Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE114375 | GEO | 2020/07/09
REPOSITORIES: GEO
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Project description:Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico
| PRJNA471189 | ENA
Project description:A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.
2021-02-08 | GSE154932 | GEO
Project description:Trypanosome RNA polymerase II transcription is polycistronic, individual mRNAs being excised by trans splicing and polyadenylation. In this study, we refined the previously published mathematical model for bloodstream form parasites and extended it to the procyclic form. We used the model, together with known mRNA half-lives, to predict the abundances of individual mRNAs, assuming rapid, unregulated mRNA processing; then we compared the results with measured mRNA abundances. Remarkably, the abundances of most mRNAs in procyclic forms are predicted quite well by the model, being largely explained by variations in mRNA decay rates and length. In bloodstream forms substantially more mRNAs are less abundant than predicted. We list mRNAs that are likely to show particularly slow or inefficient processing, either in both forms or with developmental regulation. We also measured ribosome occupancies of all mRNAs in trypanosomes grown in the same conditions as were used to measure mRNA turnover. In procyclic forms there was a weak positive correlation between ribosome density and mRNA half-life, suggesting cross-talk between translation and mRNA decay; ribosome density was related to the proportion of the mRNA on polysomes, indicating control of translation initiation. Ribosomal protein mRNAs in procyclics appeared to be exceptionally rapidly processed but poorly translated. Through this study, we conclude that lLevels of mRNAs in procyclic form trypanosomes are determined mainly by length and mRNA decay, with some control of precursor processing. In bloodstream forms variations in nuclear events play a larger role in transcriptome regulation, suggesting acquisition of new control mechanisms during adaptation to mammalian parasitism. Ribosome profiling and mRNA libraries were constructed in triplicate from in vitro PCF and in duplicate from in vitro T. brucei Lister427, to understand global differntial gene transcription.
2016-03-16 | E-GEOD-79208 | biostudies-arrayexpress
Project description:Affymetrix GeneChip PM-MM probe pair is designed with the intension of measuring non-specific binding. Though the rationale behind the design id that a PM probe is expected to have a larger value than that of the MM probe, there are many exceptions in actual data. We gave an explanation for this inconsistency based on the assumption of functional states of a gene ‘ON/OFF’. Our hypothesis on PM-MM probe pairs is that the logarithmic of PM and MM values have the same distribution when gene is in OFF state. It means that the probability of MM > PM is expected to be equal to that of MM < PM for OFF genes. The validity of the hypothesis was given by inter-platform comparisons using common targets among three different types of platforms. Keywords: Affymetrix Gene Chip; Binomial distribution; Mathematical modeling; Oligonucleotide microarray; ON/OFF genes
2007-12-31 | GSE8353 | GEO
Project description:Trypanosome RNA polymerase II transcription is polycistronic, individual mRNAs being excised by trans splicing and polyadenylation. In this study, we refined the previously published mathematical model for bloodstream form parasites and extended it to the procyclic form. We used the model, together with known mRNA half-lives, to predict the abundances of individual mRNAs, assuming rapid, unregulated mRNA processing; then we compared the results with measured mRNA abundances. Remarkably, the abundances of most mRNAs in procyclic forms are predicted quite well by the model, being largely explained by variations in mRNA decay rates and length. In bloodstream forms substantially more mRNAs are less abundant than predicted. We list mRNAs that are likely to show particularly slow or inefficient processing, either in both forms or with developmental regulation. We also measured ribosome occupancies of all mRNAs in trypanosomes grown in the same conditions as were used to measure mRNA turnover. In procyclic forms there was a weak positive correlation between ribosome density and mRNA half-life, suggesting cross-talk between translation and mRNA decay; ribosome density was related to the proportion of the mRNA on polysomes, indicating control of translation initiation. Ribosomal protein mRNAs in procyclics appeared to be exceptionally rapidly processed but poorly translated. Through this study, we conclude that lLevels of mRNAs in procyclic form trypanosomes are determined mainly by length and mRNA decay, with some control of precursor processing. In bloodstream forms variations in nuclear events play a larger role in transcriptome regulation, suggesting acquisition of new control mechanisms during adaptation to mammalian parasitism.
2016-03-16 | GSE79208 | GEO
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
2014-05-19 | E-GEOD-44800 | biostudies-arrayexpress
Project description:Advancing precision medicine in the field of cancer is still curbed by the fact that a rational basis to predict treatment outcome is missing. To unravel the mechanism behind targeted drugs (trastuzumab, pertuzumab, erlotinib), mathematical modeling employing ordinary differential equations (ODE) was combined with wet-lab experimentation. Experimentation relied on systematic perturbation experiments to monitor the signaling-response towards drugs in the context of EGF signaling in the HER2+ cell lines SKBR3 and HCC1954.
2016-12-31 | GSE55891 | GEO
Project description:Elucidation of multivalent interactions involving both DNA and histone post-translational-modifications (PTMs) is essential for providing insight into complex biological functions. We report here the high resolution crystal structure of the N-terminal triple reader module (PHD-Bromo-PWWP) of ZMYND8, which forms a stable unit, capable of simultaneously recognizing histone PTMs and providing a charged platform for DNA interaction. Monte Carlo simulations provide a model whereby the reader module directly engages the core nucleosome particle, extracting the entire histone H3 tail and initiating contacts with the DNA super-coil structure. Systematic mutation of these interaction interfaces reveals cooperativity within the ensemble. Single domain disruption destroys the functional network of interactions initiated by ZMYND8, which impairs recruitment to sites of DNA damage and alters transcription. Taken together, our results establish an important role of the ZMYND8 multivalent reader module in nucleosome binding and chromatin function.
2021-04-30 | GSE75624 | GEO
Project description:The principle of tumor growth is one of the most fundamental aspects in cancer biology and it remains to be superficially understood. Here we developed a set of genetic and mathematical tools for integrative analysis of mouse glioblastoma (GBM) progression. Quantitative temporal imaging of mouse GBM and mathematical modeling suggest that mouse GBM grows exponentially, which led to the discovery that a sustainable exponential growth requires outward migrating brain tumor stem cells (BTSCs). Quantitative single cell tracing of BTSCs in vivo unravels symmetric expansion of BTSCs, asymmetric differentiation, and a non-reversible differentiation process during tumor progression. Mosaic tracing of BTSCs and non-BTSCs reveals cellular dynamic changes and turnover. These experimentally collected parameters were integrated step by step to a quantitative mathematical model of GBM growth, which faithfully predicts tumor cellular architecture, tumor response to chemotherapy and BTSC therapy. Bulk and single cell RNA-Seq reveals distinct molecular hierarchy and molecular heterogeneity in BTSCs, which was confirmed by analyzing human GBM single-cell RNA-Seq results. This study reveals fundamental developmental principles that govern tumor growth, which provides insights into understanding cancer development, tumor heterogeneity and GBM therapy.
2022-02-02 | GSE126212 | GEO
Project description:The mechanism by which rs6983267 at 8q24.21 increases cancer risk is unclear for the lack of protein?coding genes in the region although the variant is strongly associated with cancer pathogenesis. Here we identify long non-coding RNAs (lncRNAs) near the 8q24 region and show that expression of lncRNA CARLo-5 is significantly correlated with the risk allele of the cancer-associated variant rs6883267. We further report that the 8q24 enhancer region including the variant directly interacts with active regulatory region of the lncRNA CARLo-5 promtoer. Finally, we demonstrate that CARLo-5 has an oncogenic function by regulating cell proliferation and cell-cycle. Our data provide new insight of disease-related variants in a gene desert. Comparison and identification of cell cycle-related genes differentially expressed by CARLo-5 knockdown using Nanostring analysis in HCT116 cells.
2014-03-05 | E-GEOD-48877 | biostudies-arrayexpress