Developing and Evaluating Integrated Metabolic Regulatory Models for Microbial Life

Tintle, N., Dordt College
DeJongh, M., Hope College
Lindsey, W., Dordt College
Best, A., Hope College
Creagar, M., University of San Francisco
Disselkoen, C., University of California, San Diego
Fore, R., Brown University
Friend, D., University of Nevada, Reno
Kamp, T., Dordt College
Henry, C. S., Argonne National Laboratory
A number of papers have described methods to include transcriptional regulatory networks (TRNs) in the development of metabolic models. However, in general, these models do not allow for statistical uncertainty in TRN interactions. Furthermore, methods which do allow for uncertainty do so in a non-rigorous manner which, effectively, downweights prior transcriptomics data to the point where it has little impact on the resulting model. To combat these limitations, we developed an integrated metabolic regulatory model (iMRM). This novel approach explicitly models the statistical uncertainty in gene state activity inferences by using gene activity state estimates, and the iMRM process measures the flux through reactions for a sole single-cell organism. This allows for benefits such as differential thresholds for gene activity for different genes, a stronger correlation with experimental flux data, and the application of a non-Boolean confidence metric. Furthermore, this is the first such metabolic model to be grounded on a statistically rigorous foundation.