Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction.

TitleGenome-scale models of metabolism and gene expression extend and refine growth phenotype prediction.
Publication TypeJournal Article
Year of Publication2013
AuthorsO'Brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson BØ
JournalMol Syst Biol
Volume9
Pagination693
PubMed Date10/2013
ISSN1744-4292
Abstract

Growth is a fundamental process of life. Growth requirements are well-characterized experimentally for many microbes; however, we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high-level behavior of the cell as a whole. Here, we construct an ME-Model for Escherichia coli-a genome-scale model that seamlessly integrates metabolic and gene product expression pathways. The model computes ∼80% of the functional proteome (by mass), which is used by the cell to support growth under a given condition. Metabolism and gene expression are interdependent processes that affect and constrain each other. We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi-scale phenotypes, ranging from coarse-grained (growth rate, nutrient uptake, by-product secretion) to fine-grained (metabolic fluxes, gene expression levels). Our results unify many existing principles developed to describe bacterial growth.

Alternate JournalMol. Syst. Biol.
PubMed ID24084808
Cover Image: 

Location

Location

417 Powell-Focht Bioengineering Hall

9500 Gilman Drive La Jolla, CA 92093-0412

Contact Us

Contact Us

In Silico Lab:  858-822-1144

Wet Lab:  858-246-1625

FAX:   858-822-3120

Website Concerns: sbrgit@ucsd.edu

User Login