Application of multiscale algebra and topology to understanding heterogeneity in the immune response to SARS CoV 2 infection

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The basis of variability between patients in their immune response and outcome from acute SARS-Cov-2 infection remains poorly defined, limiting opportunities for targetted intervention. The generation of multi-modal molecular and immunological data sets profiling the immune response across individuals and over time provides opportunities to address this but maximising the informativeness of such datasets remains a major roadblock. Here, we propose to address this through application of state-of-the-art integrative mathematical and computational techniques to analyse data together and extract novel insights. We will use algebraic systems biology approaches to combine algebraic geometry, data tensors, topological data analysis and network theory to encode multidimensional and multi-indexed data in order to identify signatures and cellular drivers of heterogeneity in the host immune response leading to different disease severity. We will apply this to data recently generated by the Oxford COVID-19 Multi-Omic Blood ATlas (COMBAT) consortium which includes high resolution clinical phenotyping, single cell profiling of the cellular blood compartment for composition, repertoire, transcriptomics and epigenomics, the plasma secretome, serology, viral sequencing, metagenomics and host genotyping. Our application is timely and urgent given availability of data and opportunity for impact. The work will promote collaboration between medical and mathematical sciences, promoting cross-disciplinarity. The analysis will provide novel insights into pathophysiology, identify key networks and nodal points for targetted intervention that will enable development of immunmodulatory therapy, and define biomarkers informative for the individual immune response that can be taken forward for validation and enable development of a precision medicine approach to COVID-19.