However, an important barrier to widespread usage of the DMF model is that current implementations are computationally expensive, supporting only simulations on brain parcellations that consider less than 100 brain regions. Here, we introduce an efficient and accessible implementation of the DMF model: the FastDMF. By leveraging analytical and numerical advances – including a novel estimation of the feedback inhibition control parameter, and a Bayesian optimization algorithm – the FastDMF circumvents various computational bottlenecks of previous implementations, improving interpretability, performance and memory use. Furthermore, these advances allow the FastDMF to increase the number of simulated regions by one order of magnitude, as confirmed by the good fit to fMRI data parcellated at 90 and 1000 regions. These advances open the way to the widespread use of biophysically grounded whole-brain models for investigating the interplay between anatomy, function, and brain dynamics, and to identify mechanistic explanations of recent results obtained from fine-grained neuroimaging recordings.

Computational Argumentation-based Chatbots: A Survey

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses.

Sounds are temporal stimuli decomposed into numerous elementary components by the auditory nervous system. For instance, a temporal to spectro-temporal transformation modelling the frequency decomposition performed by the cochlea is a widely adopted first processing step in today's computational models of auditory neural responses. Similarly, increments and decrements in sound intensity (i.e., of the raw waveform itself or of its spectral bands) constitute critical features of the neural code, with high behavioural significance. However, despite the growing attention of the scientific community on auditory OFF responses, their relationship with transient ON, sustained responses and adaptation remains unclear. In this context, we propose a new general model, based on a pair of linear filters, named AdapTrans, that captures both sustained and transient ON and OFF responses into a unifying and easy to expand framework. We demonstrate that filtering audio cochleagrams with AdapTrans permits to accurately render known properties of neural responses measured in different mammal species such as the dependence of OFF responses on the stimulus fall time and on the preceding sound duration. Furthermore, by integrating our framework into gold standard and state-of-the-art machine learning models that predict neural responses from audio stimuli, following a supervised training on a large compilation of electrophysiology datasets (ready-to-deploy PyTorch models and pre-processed datasets shared publicly), we show that AdapTrans systematically improves the prediction accuracy of estimated responses within different cortical areas of the rat and ferret auditory brain. Together, these results motivate the use of our framework for computational and systems neuroscientists willing to increase the plausibility and performances of their models of audition.

Electrophysiological patterns and structural substrates of Brugada syndrome: Critical appraisal and computational analyses.

Brugada syndrome (BrS) is a cardiac electrophysiological disease with unknown etiology, associated with sudden cardiac death. Symptomatic patients are treated with implanted cardiac defibrillator, but no risk stratification strategy is effective in patients that are at low to medium arrhythmic risk. Cardiac computational modeling is an emerging tool that can be used to verify the hypotheses of pathogenesis and inspire new risk stratification strategies. However, to obtain reliable results computational models must be validated with consistent experimental data. We reviewed the main electrophysiological and structural variables from BrS clinical studies to assess which data could be used to validate a computational approach. Activation delay in the epicardial right ventricular outflow tract is a consistent finding, as well as increased fibrosis and subclinical alterations of right ventricular functional and morphological parameters. The comparison between other electrophysiological variables is hindered by methodological differences between studies, which we commented. We conclude by presenting a recent theory unifying electrophysiological and structural substrate in BrS and illustrate how computational modeling could help translation to risk stratification.

Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-selective Therapeutic Strategies for FGFR4-driven Cancers.

Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple negative breast cancer (TNBC) and hepatocellular carcinoma (HCC). However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in TNBC cells. Analyzing the effects of co-targeting specific network nodes by systematically simulating the model predicted synergy of co-targeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, co-targeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that while AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven HCC cell line, where there is a synergistic effect of co-targeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment.

Guidelines for mechanistic modeling and analysis in cardiovascular research.

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

Design and development of electric radiant heaters for local heating inside the cabin of electric vehicles

The transition towards electric vehicles (EVs) represents a crucial step in mitigating global warming and reducing greenhouse gas emissions. However, the energy efficiency of EVs, particularly in terms of cabin thermal management, remains a significant challenge, especially under extreme climatic conditions. This manuscript addresses the challenge by focusing on the design and optimization of electric radiant (ER) heaters for enhancing the thermal management of EV cabins. Through an in-depth investigation involving experimental analysis and computational modeling, this study explores various materials in the design of the ER heater, aiming to maximize radiant heating effect while ensuring energy conservation. Key findings reveal that the choice of materials for the ER heater’s components significantly impacts its thermal performance. Specifically, a combination of rigid polyurethane (PU) foam as insulation and expanded polypropylene (EPP) 30 for the base structure was identified as the optimal design to achieve a maximum radiant power efficiency of about 47.3%. This improved radiant power efficiency outperformed the baseline design by more than 25%.

Modulating electrical response and impedance in Co–Cr substituted M-phase barium hexaferrites: Examining microstructure, grain boundaries, charge transport, and computational modeling

This study explores the influence of Co2+ and Cr3+ doping on the microstructure and electrical properties of Ba-hexagonal ferrite synthesized using the sol-gel method. X-ray diffraction (XRD) confirmed the formation of the anticipated hexagonal M-type crystal structure. Field emission scanning electron microscopy (FESEM) revealed the evolution of grain morphology with increasing doping levels, showcasing the formation of needle-like grains. Electrical characterization using an impedance analyzer investigated dielectric, impedance, electric modulus, and conductivity properties at room temperature. The analysis of the dielectric spectra indicated a decrease in dielectric constant and an increase in loss tangent with increasing dopant concentration. The electric modulus spectra provided evidence for non-Debye relaxation behavior, further supported by observations of relaxation processes at varying frequencies in the conductivity spectra. Additionally, an increase in doping led to a decrease in both relaxation time and AC conductivity. Electrochemical impedance spectroscopy (EIS) measurements yielded complex impedance curves. These curves exhibited good agreement with the values obtained through software-based simulations of grain and grain boundary characteristics. Furthermore, the distribution of grains and grain boundaries observed in FESEM micrographs aligned with the patterns predicted by the EIS software.

Solubility of glibenclamide in carbitol and 1-propanol mixtures at different temperatures

ABSTRACT In this work, the solubility of glibenclamide in the non-aqueous mixtures of carbitol and 1-propanol was studied at temperatures ranging from 293.2 K to 313.2 K using a shake-flask technique. The findings were represented by five computational models, including the van’t Hoff, the Jouyban-Acree, the Jouyban-Acree-van’t Hoff, the mixture response surface and the modified Wilson models. These models showed strong correlation and prediction capabilities, as indicated by the mean relative deviations for the back-calculated data. Furthermore, the thermodynamic analysis was conducted for solubility data using van’t Hoff and Gibbs equations.

Nanoscale lipid domains determine the dynamic molecular portraits of mixed DOPC/DOPS bilayers in a fluid phase: A computational insight

Lateral heterogeneity, or mosaicity, is a fundamental property inherent to cell membranes that is crucial for their functioning. While microscopic inhomogeneities (e.g. rafts) are easily detected experimentally, lipid domains with nanoscale dimensions (nanoclusters of nanodomains, NDs) resist reliable characterization by instrumental methods. In such a case, important insight can be gained via computer modeling. Here, NDs composed of lipid's head groups in the mixed zwitterionic dioleoylphosphatidylcholine (DOPC) and negatively charged dioleoylphosphatidylserine (DOPS) bilayers were studied by molecular dynamics. A new algorithm has been developed to identify NDs. Unlike most similar methods, it implicitly considers the heterogeneous distribution of lipid head atomic density and does not require subjectively chosen parameters. In DOPS-rich membranes, lipids form more compact and stable NDs due to strong interlipid interactions. In DOPC-rich systems, NDs arise due to the “packing” effect of weakly bound lipid heads. The clustering picture is related to the physical properties of the bilayer surface: DOPS-rich systems show more pronounced surface heterogeneity of hydrophilic/hydrophobic regions compared to DOPC-rich ones. The results obtained are important for the effective quantitative characterization of the “dynamic molecular portrait” of a membrane surface – its “fingerprint” characterizing dynamical distribution of its physicochemical properties.

GraphCL-DTA: A Graph Contrastive Learning With Molecular Semantics for Drug-Target Binding Affinity Prediction.

Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data without considering the information in the molecular graph itself. Moreover, most previous studies tended to design complicated representation learning modules, while uniformity used to measure representation quality is ignored. In this study, we propose GraphCL-DTA, a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. This graph contrastive learning framework replaces the dropout-based data augmentation strategy by performing data augmentation in the embedding space, thereby better preserving the semantic information of the molecular graph. A more essential and effective drug representation can be learned through this graph contrastive framework without additional supervised data. Next, we design a new loss function that can be directly used to adjust the uniformity of drug and target representations. By directly optimizing the uniformity of representations, the representation quality of drugs and targets can be improved. The effectiveness of the above innovative elements is verified on two real datasets, KIBA and Davis. Compared with the GraphDTA model, the relative improvement of the GraphCL-DTA model on the two datasets is 2.7% and 4.5%. The graph contrastive learning framework and uniformity function in the GraphCL-DTA model can be embedded into other computational models as independent modules to improve their generalization capability.

Modelling child comprehension: A case of suffixal passive construction in Korean

The present study investigates a computational model's ability to capture monolingual children's language behaviour during comprehension in Korean, an understudied language in the field. Specifically, we test whether and how two neural network architectures (LSTM, GPT-2) cope with a suffixal passive construction involving verbal morphology and required interpretive procedures (i.e., revising the mapping between thematic roles and case markers) driven by that morphology. To this end, we fine-tune our models via patching (i.e., pre-trained model + caregiver input) and hyperparameter adjustments, and measure their binary classification performance on the test sentences used in a behavioural study manifesting scrambling and omission of sentential components to varying degrees. We find that, while these models’ performance converges with the children's response patterns found in the behavioural study to some extent, the models do not faithfully simulate the children's comprehension behaviour pertaining to the suffixal passive, yielding by-model, by-condition, and by-hyperparameter asymmetries. This points to the limits of the neural networks’ capacity to address child language features. The implications of this study invite subsequent inquiries on the extent to which computational models reveal developmental trajectories of child language that have been unveiled through corpus-based or experimental research.

The evaluation of computational modelling performance within the context of rationality theory: finding the area between two curves

ABSTRACT This study aimed to investigate the development of computational modelling performances of students during the computer-assisted system (CAS) integrated ACODESA method based on Habermas’ construct of rationality. The participants included 22 university students. In the study, in which the embedded design was utilised, the qualitative phase aimed to examine the computational modelling performances of the students during the stages of CAS integrated ACODESA method. It was determined that the students showed improvement in modelling the Riemann sum accurately with CAS, and in choosing to use for loop to determine the area of the rectangles that represented the Riemann sum, and in comprehensibility and acceptability. In the quantitative phase of the study, pre and post-tests were conducted to determine the effect of the CAS-integrated ACODESA method on computational modelling performances of the students. The computational modelling scores were compared with the paired samples t-test. It was found that CAS-integrated ACODESA method made statistically significant effect on computational modelling performances of the students.

Computational modeling and uncertainty prediction of hyperelastic constitutive responses of damaged brain tissue under different temperature and strain rates.

The effect of strain rate and temperature on the hyperelastic material stress-strain characteristics of the damaged porcine brain tissue is evaluated in this present work. The desired constitutive responses are obtained using the commercially available finite element (FE) tool ABAQUS, utilizing 8-noded brick elements. The model's accuracy has been verified by comparing the results from the previously published literature. Further, the stress-strain behavior of the brain tissue is evaluated by varying the damages at various strain rates and temperatures (13, 20, 27, and 37°C) under compression test. Additionally, the sensitivity analysis of the model is computed to check the effect of input parameters, that is, the temperature, strain rate, and damages on the material properties (shear modulus). The modeling and discussion sections enumerate the inclusive features and model capabilities.

Demonstratives as attention tools: Evidence of mentalistic representations within language

Linguistic communication is an intrinsically social activity that enables us to share thoughts across minds. Many complex social uses of language can be captured by domain-general representations of other minds (i.e., mentalistic representations) that externally modulate linguistic meaning through Gricean reasoning. However, here we show that representations of others' attention are embedded within language itself. Across ten languages, we show that demonstratives-basic grammatical words (e.g., "this"/"that") which are evolutionarily ancient, learned early in life, and documented in all known languages-are intrinsic attention tools. Beyond their spatial meanings, demonstratives encode both joint attention and the direction in which the listener must turn to establish it. Crucially, the frequency of the spatial and attentional uses of demonstratives varies across languages, suggesting that both spatial and mentalistic representations are part of their conventional meaning. Using computational modeling, we show that mentalistic representations of others' attention are internally encoded in demonstratives, with their effect further boosted by Gricean reasoning. Yet, speakers are largely unaware of this, incorrectly reporting that they primarily capture spatial representations. Our findings show that representations of other people's cognitive states (namely, their attention) are embedded in language and suggest that the most basic building blocks of the linguistic system crucially rely on social cognition.