Reducing Uncertainty Through Mutual Information in Structural and Systems Biology

TL;DR

This paper introduces a method that leverages structural biology predictions to enhance systems biology models, reducing the need for extensive experimental data and enabling the evaluation of new hypotheses.

Contribution

The paper presents a novel approach combining structural biology and systems biology to improve model predictions and hypothesis evaluation without additional experimental data.

Findings

Structural biology data can augment systems biology models effectively.

The method reduces the need for costly experimental data.

Systems biology models can evaluate structural hypotheses.

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.