Human cell lines in sickness and in health: Formal and informative models to understand disease, identify drug targets and to design therapies

Project ID: 2228cd1259 (You will need this ID for your application)

UCL Lead department: Biochemical Engineering

Project Summary:

The use of human cell lines in clinical research spans a period of more than seven decades, pioneered by the use of the controversially sourced immortal HeLa cells. The first realisation of the commercial potential of human cell lines comes in 1990s with the exploration of the use of immortalised HEK293 cells in gene therapies. The advances in genome sequencing, analytical capabilities and the ability to genetically modify mammalian cell lines led to exponential increase in our understanding of the underlying molecular mechanisms governing the behaviour of human cell lines. This progress is coupled with rapid generation of large amounts of data on specific cell lines of commercial interest or those that are utilised to study disease states amassing to publicly available data of 100+TB size for well-studied cell lines such as breast carcinoma or neuroblastoma cells, or human embryonic kidney cells. Despite this accumulation of knowledge, we currently lack formal tools, which integrate the available information in a coherent, interpretable and quantifiable manner. This is possible only though mechanistic models that feed upon available data. Such models are able to provide simulation outcomes relating to different scenarios, which can allow the testing of a wide range of possibilities that are infeasibly large numbered to explore in the physical world, i.e., in the laboratory. These tests can generate further data for active learning to advise the identification of effective drug target candidates or cell line modification strategies to meet desired outcomes. This project will addresses the aforementioned gap in our knowledgebase and will develop formal models of two human cell lines, one with pathophysiological relevance and another with manufacturing relevance. These models will be employed to create design spaces for target identification and will serve as future predictive platforms to support research in cell line development and disease target identification.