TRIMER: Transcription Regulation Integrated with Metabolic Regulation
Puhua Niu,
Maria J. Soto,
Byung-Jun Yoon,
Edward R. Dougherty,
Francis J. Alexander,
Ian Blaby,
Xiaoning Qian
Affiliations
Puhua Niu
Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX, 77843, USA
Maria J. Soto
US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
Byung-Jun Yoon
Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX, 77843, USA; Brookhaven National Laboratory, Computational Science Initiative, Upton, NY, 11973, USA
Edward R. Dougherty
Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX, 77843, USA
Francis J. Alexander
Brookhaven National Laboratory, Computational Science Initiative, Upton, NY, 11973, USA
Ian Blaby
US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Corresponding author
Xiaoning Qian
Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX, 77843, USA; Brookhaven National Laboratory, Computational Science Initiative, Upton, NY, 11973, USA; Corresponding author
Summary: There has been extensive research in predictive modeling of genome-scale metabolic reaction networks. Living systems involve complex stochastic processes arising from interactions among different biomolecules. For more accurate and robust prediction of target metabolic behavior under different conditions, not only metabolic reactions but also the genetic regulatory relationships involving transcription factors (TFs) affecting these metabolic reactions should be modeled. We have developed a modeling and simulation pipeline enabling the analysis of Transcription Regulation Integrated with Metabolic Regulation: TRIMER. TRIMER utilizes a Bayesian network (BN) inferred from transcriptomes to model the transcription factor regulatory network. TRIMER then infers the probabilities of the gene states relevant to the metabolism of interest, and predicts the metabolic fluxes and their changes that result from the deletion of one or more transcription factors at the genome scale. We demonstrate TRIMER’s applicability to both simulated and experimental data and provide performance comparison with other existing approaches.
