Reducing Uncertainty through Mutual Information in Structural and Systems Biology

Vincent Zaballa, Elliot E Hui

Keywords: Sysems biology, structural biology, probabilistic machine learning

TL;DR: We describe a method that combines structural biology predictions with systems biology that helps support hypotheses in both areas.

Abstract: Systems biology models are useful models of complex biological systems that may require a large amount of experimental data to fit each model's parameters or to approximate a likelihood function. These models range from a few to thousands of parameters depending on the complexity of the biological system modeled, potentially making the task of fitting parameters to the model difficult - especially when new experimental data cannot be gathered. We demonstrate a method that uses structural biology predictions to augment systems biology models to improve systems biology models' predictions without having to gather more experimental data. Additionally, we show how systems biology models' predictions can help evaluate novel structural biology hypotheses, which may also be expensive or infeasible to validate.

Submission Number: 67