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Title: Approaches to Computational Strain Design in the Multiomics Era

Abstract

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:
;
Publication Date:
Research Org.:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO)
OSTI Identifier:
1505251
Alternate Identifier(s):
OSTI ID: 1507281
Report Number(s):
NREL/JA-2700-73411
Journal ID: ISSN 1664-302X; 597
Grant/Contract Number:  
AC3608GO28308
Resource Type:
Journal Article: Published Article
Journal Name:
Frontiers in Microbiology
Additional Journal Information:
Journal Name: Frontiers in Microbiology Journal Volume: 10; Journal ID: ISSN 1664-302X
Publisher:
Frontiers Research Foundation
Country of Publication:
Switzerland
Language:
English
Subject:
09 BIOMASS FUELS; 59 BASIC BIOLOGICAL SCIENCES; constraint-based methods; kinetic metabolic models; machine learning; multiomics; strain engineering

Citation Formats

St. John, Peter C., and Bomble, Yannick J. Approaches to Computational Strain Design in the Multiomics Era. Switzerland: N. p., 2019. Web. doi:10.3389/fmicb.2019.00597.
St. John, Peter C., & Bomble, Yannick J. Approaches to Computational Strain Design in the Multiomics Era. Switzerland. https://doi.org/10.3389/fmicb.2019.00597
St. John, Peter C., and Bomble, Yannick J. 2019. "Approaches to Computational Strain Design in the Multiomics Era". Switzerland. https://doi.org/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},
url = {https://www.osti.gov/biblio/1505251}, journal = {Frontiers in Microbiology},
issn = {1664-302X},
number = ,
volume = 10,
place = {Switzerland},
year = {Fri Apr 05 00:00:00 EDT 2019},
month = {Fri Apr 05 00:00:00 EDT 2019}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at https://doi.org/10.3389/fmicb.2019.00597

Citation Metrics:
Cited by: 14 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: Overview of computational techniques in the Learn step. Omics datasets in Test can be interpreted through a number of different computational strategies.

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Figures / Tables found in this record:

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.