Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensionaltemporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identifies step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development. SOURCE: Russell RockneDivision of Mathematical Oncology City of Hope

Pluto Bioinformatics

GSE133642: State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Leukemia Development