Using Single-Cell Data-driven Boolean Network Models to Analyze Prostate Cancer Dynamics

MoriahEchlin

Tampere University

"Using Single-Cell Data-driven Boolean Network Models to Analyze Prostate Cancer Dynamics"

Cancer is a multifaceted disease, with many unique drivers; yet all cancers have a common foundation – the abnormal and malignant behavior of the body’s cells. Broadly, cellular behaviors result from the dynamics of the gene regulatory network (GRN) and genetic mutations can force the GRN into irregular dynamics. Thus, cells can exhibit the pathological properties associated with cancer: unchecked growth, immune evasion, and metastasis. To understand the origins and ramifications of malignant changes to the GRN, we combine clinically relevant single-cell transcriptomic data with a dynamical systems theoretical framework. This approach takes advantage of the system-wide gene correlations and cell state heterogeneity captured in single-cell ‘omics and the temporal and functional structure provided by dynamical systems models. Specifically, we use a Boolean network architecture to convert distinct cellular profiles to dynamical states. Our work focuses on the conversion of single-cell transcriptomic data to informative Boolean states and Boolean network models and their subsequent analysis with the aim of identifying disease-relevant genes, inter-gene dependencies, and cell state dynamics that would not be evident in the original unstructured data.
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