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Mini Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Microbiol. | doi: 10.3389/fmicb.2019.00597

Approaches to computational strain design in the multiomics era

  • 1National Renewable Energy Laboratory (DOE), United States

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.

Keywords: Constraint-based methods, kinetic metabolic models, machine learning, multiomics, Strain Engineering

Received: 21 Dec 2018; Accepted: 08 Mar 2019.

Edited by:

Yinjie Tang, Washington University in St. Louis, United States

Reviewed by:

Ilya R. Akberdin, Independent researcher
Hector Garcia Martin, Lawrence Berkeley National Laboratory, United States Department of Energy (DOE), United States  

Copyright: © 2019 St John and Bomble. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: PhD. Yannick J. Bomble, National Renewable Energy Laboratory (DOE), Golden, Colorado, United States, yannick.bomble@nrel.gov