Functional characterization of alternate optimal solutions of Escherichia coli's transcriptional and translational machinery.

TitleFunctional characterization of alternate optimal solutions of Escherichia coli's transcriptional and translational machinery.
Publication TypeJournal Article
Year of Publication2010
AuthorsThiele I, Fleming RMT, Bordbar A, Schellenberger J, Palsson BØ
JournalBiophysical journal
PubMed Date2010 May 19
KeywordsComputational Biology, DNA, Bacterial, Endoribonucleases, Escherichia coli, Escherichia coli Proteins, Gene Expression Profiling, Gene Expression Regulation, Bacterial, Gene Regulatory Networks, Genes, Bacterial, Genetic Engineering, Genome, Bacterial, Genomics, Multienzyme Complexes, Oligonucleotide Array Sequence Analysis, Polyribonucleotide Nucleotidyltransferase, Protein Biosynthesis, Ribosomes, RNA Helicases, RNA, Bacterial, Solutions

The constraint-based reconstruction and analysis approach has recently been extended to describe Escherichia coli's transcriptional and translational machinery. Here, we introduce the concept of reaction coupling to represent the dependency between protein synthesis and utilization. These coupling constraints lead to a significant contraction of the feasible set of steady-state fluxes. The subset of alternate optimal solutions (AOS) consistent with maximal ribosome production was calculated. The majority of transcriptional and translational reactions were active for all of these AOS, showing that the network has a low degree of redundancy. Furthermore, all calculated AOS contained the qualitative expression of at least 92% of the known essential genes. Principal component analysis of AOS demonstrated that energy currencies (ATP, GTP, and phosphate) dominate the network's capability to produce ribosomes. Additionally, we identified regulatory control points of the network, which include the transcription reactions of sigma70 (RpoD) as well as that of a degradosome component (Rne) and of tRNA charging (ValS). These reactions contribute significant variance among AOS. These results show that constraint-based modeling can be applied to gain insight into the systemic properties of E. coli's transcriptional and translational machinery.

Alternate JournalBiophys. J.
PubMed ID20483314



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