(507f) Reconstruction of Integrated Metabolic and Transcriptional Regulatory Network Models As a Platform for Metabolic Engineering

Authors: 
Kim, J., University of Wisconsin-Madison
Reed, J. L., University of Wisconsin-Madison


Computational models of metabolic and transcriptional regulatory networks have been useful for metabolic engineering of microbial systems. While genome-scale metabolic network models are being rapidly developed for many organisms, the reconstruction of genome-scale transcriptional regulatory networks has been challenging due to the limited availability of experimental data and computational tools. A number of computational approaches based on statistical inference have been developed to reconstruct transcriptional regulatory network models, but these models often cannot be directly integrated with metabolic network models. Among different types of models, a Boolean model of transcriptional regulatory networks provides mechanistic details and can be easily combined with a metabolic model to yield a functional integrated model [1]. We previously developed an automated approach to integrate and refine metabolic and transcriptional regulatory network models using large-scale growth phenotype data [2], and also developed a computational method to identify metabolic engineering strategies using these integrated models [3]. Here, we describe an automated approach for reconstruction of Boolean transcriptional regulatory network models by integrating genome-scale metabolic models and high-throughput datasets including protein-DNA binding, gene expression, and growth phenotypes. The resulting integrated metabolic and transcriptional regulatory network models are therefore consistent with multiple datasets, and can be easily updated as new experimental data becomes available. The developed approach will facilitate the reconstruction of such models for different organisms and their applications in metabolic engineering.

1. Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature 429: 92-96.

2. Barua D, Kim J, Reed JL (2010) An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLoS Computational Biology 6: e1000970.

3. Kim J, Reed JL (2010) OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains. BMC Systems Biology 4: 53.