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Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

ABSTRACT: We develop an R/Bioconductor package CancerInSilico to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for cell-based mathematical model, implemented for an off-lattice, cell-center Monte Carlo mathematical model. We also adapt this model to simulate the impact of growth suppression by targeted therapeutics in cancer and benchmark simulations against bulk in vitro experimental data. Sensitivity to parameters is evaluated and used to predict the relative impact of variation in cellular growth parameters and cell types on tumor heterogeneity in therapeutic response.

ORGANISM(S): Homo sapiens

PROVIDER: GSE114375 | GEO | 2020/07/09

REPOSITORIES: GEO

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Project description:Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influences the rate and timing of gene expression. In this work we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are determined using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome- wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. MCF-7 cells were mock treated or with 10nM 17b-E2 to seven time points (5', 10', 20', 40', 80', 160', 320') Genome-wide identification of RNA polymerase II (RNAPII) occupancy at different time points following E2 induction of MCF-7 cells

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Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

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