Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA283801 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. 84 BL2 cell-line samples were hybridized to HGU133+2 Affymetrix GeneChips.
2015-12-10 | E-GEOD-68761 | biostudies-arrayexpress
Project description:Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines.
2015-12-10 | GSE68761 | GEO
Project description:With the emergence of various drug-based treatment strategies, many drug response prediction models have been developed to understand their effects. However, in order to gain comprehensive understanding of a drug response, a prediction model should reflect the underlying biological mechanisms, but the current models suffer from interpretability and scalability problems. Machine learning-based prediction models base their predictions on inferred features, which usually are not well correlated with biological mechanisms, posing a challenge on interpretability of its response predictions. In this regard, using Boolean modeling schemes may allow interpretations on mechanisms that contribute to a particular response, but optimizing Boolean models is difficult because of their high dimensional search space and discontinuous loss function. Here, we developed a scalable derivative-free optimizer for weighted sum Boolean network through meta-reinforcement learning. By using graph network and coordinate-wise policy, our learned optimizer can optimize high dimensional Boolean networks containing over 100 parameters of arbitrary structure, showing higher sample efficiency compared with other meta-heuristic algorithms. The optimized Boolean networks successfully predict the drug responses congruent with public databases and in-house experimental data. Moreover, mechanistic analysis of optimized networks shows reliable interpretability of the predictions by meaningful suggestions of known basket trial drug response prediction markers.
2024-04-27 | GSE184731 | GEO
Project description:Adapted Boolean Network Models for Extracellular Matrix Formation
| PRJNA110515 | ENA
Project description:Here we characterise the response of models of ER-positive breast cancer to treatment with the small molecule MDM2 inhibitor NVP-CGM097, a dihydroisoquinolinone derivative currently evaluated in a phase I clinical trial. We show that NVP-CGM097 reduces tumour cell viability of in vitro and in vivo models of endocrine sensitive, endocrine resistant and palbociclib (CDK4/6 inhibitor) resistant p53 wildtype (p53wt) ER-positive breast cancer. NVP-CGM097 synergises with both fulvestrant and palbociclib in models of therapy resistance. Importantly, we identify the key mechanisms of the synergistic interactions between NVP-CGM097 and endocrine therapy, which occurs through the inhibition of E2F Targets and G2M Checkpoint signalling and induction of senescence, rather than depending upon upregulation of p53 dependent apoptotic pathways. Moreover, we find these same pathways are synergistically targeted during the combination treatment of ER positive breast cancer models with NVP-CGM097 and palbociclib. This indicates the genuine potential of MDM2 inhibition as therapy in advanced ER-positive breast cancer as combination endocrine therapy and CDK4/6 inhibitor treatment becomes embedded as standard of care.
2020-04-01 | GSE140758 | GEO
Project description:We investigated changes to murine pancreatic fibroblast gene expression in response to co-culture with 8 murine pancreatic tumour cell sub-populations. Tumours are complex ecosystems where phenotypically diverse tumour cells are embedded in a heterocellular environment. Using an in vitro model of intra-tumoral heterogeneity, we show that clonal tumour cell populations establish distinct interactions with stromal fibroblasts to expand phenotypic diversity across tumour and stromal cell populations. Heterocellular interactions drive differential engagement of tumour cell reciprocal signalling pathways, resulting in normalisation of cell-autonomous differences in MAPK signalling but diversification of AKT signalling. Consequently, tumour cell clones display differential cell-autonomous and non-cell autonomous dependencies. These results demonstrate that tumour-stroma interactions amplify tumour cell autonomous diversity and that our existing perspective on tumour cell heterogeneity underestimates functional diversity.
2019-09-30 | GSE135434 | GEO
Project description:We investigated changes to gene expression in 8 murine pancreatic tumour sub-clones response to co-culture with fibroblasts. Tumours are complex ecosystems where phenotypically diverse tumour cells are embedded in a heterocellular environment. Using an in vitro model of intra-tumoral heterogeneity, we show that clonal tumour cell populations establish distinct interactions with stromal fibroblasts to expand phenotypic diversity across tumour and stromal cell populations. Heterocellular interactions drive differential engagement of tumour cell reciprocal signalling pathways, resulting in normalisation of cell-autonomous differences in MAPK signalling but diversification of AKT signalling. Consequently, tumour cell clones display differential cell-autonomous and non-cell autonomous dependencies. These results demonstrate that tumour-stroma interactions amplify tumour cell autonomous diversity and that our existing perspective on tumour cell heterogeneity underestimates functional diversity.
2019-09-30 | GSE135433 | GEO
Project description:By applying MC EMiNEM (a novel method based on the concept of Nested Effects Models (NEMs) for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription) to the expression data from four gene perturbation studies (three of them unpublished) in Saccharomyces cerevisiae, we hope to derive new insight into the Mediator signaling network and specific transcription factor - Mediator subunit interactions. The structure of the resulting regulatory networks allows us to hypothesize on possible structural changes of the Mediator upon binding of activators or repressors.
2012-06-01 | E-MTAB-1037 | biostudies-arrayexpress
Project description:Synergistic interactions in mixed biofilms
| PRJEB43664 | ENA
Project description:Gene Expression Profiling of Interactions Between Estrogen- and Aryl hydrocarbon-Receptor Signalling Pathways
2007-01-31 | E-MEXP-946 | biostudies-arrayexpress