共 46 条
An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli
被引:60
作者:
Carrera, Javier
[1
]
Estrela, Raissa
[2
]
Luo, Jing
[1
]
Rai, Navneet
[1
]
Tsoukalas, Athanasios
[1
,3
]
Tagkopoulos, Ilias
[1
,3
]
机构:
[1] Univ Calif Davis, UC Davis Genome Ctr, Davis, CA 95616 USA
[2] Univ Calif Berkeley, Dept Mol & Cell Biol, Berkeley, CA 94720 USA
[3] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 USA
基金:
美国国家科学基金会;
关键词:
genome engineering;
genome-scale model;
model-driven experimentation;
predictive modeling and integration;
systems and synthetic biology;
SCALE METABOLIC RECONSTRUCTIONS;
GENE NETWORK INFERENCE;
WHOLE-CELL SIMULATION;
REGULATORY NETWORKS;
COMPUTATIONAL MODEL;
EXPRESSION;
ENVIRONMENTS;
VARIABILITY;
ANNOTATION;
VALIDATION;
D O I:
10.15252/msb.20145108
中图分类号:
Q5 [生物化学];
Q7 [分子生物学];
学科分类号:
071010 ;
081704 ;
摘要:
Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems.
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