Analysis of the PROM algorithm as a tool to generate genome-scale metabolic-regulatory networks
Caballero, Bozena
Loading…
Permalink
https://hdl.handle.net/2142/34309
Description
Title
Analysis of the PROM algorithm as a tool to generate genome-scale metabolic-regulatory networks
Author(s)
Caballero, Bozena
Issue Date
2012-09-18T21:10:38Z
Director of Research (if dissertation) or Advisor (if thesis)
Price, Nathan D.
Department of Study
Chemical & Biomolecular Engr
Discipline
Chemical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
probabilistic regulation of metabolism (PROM)
genome-scale metabolic-regulatory networks
growth phenotype predictions
modeling flux changes
Abstract
In this paper, we analyzed the capability of PROM’s algorithm to generate genome--‐scale metabolic--‐regulatory networks, which accurately predict growth phenotypes of transcriptional regulatory mutants under various conditions. E. coli, M. tuberculosis and S. cerevisase were used as model organisms. We showed that PROM could be successfully applied to model less complex systems (E. coli and M. tuberculosis) but not eukaryotes (S. cereavisae). The effects of the accuracy of the metabolic and regulatory networks reconstructions as well as the amount of gene expression data (microarrays) on PROM’s ability to simulate growth phenotypes was analyzed. It was determined that well defined metabolic model and transcriptional regulatory network were crucial for PROM to be predictive. However, accurately represented gene--‐ transcription factor (TF) interactions played a more significant role than the metabolic model. Also, those interactions had to be determined experimentally and not through an inference algorithm (such as ASTRIX). In case of the amount of gene expression data, it was observed that a number of microarrays needed for best PROM’s performance was species specific and incorporation of additional samples resulted in no further improvement of the model. The extension of PROM’s algorithm to predict changes in reaction rates (fluxes) for transcriptional regulatory mutants growing on different media showed that incorporation of Flux Variability Analysis (FVA) was not sufficient for such studies.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
