Prediction Stability in a data-based mechanistic model of sigmaF during sporulation in Bacillus subtilis

TL;DR

This study examines the robustness of a mechanistic model of sigmaF regulation during Bacillus subtilis sporulation by analyzing multiple optimal parameter sets, confirming the model's predictive stability across parameter variations.

Contribution

It introduces a global parameter screening approach to assess prediction stability in a detailed biological network model.

Findings

All parameter sets fitting the data predicted the correct physiological behavior.

The screening identified sensitive and sloppy parameters.

Additional datasets are needed to refine parameter estimates.

Abstract

Mathematical modeling of biological networks can help to integrate a large body of information into a consistent framework, which can then be used to arrive at novel mechanistic insight and predictions. We have previously developed a detailed, mechanistic model for the regulation of {\sigma}F during sporulation in Bacillus subtilis. The model was based on a wide range of quantitative data, and once fitted to the data, the model made predictions that could be confirmed in experiments. However, the analysis was based on a single optimal parameter set. We wondered whether the predictions of the model would be stable for all optimal parameter sets. To that end we conducted a global parameter screen within the physiological parameter ranges. The screening approach allowed us to identify sensitive and sloppy parameters, and highlighted further required datasets during the optimization. Eventually, all parameter sets that reproduced all available data predicted the physiological situation correctly.