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Title: Approaches to Computational Strain Design in the Multiomics Era

Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design Build Test Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representationsmore » of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.« less
Authors:
ORCiD logo [1] ;  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Report Number(s):
NREL/JA-2700-73411
Journal ID: ISSN 1664-302X
Grant/Contract Number:
AC3608GO28308
Type:
Published Article
Journal Name:
Frontiers in Microbiology
Additional Journal Information:
Journal Volume: 10; Journal ID: ISSN 1664-302X
Publisher:
Frontiers Research Foundation
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B)
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; 59 BASIC BIOLOGICAL SCIENCES; constraint-based methods; kinetic metabolic models; machine learning; multiomics; strain engineering
OSTI Identifier:
1505251
Alternate Identifier(s):
OSTI ID: 1507281

St. John, Peter C., and Bomble, Yannick J.. Approaches to Computational Strain Design in the Multiomics Era. United States: N. p., Web. doi:10.3389/fmicb.2019.00597.
St. John, Peter C., & Bomble, Yannick J.. Approaches to Computational Strain Design in the Multiomics Era. United States. doi:10.3389/fmicb.2019.00597.
St. John, Peter C., and Bomble, Yannick J.. 2019. "Approaches to Computational Strain Design in the Multiomics Era". United States. doi:10.3389/fmicb.2019.00597.
@article{osti_1505251,
title = {Approaches to Computational Strain Design in the Multiomics Era},
author = {St. John, Peter C. and Bomble, Yannick J.},
abstractNote = {Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design Build Test Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representations of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.},
doi = {10.3389/fmicb.2019.00597},
journal = {Frontiers in Microbiology},
number = ,
volume = 10,
place = {United States},
year = {2019},
month = {4}
}

Works referenced in this record:

Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli
journal, June 2013

Ensemble modeling for strain development of l-lysine-producing Escherichia coli
journal, July 2009
  • Contador, Carolina A.; Rizk, Matthew L.; Asenjo, Juan A.
  • Metabolic Engineering, Vol. 11, Issue 4-5, p. 221-233
  • DOI: 10.1016/j.ymben.2009.04.002