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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