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MetabolomicsMulti-omic Attributes and Unbiased Computational Modeling for the Prediction of Immunomodulatory Potency of Mesenchymal Stromal Cells
Not available
ORGANISM(S): Homo sapiens 
ST002052 | MetabolomicsWorkbench
MetabolomicsMass spectrometry based metabolomics for in vitro systems pharmacology: Pitfalls, challenges, and computational solutions

INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC...

2017-08-23 | MTBLS401 | MetaboLights
GenomicsComputational modeling and preclinical evaluation support targeting somatic instability for Huntington’s disease treatment.
Computational modeling and preclinical evaluation support targeting somatic instability for Huntington’s disease treatment.
PRJNA1496612 | ENA
TranscriptomicsOptimizing molecular signatures for prostate cancer recurrence
The derivation of molecular signatures indicative of disease status and predictive of subsequent behavior could facilitate the optimal choice of treatment for prostate cancer patients. In this study, we conducted a computational analysis of gene expression profile data obtained from 79 cases, 39 of ...
ORGANISM(S): Homo sapiens 
2010-11-05 | E-GEOD-25136 | biostudies-arrayexpress
ModelsLiX2019 - macrophage polarization and tumor cell plasticity
This model is based on: Computational Modeling of the Crosstalk Between Macrophage Polarization and Tumor Cell Plasticity in the Tumor Microenvironment. Abstract: Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both ca...
2019-09-23 | MODEL1909230002 | BioModels
Immuno-oncology 
ProteomicsStructural Assessment of the Full-Length Tumor Suppressor Protein p53 by Mass Spectrometry-Guided Computational Modeling
The tetrameric tumor suppressor p53 represents a great challenge for 3D structural analysis due to its high degree of intrinsic disorder (ca. 40%). We developed and applied an integrative structural biology approach combining complementary techniques of structural mass spectrometry (MS), namely cros...
ORGANISM(S): Escherichia coli 
2023-06-26 | PXD037030 | Pride
Computational modeling  cross-linking mass spectrometry  dna binding  intrinsically disordered proteins  tumor suppressor p53 
TranscriptomicsProneurogenic ligands defined by modeling developing cortex growth factor communication networks
The neural stem cell decision to self-renew or differentiate is tightly regulated by its microenvironment. Here, we have asked about this microenvironment, focusing on growth factors in the embryonic cortex at a time when it is largely comprised of neural precursor cells (NPCs) and newborn neurons. ...
ORGANISM(S): Mus musculus 
2016-08-23 | E-GEOD-84482 | biostudies-arrayexpress
GenomicsConnecting Transcriptomics with Computational Modeling to Reveal Developmental Adaptations in Pediatric Human Atrial Tissue
Connecting Transcriptomics with Computational Modeling to Reveal Developmental Adaptations in Pediatric Human Atrial Tissue
PRJNA1159205 | ENA
UnknownComputational analysis of 4-1BB-induced NFκB signaling suggests improvements to CAR cell design.
Not available
S-EPMC9413922 | biostudies-literature
UnknownMulti-scale modeling of macrophage-T cell interactions within the tumor microenvironment.
Not available
S-EPMC7790427 | biostudies-literature
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