University of Illinois Urbana-Champaign

Analysis of the PROM algorithm as a tool to generate genome-scale metabolic-regulatory networks

Caballero, Bozena

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Caballero_Bozena.pdf

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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
Date of Ingest
2012-09-18T21:10:38Z
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.
Graduation Semester
2012-08
Permalink
http://hdl.handle.net/2142/34309
Copyright and License Information
Copyright 2012 Bozena Caballero

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