Computational Model of Nanocarrier Adhesion to Mouse and Human Tissues

A collection of results from a biophysically inspired computational model simulating the adhesion of functionalized nanocarriers to cell surfaces. The model accounts for protein expression, membrane mechanics, and entropic factors, with predictions validated against in vivo experiments targeting mouse lung, heart, kidney, liver, and spleen tissues. The author is N. Ramakrishnan, and the data was last updated in June 2020.

Use Cases

• Validate model predictions of nanocarrier avidity against in vivo experimental data for tissues like lung and liver.
• Analyze the sensitivity of computed NC avidity to input parameters such as ligand density and receptor expression.
• Predict partitioning coefficients of functionalized NCs for human tissues using the provided computational framework.
• Investigate the role of specific factors like membrane bending mechanics and entropy loss upon binding in tissue targeting.

Strengths

• Model predictions show quantitative agreement with in vivo experiments for multiple mouse organs.
• The computational framework systematically accounts for proteomic factors, mechanical factors, and entropic contributions.
• The dataset is associated with a peer-reviewed research publication and is published under a CC0 1.0 public domain license.

Limitations

• The dataset is derived from a computational model, not direct experimental measurements, and its accuracy depends on model assumptions.
• Sample data and specific file formats are unavailable, limiting immediate usability without further context from the associated publication.
• The data is from 2020 and may not reflect the most recent advancements in nanocarrier design or biological understanding.

Provenance

Source

Dryad digital repository.

Collection Method

Output from a computational model simulating multivalent receptor-ligand interactions and membrane mechanics.

Freshness

Last updated on 2020-06-30.

Geography

Model predictions are for mouse tissues (lung, heart, kidney, liver, spleen) and include predictions for human tissues.