Homo sapiens
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PROVIDER: PRJNA377298 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:LASSIM -a network inference toolbox for genome-wide mechanistic modeling
| PRJNA259237 | ENA
Project description:LASSIM -a network inference toolbox for genome-wide mechanistic modeling [MAF, MYB & GATA3 siRNA]
| PRJNA275286 | ENA
Project description:LASSIM -a network inference toolbox for genome-wide mechanistic modeling [Time-series polarization: CD4+ T cells toward Th2]
| PRJNA259241 | ENA
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE60683 | GEO
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE95508 | GEO
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE65880 | GEO
Project description:Recent and ongoing revolutions in measurement technologies imply completely new possibilities for genome research: today, time-resolved, quantitative, and systems-level data are available. Nevertheless, without a corresponding revolution in methods for data analysis, these new data tend to drown researchers and doctors, rather than provide clear and useful insights. Such new methods are developed within the field of systems biology. Systems biology has two main approaches: mechanistically detailed and well-determined simulation models for small subsystems, and more approximative statistical models for the entire genome. However, there are few, if any, methods that combine the strengths of these two approaches. Herein, we present LASSIM, a new simulation-based approach, which can be applied to systems of the size of the entire genome. The superior performance of LASSIM is demonstrated in three examples: i) an example with simulated data shows that unlike traditional large-scale methods, LASSIM correctly identifies the true behavior between measured data-points, ii) LASSIM outperforms the winner of a previous DREAM challenge, the most competitive benchmarking approach available, iii) based on new data from TH2 differentiation, LASSIM identifies a first mechanistic model for the entire genome. The key predictions of this model are typically enriched for DNA bindings, which suggests that most predicted interactions are direct. Moreover, in silico knockdowns were experimentally validated. In summary, LASSIM opens the door to a new type of model-based data analysis: to models that combine the strengths of reliable mechanistic models with truly systems-level data.
2017-06-12 | GSE60681 | GEO
Project description:Recent and ongoing revolutions in measurement technologies imply completely new possibilities for genome research: today, time-resolved, quantitative, and systems-level data are available. Nevertheless, without a corresponding revolution in methods for data analysis, these new data tend to drown researchers and doctors, rather than provide clear and useful insights. Such new methods are developed within the field of systems biology. Systems biology has two main approaches: mechanistically detailed and well-determined simulation models for small subsystems, and more approximative statistical models for the entire genome. However, there are few, if any, methods that combine the strengths of these two approaches. Herein, we present LASSIM, a new simulation-based approach, which can be applied to systems of the size of the entire genome. The superior performance of LASSIM is demonstrated in three examples: i) an example with simulated data shows that unlike traditional large-scale methods, LASSIM correctly identifies the true behavior between measured data-points, ii) LASSIM outperforms the winner of a previous DREAM challenge, the most competitive benchmarking approach available, iii) based on new data from TH2 differentiation, LASSIM identifies a first mechanistic model for the entire genome. The key predictions of this model are typically enriched for DNA bindings, which suggests that most predicted interactions are direct. Moreover, in silico knockdowns were experimentally validated. In summary, LASSIM opens the door to a new type of model-based data analysis: to models that combine the strengths of reliable mechanistic models with truly systems-level data.
2024-01-26 | GSE56434 | GEO
Project description:We applied quantitative mass spectrometry (MS)-based proteomics to study the roles of Cbl and Cbl-b in long-term signaling responses related to neurite outgrowth and differentiation of SH-SY5Y neuroblastoma cells. Using stable isotope labeling by amino acids in cell culture (SILAC) and tandem mass tag (TMT)-labeling in combination with off-line high-pH reversed-phase fractionation and LC-MS/MS we analyzed how Cbl and Cbl-b depletion by siRNA affected the proteome, phosphoproteome and ubiquitylome of the neuroblastoma cells. SILAC proteome SILAC (Light Arg0/Lys0, medium Arg6/Lys4, heavy Arg10/Lys8) SH-SY5Y cells were treated with Cbl and Cbl-b or control (GFP) siRNA for 72 hours. For combined stimulation with ligand cocktail (FGF-2, IGF-1 PDGF-BB, TGFα) cells were treated with ligands for 48 h. Samples were analyzed in triplicates with set-up as described below: Set-up 1, 3 replicates (R1-3): Light: siGFP, Heavy: siCbl/siCbl-b Set-up 2, 3 replicates (E1-3): Light: siGFP + ligand cocktail, Medium: siCbl/siCbl-b, Heavy: siCbl/siCbl-b + ligand cocktail TMT phosphoproteome and proteome SH-SY5Y cells were treated with Cbl and Cbl-b siRNA, control (GFP) siRNA or Retinoic acid (RA) for 24 hours. Samples were prepared in triplicates and labelled with TMT10-plex reagents according to the set-up below: TMT10-126: siGFP E1 TMT10-127N: siCbl/siCbl-b E1 TMT10-127C: Retinoic acid E1 TMT10-128N: siGFP E2 TMT10-128C: siCbl/siCbl-b E2 TMT10-129N: Retinoic acid E2 TMT10-129C: siGFP E3 TMT10-130N: siCbl/siCbl-b E3 TMT10-130C: Retinoic acid E3 TMT10-131: Mix of the 9 samples SILAC Ubiquitin pulldown SILAC (Light Arg0/Lys0, heavy Arg10/Lys8) SH-SY5Y cells were treated with Cbl and Cbl-b or control (GFP) siRNA for 24 hours. Samples were analyzed in duplicates with set-up as described below: Light: siGFP, Heavy: siCbl/siCbl-b
2021-03-30 | PXD020389 | Pride
Project description:We report the development of a new computational method to assess differences in cell-cell interactions between conditions through utilizing single-cell RNA sequencing data. The pipeline, known as Cell Interaction Network Inference from Single-cell Expression data (CINS), combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie these interactions.
2022-09-28 | GSE165638 | GEO