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

患者的免疫反应和急性SARS-CoV-2感染结局之间的变异性基础仍然不明确，限制了有针对性的干预机会。描述个人免疫反应的多模式分子和免疫学数据集的产生为解决这一问题提供了机会，但最大限度地提高此类数据集的信息量仍然是一个主要障碍。在这里，我们建议通过应用最先进的综合数学和计算技术来解决这个问题，以便一起分析数据并提取新的见解。我们将使用代数系统生物学方法结合代数几何、数据张量、拓扑数据分析和网络理论对多维和多索引数据进行编码，以识别导致不同疾病严重程度的宿主免疫反应中的异质性特征和细胞驱动因素。我们将把这一点应用于牛津新冠肺炎多组血图谱(战斗)联盟最近生成的数据，其中包括高分辨率临床表型鉴定、细胞血室单细胞图谱的组成、谱系、转录组和表观基因组学、血浆分泌组、血清学、病毒测序、元基因组学和宿主基因分型。考虑到数据的可用性和影响的机会，我们的应用是及时和紧迫的。这项工作将促进医学和数学科学之间的合作，促进跨学科。这项分析将为病理生理学提供新的见解，确定靶向干预的关键网络和节点，从而能够开发免疫调节疗法，并定义可用于验证的个体免疫反应的生物标志物，并使对新冠肺炎的精确医学方法的开发成为可能。

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.