Biology subjects

Gurev, V.

Publications and source records attributed to Gurev, V..

4 recordsLinked to original sources

bioRxiv2023

Inferring parameters of pyramidal neuronexcitability in mouse models of Alzheimer'sdisease using biophysical modeling and deeplearning

Alzheimers disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We introduce a novel parameter inference technique, deep hybrid modeling (DeepHM), that combines deep learning with biophysical modeling to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks (cGANs) to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrate that DeepHM accurately infers parameter distributions of the conductance-based model and outperforms a Markov chain Monte Carlo method on several test cases using synthetic data. We then use DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimers mouse models compared to their wildtype controls at 12 and 24 months. We find that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.

neuroscience↗

bioRxiv2021

Machine Learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSUp to 30%-50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge. ObjectiveThe main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology. Materials and MethodsRetrospective data from 57 patients who underwent CRT device implantation was utilized. Positive response to CRT was defined by a 10% increase in the left ventricular ejection fraction in a year after implantation. For each patient, an anatomical model of the heart and torso was reconstructed from MRI and CT images and tailored to ECG recorded in the participant. The models were used to compute ventricular activation time, ECG duration and electrical dyssynchrony indices during intrinsic rhythm and BiV pacing from active poles of leads. For building a predictive model of CRT response, we used clinical data recorded before CRT device implantation together with model-derived biomarkers of ventricular excitation in the left bundle branch block mode of activation and under BiV stimulation. Several Machine Learning (ML) classifiers and feature selection algorithms were tested on the hybrid dataset, and the quality of predictors was assessed using the area under receiver operating curve (ROC AUC). The classifiers on the hybrid data were compared with ML models built on clinical data only. ResultsThe best ML classifier utilizing a hybrid set of clinical and model-driven data demonstrated ROC AUC of 0.82, an accuracy of 0.82, sensitivity of 0.85, and specificity of 0.78, improving quality over that of ML predictors built on clinical data from much larger datasets. Distance from the LV pacing site to the post-infarction zone and ventricular activation characteristics under BiV pacing were shown as the most relevant model-driven features for CRT response classification. ConclusionOur results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes.

physiology↗

bioRxiv2019

Model order reduction for left ventricular mechanics via congruency training

Computational models of the cardiovascular system and heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. Recent technological advances allow for finite element models of heart ventricles and atria to be customized to medical images and to assimilate electrical and hemodynamic measurements. Optimizing model parameters to physiological data is, however, challenging due to the computational complexity of finite element models. Metaheuristic algorithms and other optimization strategies typically require sampling hundreds of points in the model parameter space before converging to optimal solutions. Similarly, resolving uncertainty of model outputs to input assumptions is difficult for finite element models due to their computational cost. In this paper, we present a novel, multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics. Our approach is centered around well established findings on the similarity between contraction of an isolated muscle and the whole ventricle. Specifically, we demonstrate that simple linear transformations between sarcomere strain (tension) and ventricular volume (pressure) are sufficient to reproduce global pressure-volume outputs of 3-D finite element models even by a reduced model with just a single myocyte unit. We further develop a procedure for congruency training of a surrogate low-order model from multi-scale finite elements, and we construct an example of parameter optimization based on medical images. We discuss how the presented approach might be employed to process large datasets of medical images as well as databases of echocardiographic reports, paving the way towards application of heart mechanics models in the clinical practice.

biophysics↗

bioRxiv2019

Intrinsic structure of model-derived metrics for in silico proarrhytmic riskassessment identified by global sensitivity analysis

Multiscale computational models of heart are being extensively investigated for improved assessment of drug-induced Torsades de Pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics (features) such as action potential duration, net charge carried by ionic currents (qNet) and others have been proposed in the past as potential candidates for classifying TdP risk. However, the criteria for selection of new risk metrics are still poorly justified, and they are often trained/tested only on small datasets. Moreover, classifiers built on derived features have thus far not consistently provided increased prediction accuracies compared to classifiers based on in vitro measurements of drug effects on ion channels (direct features). In this paper, we analyze a large population of virtual drugs to examine systematically the sensitivity of several model-derived features. The influence of different ion channels in regulation of the model-derived features is identified using global sensitivity analysis (GSA). Specifically, the analysis points to key differences in the input parameters that affect several model-derived features and the generation of early afterdepolarizations (EAD), thus opposing the idea that these features and sensitivity to EAD might be strongly correlated. We also demonstrate that previously proposed model-derived features could be well fitted by a linear combination of direct features. This well explains the observed comparable performances of classifiers built on direct features and model-derived features. Combining GSA and simple probability analysis, we also show that the odds of any linear metric constructed from direct features to perform as well as qNet is very low. Nevertheless, despite high predictive power of qNet to separate drugs into correct categories of TdP risk, the GSA results suggest that the actual mechanistic interpretation for qNets improved performance deserves further investigation. In conclusion, analyses like ours can provide more robust feature selection/construction. Improved experimental designs with increased focus on the critical model parameters indicated by GSA can potentially reduce the uncertainties of key model components and result in increased confidence of TdP risk predicted by in silico models. Author SummaryBiophysical models often have extremely involved intrinsic structure. In the majority of research, either complex methods of non-linear dynamics and empirical analysis are employed to explore the underlying structure of cell processes such as transmembrane ionic currents. Global sensitivity analysis (GSA) could be considered as a brute force alternative to study the model relationships between physical processes, discovering the mechanisms responsible for phenomena of interest. As we demonstrated here, GSA application could be extended to explore the structure of features derived from outputs of biophysical models and used in statistical models to build regressions or classifiers. In particular, GSA seems to be valuable to formalize the methods of feature selection/construction that are used for classification of drugs with respect to their cardiotoxicity.

pharmacology and toxicology↗