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

PROVIDER: PRJNA471189 | ENA |

REPOSITORIES: ENA

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

Project description: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.

2020-07-09 | GSE114375 | GEO

robust statistical modeling improves sensitivity of high-throughput rnA structure probing experiments

Project description:Structure probing coupled with high-throughput sequencing holds the potential to revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite major technological advances, intrinsic noise and high coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline which accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome-wide. We demonstrate on two yeast data sets that our method has greatly increased sensitivity, enabling the identification of modified regions on many more transcripts compared with existing pipelines. It also provides confident predictions at much lower coverage levels than previously reported. Our results show that statistical modeling greatly extends the scope and potential of transcriptome-wide structure probing experiments.

2016-11-01 | GSE78208 | GEO

Multicellular modeling of ciliopathy by combining iPS cells and microfluidic airway-on-a-chip technology

Project description:Multicellular modeling of ciliopathy by combining iPS cells and microfluidic airway-on-a-chip technology

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Time-course transcriptome analysis of TF-induced iPSC neurons

Project description:We report on a comprehensive time-course study of transcription factor (TF)-induced iPSC neurons cultured in vitro through an intrinsic maturation program following neurogenesis. Combining the present RNA-seq study with immunocytochemistry and electrophysiological analyses, we determined the transcriptional and morphological sequences of key developmental events associated with postnatal brain-like transition and spinogenesis. TF-induced iPSC neurons successfully acquired structural and functional synaptic maturity, which will critically expand their utility in modeling higher brain functions and disorders.

2023-03-23 | GSE224751 | GEO

Trypanosoma brucei strain:Lister 427

Project description:Mathematical modeling of Trypanosoma brucei transcription

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Systems modeling of oncogenic G-protein and oncogenic GPCR signaling reveals unexpected differences in downstream signaling pathway activation [FAKi_experiment]

Project description:Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease causing mutations to one or more proteins in the network promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLT2R mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas.

2024-05-10 | GSE267153 | GEO

Systems modeling of oncogenic G-protein and oncogenic GPCR signaling reveals unexpected differences in downstream signaling pathway activation [CY_GQ_experiment]

2024-05-10 | GSE267152 | GEO

Modeling Hepatoblastoma

Project description:Modeling Hepatoblastoma

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Rice leaves in the field in 2009

Project description:Time-course transcriptional profiling of rice leaf in the field in 2009. This experiment was performed to validate the results of field transcriptomic modeling. Using 461 field transcriptome data obtained in 2008 (GSE36040; GSE36042; GSE36043; GSE36044; GSE18685) and the corresponding meteorologicla dara, we perfomred statistical modeling of transcriptome.

2012-12-07 | GSE36777 | GEO

Modeling gene regulation from matched expression and chromatin accessibility data

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2017-05-03 | GSE98479 | GEO

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