<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Hou J</submitter><funding>NCI NIH HHS</funding><funding>National Institutes of Health</funding><pagination>vdae184</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11600334</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>6(1)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. The aim of this study was to develop a physics-based framework connecting to transcriptomics to predict patient-specific glioblastoma cell migration.&lt;h4>Methods and results&lt;/h4>We applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3 principal physical parameters that govern cell migration: motor number-describing myosin II activity, clutch number-describing adhesion level, and F-actin polymeri</pubmed_abstract><journal>Neuro-oncology advances</journal><pubmed_title>Cell migration simulator-based biomarkers for glioblastoma.</pubmed_title><pmcid>PMC11600334</pmcid><funding_grant_id>U54CA210190</funding_grant_id><funding_grant_id>P01 CA254849</funding_grant_id><funding_grant_id>P01CA254849</funding_grant_id><pubmed_authors>Odde DJ</pubmed_authors><pubmed_authors>Hou J</pubmed_authors><pubmed_authors>McMahon M</pubmed_authors><pubmed_authors>Jubenville T</pubmed_authors><pubmed_authors>Chen CC</pubmed_authors><pubmed_authors>Sarkaria JN</pubmed_authors></additional><is_claimable>false</is_claimable><name>Cell migration simulator-based biomarkers for glioblastoma.</name><description>&lt;h4>Background&lt;/h4>Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. The aim of this study was to develop a physics-based framework connecting to transcriptomics to predict patient-specific glioblastoma cell migration.&lt;h4>Methods and results&lt;/h4>We applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3 principal physical parameters that govern cell migration: motor number-describing myosin II activity, clutch number-describing adhesion level, and F-actin polymeri</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Jan-Dec</publication><modification>2025-04-04T01:45:27.169Z</modification><creation>2025-04-04T01:45:27.169Z</creation></dates><accession>S-EPMC11600334</accession><cross_references><pubmed>39605317</pubmed><doi>10.1093/noajnl/vdae184</doi></cross_references></HashMap>