Research Projects

In this study, an algorithm driven by statistical modelling paradigm is planned to be built that can be used to observe the dynamic movement of viral load in cells, host protein expression and extracellular viral load under a simulation environment. While creating such an environment, the aim is to come up with a mathematical model that can be used for testing the impact of drugs on the infected cell population and viral replication. First, the study aims to construct a module that will help in visualising the stochasticity of the infection dynamics in a cell population. Secondly, it is planned to incorporate a module for visualising the protein expression dynamics in a cell infected with SARS-Cov-2 and categorise them into different groups using unsupervised machine learning techniques. Thirdly, drug specific modules will be incorporated that can help in monitoring the effect of various drugs targeting angiotensin converting enzyme ACE2, TNF? and systemic cytokines. The novelty of the proposed framework lies in its ability to explain the underlying mechanism of the drug cell interaction leading to reduction in viral replication. Such simulation framework can be used as an interface for testing and screening of drugs before putting them into clinical trial.

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Developed mathematical model to study the dynamics of viral infection, T cell and macrophages and effect of drug administration in reducing SARS-Cov-2 infection. The model includes adaptive and innate immune cells. The study shows that viral growth decreases when both T cells and macrophages act together to counter the infection rather than only T cells. The system of ODEs also reflects the time course of viral load, T cell and macrophages under mild and severe COVID condition. The model was validated using the time course of viral load from COVID-19 patients. Drug targeting the infection rate parameter is most effective in blocking the viral infection. In contrast, drug targeting the clearance rate is relatively less effective in blocking the infection under severe condition. In order to validate the model, a single cell RNA sequencing dataset for SARS-CoV-2 patients has been taken to identify the relative amount of T cell and macrophages under mild and severe conditions. A combination of tSNE and HDBSCAN was used to identify the relative amount of cells present in bronchoaveolar fluid collected from patients.

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