Dataset Information

Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer.

ABSTRACT: Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues. Predicted chemokine and cytokine output profiles from multi-cell computational models of gingival epithelial keratinocytes (GE KER), dendritic cells (DC), and helper T lymphocytes (HTL) exposed to lipopolysaccharide (LPS) or synthetic triacylated lipopeptide (Pam3CSK4) as well as multi-cell computational models of multiple myeloma (MM) and DC were validated using the observed chemokine and cytokine responses from the same cell type combinations grown in laboratory multi-cell cultures with accuracy. Predicted and observed chemokine and cytokine responses of GE KER?+?DC?+?HTL exposed to LPS and Pam3CSK4 matched 75% (15/20, p?=?0.02069) and 80% (16/20, P?=?0.005909), respectively. Multi-cell computational models became 'personalized' when cell line-specific genomic data were included into simulations, again validated with the same cell lines grown in laboratory multi-cell cultures. Here, predicted and observed chemokine and cytokine responses of MM cells lines MM.1S and U266B1 matched 75% (3/4) and MM.1S and U266B1 inhibition of DC marker expression in co-culture matched 100% (6/6). Multi-cell computational models have the potential to identify approaches altering the predicted disease-associated output profiles, particularly as high throughput screening tools for anti-inflammatory or immuno-oncology treatments of inflamed multi-cellular tissues and the tumor microenvironment.

SUBMITTER: Fischer CL

PROVIDER: S-EPMC6659691 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer.

Fischer Carol L CL Bates Amber M AM Lanzel Emily A EA Guthmiller Janet M JM Johnson Georgia K GK Singh Neeraj Kumar NK Kumar Ansu A Vidva Robinson R Abbasi Taher T Vali Shireen S Xie Xian Jin XJ Zeng Erliang E Brogden Kim A KA

Scientific reports 20190726 1

Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues. Predicted chemokine and cytokine output profiles from multi-cell computational models of gingival epithelial keratinocytes (GE KER), dendritic cells (DC), and helper T lymphocytes (HTL) exposed to lipopolysaccharid ...[more]

PMID: 31350446

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Dataset Information

Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer.

Publications

Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer.

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