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 »
- 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}
}
Web of Science
Figures / Tables:
Works referenced in this record:
Systems Biology of Metabolism
journal, June 2017
- Nielsen, Jens
- Annual Review of Biochemistry, Vol. 86, Issue 1
Computational methods in metabolic engineering for strain design
journal, August 2015
- Long, Matthew R.; Ong, Wai Kit; Reed, Jennifer L.
- Current Opinion in Biotechnology, Vol. 34
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
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
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
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
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
COPASI--a COmplex PAthway SImulator
journal, October 2006
- Hoops, S.; Sahle, S.; Gauges, R.
- Bioinformatics, Vol. 22, Issue 24
Computational solutions for omics data
journal, April 2013
- Berger, Bonnie; Peng, Jian; Singh, Mona
- Nature Reviews Genetics, Vol. 14, Issue 5
The discrepancy between data for and expectations on metabolic models: How to match experiments and computational efforts to arrive at quantitative predictions?
journal, April 2018
- Tummler, Katja; Klipp, Edda
- Current Opinion in Systems Biology, Vol. 8
From systems biology to metabolically engineered cells — an omics perspective on the development of industrial microbes
journal, October 2018
- Becker, Judith; Wittmann, Christoph
- Current Opinion in Microbiology, Vol. 45
Approximative kinetic formats used in metabolic network modeling
journal, January 2005
- Heijnen, Joseph J.
- Biotechnology and Bioengineering, Vol. 91, Issue 5
A Whole-Cell Computational Model Predicts Phenotype from Genotype
journal, July 2012
- Karr, Jonathan R.; Sanghvi, Jayodita C.; Macklin, Derek N.
- Cell, Vol. 150, Issue 2
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
Ensemble Modeling of Metabolic Networks
journal, December 2008
- Tran, Linh M.; Rizk, Matthew L.; Liao, James C.
- Biophysical Journal, Vol. 95, Issue 12
Integration of gene expression data into genome-scale metabolic models
journal, October 2004
- Åkesson, Mats; Förster, Jochen; Nielsen, Jens
- Metabolic Engineering, Vol. 6, Issue 4
Exact stochastic simulation of coupled chemical reactions
journal, December 1977
- Gillespie, Daniel T.
- The Journal of Physical Chemistry, Vol. 81, Issue 25
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
A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data
journal, May 2018
- Costello, Zak; Martin, Hector Garcia
- npj Systems Biology and Applications, Vol. 4, Issue 1
From the first drop to the first truckload: commercialization of microbial processes for renewable chemicals
journal, December 2013
- Van Dien, Stephen
- Current Opinion in Biotechnology, Vol. 24, Issue 6
Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information
journal, June 2018
- Larsen, Peter E.; Zerbs, Sarah; Laible, Philip D.
- mSystems, Vol. 3, Issue 3
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
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
Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks
journal, December 2017
- Saa, Pedro A.; Nielsen, Lars K.
- Biotechnology Advances, Vol. 35, Issue 8
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
Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow
journal, May 2016
- Brunk, Elizabeth; George, Kevin W.; Alonso-Gutierrez, Jorge
- Cell Systems, Vol. 2, Issue 5
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
13C Metabolic Flux Analysis
journal, July 2001
- Wiechert, Wolfgang
- Metabolic Engineering, Vol. 3, Issue 3
Metabolome, transcriptome and metabolic flux analysis of arabinose fermentation by engineered Saccharomyces cerevisiae
journal, November 2010
- Wisselink, H. Wouter; Cipollina, Chiara; Oud, Bart
- Metabolic Engineering, Vol. 12, Issue 6
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
Genome scale engineering techniques for metabolic engineering
journal, November 2015
- Liu, Rongming; Bassalo, Marcelo C.; Zeitoun, Ramsey I.
- Metabolic Engineering, Vol. 32
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
A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data
journal, September 2014
- Khodayari, Ali; Zomorrodi, Ali R.; Liao, James C.
- Metabolic Engineering, Vol. 25
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
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
Metabolic Models of Protein Allocation Call for the Kinetome
journal, December 2017
- Nilsson, Avlant; Nielsen, Jens; Palsson, Bernhard O.
- Cell Systems, Vol. 5, Issue 6
Evaluation of rate law approximations in bottom-up kinetic models of metabolism
journal, June 2016
- Du, Bin; Zielinski, Daniel C.; Kavvas, Erol S.
- BMC Systems Biology, Vol. 10, Issue 1
Using Genome-scale Models to Predict Biological Capabilities
journal, May 2015
- O’Brien, Edward J.; Monk, Jonathan M.; Palsson, Bernhard O.
- Cell, Vol. 161, Issue 5
StochKit2: software for discrete stochastic simulation of biochemical systems with events
journal, July 2011
- Sanft, K. R.; Wu, S.; Roh, M.
- Bioinformatics, Vol. 27, Issue 17
Overflow metabolism in Escherichia coli results from efficient proteome allocation
journal, December 2015
- Basan, Markus; Hui, Sheng; Okano, Hiroyuki
- Nature, Vol. 528, Issue 7580
Let's talk about flux or the importance of (intracellular) reaction rates
journal, November 2016
- Blank, Lars M.
- Microbial Biotechnology, Vol. 10, Issue 1
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
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
Protein identification and expression analysis using mass spectrometry
journal, May 2006
- Kolker, Eugene; Higdon, Roger; Hogan, Jason M.
- Trends in Microbiology, Vol. 14, Issue 5
The Impact of Systems Biology on Bioprocessing
journal, December 2017
- Campbell, Kate; Xia, Jianye; Nielsen, Jens
- Trends in Biotechnology, Vol. 35, Issue 12
Stochastic fluctuations in metabolic pathways
journal, May 2007
- Levine, E.; Hwa, T.
- Proceedings of the National Academy of Sciences, Vol. 104, Issue 22
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
Translating biochemical network models between different kinetic formats
journal, March 2009
- Hadlich, Frieder; Noack, Stephan; Wiechert, Wolfgang
- Metabolic Engineering, Vol. 11, Issue 2
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
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
Comparison and applications of label-free absolute proteome quantification methods on Escherichia coli
journal, September 2012
- Arike, L.; Valgepea, K.; Peil, L.
- Journal of Proteomics, Vol. 75, Issue 17
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
Analytics for Metabolic Engineering
journal, September 2015
- Petzold, Christopher J.; Chan, Leanne Jade G.; Nhan, Melissa
- Frontiers in Bioengineering and Biotechnology, Vol. 3
Bespoke design of whole-cell microbial machines
journal, November 2016
- Vickers, Claudia
- Microbial Biotechnology, Vol. 10, Issue 1
Context-Specific Metabolic Networks Are Consistent with Experiments
journal, May 2008
- Becker, Scott A.; Palsson, Bernhard O.
- PLoS Computational Biology, Vol. 4, Issue 5
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
13C metabolic flux analysis at a genome-scale
journal, November 2015
- Gopalakrishnan, Saratram; Maranas, Costas D.
- Metabolic Engineering, Vol. 32
Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli
journal, June 2013
- Chang, R. L.; Andrews, K.; Kim, D.
- Science, Vol. 340, Issue 6137, p. 1220-1223
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
Methods for the integration of multi-omics data: mathematical aspects
journal, January 2016
- Bersanelli, Matteo; Mosca, Ettore; Remondini, Daniel
- BMC Bioinformatics, Vol. 17, Issue S2
Thermodynamics-Based Metabolic Flux Analysis
journal, March 2007
- Henry, Christopher S.; Broadbelt, Linda J.; Hatzimanikatis, Vassily
- Biophysical Journal, Vol. 92, Issue 5
What is flux balance analysis?
journal, March 2010
- Orth, Jeffrey D.; Thiele, Ines; Palsson, Bernhard Ø
- Nature Biotechnology, Vol. 28, Issue 3
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
Integrated Analysis of Transcriptomic and Proteomic Data
journal, February 2013
- Haider, Saad; Pal, Ranadip
- Current Genomics, Vol. 14, Issue 2
Engineering Cellular Metabolism
journal, March 2016
- Nielsen, Jens; Keasling, Jay D.
- Cell, Vol. 164, Issue 6
Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples
journal, August 2012
- Wagner, Günter P.; Kin, Koryu; Lynch, Vincent J.
- Theory in Biosciences, Vol. 131, Issue 4
Current state and challenges for dynamic metabolic modeling
journal, October 2016
- Vasilakou, Eleni; Machado, Daniel; Theorell, Axel
- Current Opinion in Microbiology, Vol. 33
Quantitative -omic data empowers bottom-up systems biology
journal, June 2018
- Yurkovich, James T.; Palsson, Bernhard O.
- Current Opinion in Biotechnology, Vol. 51