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Circadian clocks regulate the immune system, rendering humans more susceptible to infections at certain times of the day. Circadian modulation of SARS-CoV-2 infection has not yet been clearly established, nonetheless the circadian control of other respiratory viruses such as influenza A makes apparent the need to study the interaction between circadian rhythms and COVID-19 disease progression. We incorporated circadian oscillations into a mechanistic model of SARS-CoV-2 dynamics and immune response fit to viral load data from COVID-19 patients. The model predicts that circadian variation of parameters associated with the innate immune response and viral death rate lead to faster clearance of the virus, whereas circadian variation of parameters representing the susceptible cell infection rate, the viral production rate, and the adaptive immune response lead to slower clearance of the virus. We then used a model of remdesivir to simulate antiviral therapy. Our model simulations predict that the effectiveness of the treatment depends on the time of day the drug is administered. This prediction is conditional on the plausible, but entirely hypothetical, circadian interactions added to the model. Based on our proof-of-concept modeling results, we advocate for experimental and clinical studies to assess the impact that dosing time of day may have on the efficacy and toxicity of current COVID-19 antiviral drugs. 

Abstract Alzheimer’s 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 used deep hybrid modeling (DeepHM), a recently developed parameter inference technique 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 to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures. We then used DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimer’s mouse models compared to their wildtype controls at 12 and 24 months. We found that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.