Approaches to Computational Strain Design in the Multiomics Era

St. John, Peter C.; Bomble, Yannick J.

doi:10.3389/fmicb.2019.00597

Title: Approaches to Computational Strain Design in the Multiomics Era

Journal Article · Fri Apr 05 00:00:00 EDT 2019 · Frontiers in Microbiology

DOI:https://doi.org/10.3389/fmicb.2019.00597· OSTI ID:1505251

St. John, Peter C.; Bomble, Yannick J.

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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO)

Grant/Contract Number:: AC3608GO28308

OSTI ID:: 1505251

Alternate ID(s):: OSTI ID: 1507281

Report Number(s):: NREL/JA-2700-73411; 597

Journal Information:: Frontiers in Microbiology, Journal Name: Frontiers in Microbiology Vol. 10; ISSN 1664-302X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: Switzerland

Language:: English

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Cited by: 14 works

Citation information provided by
Web of Science

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