skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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 Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B)
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. doi:10.3389/fmicb.2019.00597.
St. John, Peter C., and Bomble, Yannick J. Fri . "Approaches to Computational Strain Design in the Multiomics Era". Switzerland. 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},
issn = {1664-302X},
number = ,
volume = 10,
place = {Switzerland},
year = {2019},
month = {4}
}

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

Citation Metrics:
Cited by: 1 work
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.

Save / Share:

Works referenced in this record:

Systems Biology of Metabolism
journal, June 2017


Computational methods in metabolic engineering for strain design
journal, August 2015


Facilitate Collaborations among Synthetic Biology, Metabolic Engineering and Machine Learning
journal, March 2016

  • Wu, Stephen Gang; Shimizu, Kazuyuki; Tang, Joseph Kuo-Hsiang
  • ChemBioEng Reviews, Vol. 3, Issue 2
  • DOI: 10.1002/cben.201500024

Synthetic and systems biology for microbial production of commodity chemicals
journal, April 2016

  • Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.
  • npj Systems Biology and Applications, Vol. 2, Issue 1
  • DOI: 10.1038/npjsba.2016.9

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models
journal, October 2015

  • King, Zachary A.; Lu, Justin; Dräger, Andreas
  • Nucleic Acids Research, Vol. 44, Issue D1
  • DOI: 10.1093/nar/gkv1049

Acceleration Strategies to Enhance Metabolic Ensemble Modeling Performance
journal, September 2017

  • Greene, Jennifer L.; Wäechter, Andreas; Tyo, Keith E. J.
  • Biophysical Journal, Vol. 113, Issue 5
  • DOI: 10.1016/j.bpj.2017.07.018

High-throughput generation, optimization and analysis of genome-scale metabolic models
journal, August 2010

  • Henry, Christopher S.; DeJongh, Matthew; Best, Aaron A.
  • Nature Biotechnology, Vol. 28, Issue 9
  • DOI: 10.1038/nbt.1672

COPASI--a COmplex PAthway SImulator
journal, October 2006


Computational solutions for omics data
journal, April 2013

  • Berger, Bonnie; Peng, Jian; Singh, Mona
  • Nature Reviews Genetics, Vol. 14, Issue 5
  • DOI: 10.1038/nrg3433

From systems biology to metabolically engineered cells — an omics perspective on the development of industrial microbes
journal, October 2018


Approximative kinetic formats used in metabolic network modeling
journal, January 2005

  • Heijnen, Joseph J.
  • Biotechnology and Bioengineering, Vol. 91, Issue 5
  • DOI: 10.1002/bit.20558

A Whole-Cell Computational Model Predicts Phenotype from Genotype
journal, July 2012


A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains
journal, December 2016

  • Khodayari, Ali; Maranas, Costas D.
  • Nature Communications, Vol. 7, Issue 1
  • DOI: 10.1038/ncomms13806

Ensemble Modeling of Metabolic Networks
journal, December 2008


Integration of gene expression data into genome-scale metabolic models
journal, October 2004


Exact stochastic simulation of coupled chemical reactions
journal, December 1977

  • Gillespie, Daniel T.
  • The Journal of Physical Chemistry, Vol. 81, Issue 25
  • DOI: 10.1021/j100540a008

Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints
journal, August 2017

  • Sánchez, Benjamín J.; Zhang, Cheng; Nilsson, Avlant
  • Molecular Systems Biology, Vol. 13, Issue 8
  • DOI: 10.15252/msb.20167411

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
journal, May 2018


From the first drop to the first truckload: commercialization of microbial processes for renewable chemicals
journal, December 2013


Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information
journal, June 2018


In silico method for modelling metabolism and gene product expression at genome scale
journal, January 2012

  • Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem
  • Nature Communications, Vol. 3, Issue 1
  • DOI: 10.1038/ncomms1928

Dynamic modeling of the central carbon metabolism ofEscherichia coli
journal, May 2002

  • Chassagnole, Christophe; Noisommit-Rizzi, Naruemol; Schmid, Joachim W.
  • Biotechnology and Bioengineering, Vol. 79, Issue 1
  • DOI: 10.1002/bit.10288

Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks
journal, December 2017


Network-based prediction of human tissue-specific metabolism
journal, August 2008

  • Shlomi, Tomer; Cabili, Moran N.; Herrgård, Markus J.
  • Nature Biotechnology, Vol. 26, Issue 9
  • DOI: 10.1038/nbt.1487

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow
journal, May 2016


Stochasticity of metabolism and growth at the single-cell level
journal, September 2014

  • Kiviet, Daniel J.; Nghe, Philippe; Walker, Noreen
  • Nature, Vol. 514, Issue 7522
  • DOI: 10.1038/nature13582

13C Metabolic Flux Analysis
journal, July 2001


Metabolome, transcriptome and metabolic flux analysis of arabinose fermentation by engineered Saccharomyces cerevisiae
journal, November 2010


Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models
journal, January 2010

  • Lewis, Nathan E.; Hixson, Kim K.; Conrad, Tom M.
  • Molecular Systems Biology, Vol. 6, Issue 1
  • DOI: 10.1038/msb.2010.47

Genome scale engineering techniques for metabolic engineering
journal, November 2015


Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach
journal, July 2016

  • Saa, Pedro A.; Nielsen, Lars K.
  • Scientific Reports, Vol. 6, Issue 1
  • DOI: 10.1038/srep29635

A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data
journal, September 2014


GillesPy: A Python Package for Stochastic Model Building and Simulation
journal, September 2016

  • Abel, John H.; Drawert, Brian; Hellander, Andreas
  • IEEE Life Sciences Letters, Vol. 2, Issue 3
  • DOI: 10.1109/lls.2017.2652448

COBRApy: COnstraints-Based Reconstruction and Analysis for Python
journal, January 2013

  • Ebrahim, Ali; Lerman, Joshua A.; Palsson, Bernhard O.
  • BMC Systems Biology, Vol. 7, Issue 1
  • DOI: 10.1186/1752-0509-7-74

Metabolic Models of Protein Allocation Call for the Kinetome
journal, December 2017


Evaluation of rate law approximations in bottom-up kinetic models of metabolism
journal, June 2016


Using Genome-scale Models to Predict Biological Capabilities
journal, May 2015


StochKit2: software for discrete stochastic simulation of biochemical systems with events
journal, July 2011


Overflow metabolism in Escherichia coli results from efficient proteome allocation
journal, December 2015

  • Basan, Markus; Hui, Sheng; Okano, Hiroyuki
  • Nature, Vol. 528, Issue 7580
  • DOI: 10.1038/nature15765

Let's talk about flux or the importance of (intracellular) reaction rates
journal, November 2016


MATCONT: A MATLAB package for numerical bifurcation analysis of ODEs
journal, June 2003

  • Dhooge, A.; Govaerts, W.; Kuznetsov, Yu. A.
  • ACM Transactions on Mathematical Software, Vol. 29, Issue 2
  • DOI: 10.1145/779359.779362

Toward Synthetic Biology Strategies for Adipic Acid Production: An  in Silico Tool for Combined Thermodynamics and Stoichiometric Analysis of Metabolic Networks
journal, December 2017

  • Averesch, Nils J. H.; Martínez, Verónica S.; Nielsen, Lars K.
  • ACS Synthetic Biology, Vol. 7, Issue 2
  • DOI: 10.1021/acssynbio.7b00304

Protein identification and expression analysis using mass spectrometry
journal, May 2006


The Impact of Systems Biology on Bioprocessing
journal, December 2017


Stochastic fluctuations in metabolic pathways
journal, May 2007

  • Levine, E.; Hwa, T.
  • Proceedings of the National Academy of Sciences, Vol. 104, Issue 22
  • DOI: 10.1073/pnas.0610987104

Comparative genomics and transcriptomics analysis-guided metabolic engineering of Propionibacterium acidipropionici for improved propionic acid production
journal, December 2017

  • Guan, Ningzi; Du, Bin; Li, Jianghua
  • Biotechnology and Bioengineering, Vol. 115, Issue 2
  • DOI: 10.1002/bit.26478

Translating biochemical network models between different kinetic formats
journal, March 2009


Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis
journal, September 2010

  • Chandrasekaran, Sriram; Price, Nathan D.
  • Proceedings of the National Academy of Sciences, Vol. 107, Issue 41
  • DOI: 10.1073/pnas.1005139107

Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction
journal, January 2013

  • O'Brien, Edward J.; Lerman, Joshua A.; Chang, Roger L.
  • Molecular Systems Biology, Vol. 9, Issue 1
  • DOI: 10.1038/msb.2013.52

Comparison and applications of label-free absolute proteome quantification methods on Escherichia coli
journal, September 2012


Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
journal, October 2016

  • Kim, Minseung; Rai, Navneet; Zorraquino, Violeta
  • Nature Communications, Vol. 7, Issue 1
  • DOI: 10.1038/ncomms13090

Analytics for Metabolic Engineering
journal, September 2015

  • Petzold, Christopher J.; Chan, Leanne Jade G.; Nhan, Melissa
  • Frontiers in Bioengineering and Biotechnology, Vol. 3
  • DOI: 10.3389/fbioe.2015.00135

Bespoke design of whole-cell microbial machines
journal, November 2016


Context-Specific Metabolic Networks Are Consistent with Experiments
journal, May 2008


A Method to Constrain Genome-Scale Models with 13C Labeling Data
journal, September 2015

  • García Martín, Héctor; Kumar, Vinay Satish; Weaver, Daniel
  • PLOS Computational Biology, Vol. 11, Issue 9
  • DOI: 10.1371/journal.pcbi.1004363

13C metabolic flux analysis at a genome-scale
journal, November 2015


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


Model-based metabolism design: constraints for kinetic and stoichiometric models
journal, February 2018

  • Stalidzans, Egils; Seiman, Andrus; Peebo, Karl
  • Biochemical Society Transactions, Vol. 46, Issue 2
  • DOI: 10.1042/bst20170263

Methods for the integration of multi-omics data: mathematical aspects
journal, January 2016


Thermodynamics-Based Metabolic Flux Analysis
journal, March 2007

  • Henry, Christopher S.; Broadbelt, Linda J.; Hatzimanikatis, Vassily
  • Biophysical Journal, Vol. 92, Issue 5
  • DOI: 10.1529/biophysj.106.093138

What is flux balance analysis?
journal, March 2010

  • Orth, Jeffrey D.; Thiele, Ines; Palsson, Bernhard Ø
  • Nature Biotechnology, Vol. 28, Issue 3
  • DOI: 10.1038/nbt.1614

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

Integrated Analysis of Transcriptomic and Proteomic Data
journal, February 2013


Engineering Cellular Metabolism
journal, March 2016


Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples
journal, August 2012


Current state and challenges for dynamic metabolic modeling
journal, October 2016


Quantitative -omic data empowers bottom-up systems biology
journal, June 2018


    Figures / Tables found in this record:

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