[1] N. Hillson et al., “Building a global alliance of biofoundries,” Nature
Commun., vol. 10, no. 1, pp. 1–4, 2019.
3758
IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 20, NO. 6, NOVEMBER/DECEMBER 2023
[2] J. D. Orth, I. Thiele, and B. O. Palsson, “What is flux balance analysis?,”
Nature Biotechnol., vol. 28, no. 3, pp. 245–248, 2010.
[3] W. B. Copeland et al., “Computational tools for metabolic engineering,”
Metabolic Eng., vol. 14, no. 3, pp. 270–280, 2012.
[4] A. P. Burgard, P. Pharkya, and C. D. Maranas, “Optknock: A bilevel
programming framework for identifying gene knockout strategies for
microbial strain optimization,” Biotechnol. Bioeng., vol. 84, no. 6,
pp. 647–657, 2003.
[5] P. Pharkya, A. P. Burgard, and C.D. Maranas, “OptStrain: A computational
framework for redesign of microbial production systems,” Genome Res.,
vol. 14, no. 11, pp. 2367–2376, 2004.
[6] P. Pharkya and C. D. Maranas, “An optimization framework for identifying
reaction activation/inhibition or elimination candidates for overproduction
in microbial systems,” Metabolic Eng., vol. 8, no. 1, pp. 1–13, 2006.
[7] P. R. Patil, I. Rocha, J. Förster, and J. Nielsen, “Evolutionary programming
as a platform for in silico metabolic engineering,” BMC Bioinf., vol. 6,
no. 1, 2005, Art. no. 308.
[8] S. Ranganathan, P. F. Suthers, and C. D. Maranas, “OptForce: An optimization procedure for identifying all genetic manipulations leading
to targeted overproductions,” PLoS Comput Biol, vol. 6, no. 4, 2010,
Art. no. e1000744.
[9] I. Rocha et al., “OptFlux: An open-source software platform for in silico
metabolic engineering,” BMC Syst. Biol., vol. 4, no. 1, pp. 1–12, 2010.
[10] Y. Toya and H. Shimizu, “Flux analysis and metabolomics for systematic
metabolic engineering of microorganisms,” Biotechnol. Adv., vol. 31, no. 6,
pp. 818–826, 2013.
[11] D. S. Lun et al., “Large-scale identification of genetic design strategies
using local search,” Mol. Syst. Biol., vol. 5, no. 1, 2009, Art. no. 296.
[12] G. Rockwell, N. J. Guido, and G. M. Church, “Redirector: Designing cell
factories by reconstructing the metabolic objective,” PLoS Comput Biol,
vol. 9, no. 1, 2013, Art. no. e1002882.
[13] L. Yang, W. R. Cluett, and R. Mahadevan, “EMILiO: A fast algorithm for
genome-scale strain design,” Metabolic Eng., vol. 13, no. 3, pp. 272–281,
2011.
[14] D. Egen and D. S. Lun, “Truncated branch and bound achieves efficient constraint-based genetic design,” Bioinformatics, vol. 28, no. 12,
pp. 1619–1623, 2012.
[15] N. E. Lewis et al., “Omic data from evolved E. coli are consistent with
computed optimal growth from genome-scale models,” Mol. Syst. Biol.,
vol. 6, no. 1, 2010, Art. no. 390.
[16] D. Gu, C. Zhang, S. Zhou, L. Wei, and Q. Hua, “IdealKnock: A framework
for efficiently identifying knockout strategies leading to targeted overproduction,” Comput. Biol. Chem., vol. 61, pp. 229–237, 2016.
[17] S. Ohno, H. Shimizu, and C. Furusawa, “FastPros: Screening of reaction
knockout strategies for metabolic engineering,” Bioinformatics, vol. 30,
no. 7, pp. 981–987, 2014.
[18] T. Tamura, “Grid-based computational methods for the design of
constraint-based parsimonious chemical reaction networks to simulate
metabolite production: GridProd,” BMC Bioinf., vol. 19, no. 1, 2018,
Art. no. 325.
[19] P. Schneider, P. S. Bekiaris, A. von Kamp, and S. Klamt, “StrainDesign:
A comprehensive Python package for computational design of metabolic
networks,” Bioinformatics, vol. 38, no. 21, pp. 4981–4983, 2022.
[20] D. Machado, M. J. Herrgård, and I. Rocha, “Stoichiometric representation
of gene–protein–reaction associations leverages constraint-based analysis
from reaction to gene-level phenotype prediction,” PLoS Comput. Biol.,
vol. 12, no. 10, 2016, pp. e1005140.
[21] Z. Razaghi-Moghadam and Z. Nikoloski, “GeneReg: A constraint-based
approach for design of feasible metabolic engineering strategies at the
gene level,” Bioinformatics, vol. 37, no. 12, pp. 1717–1723, 2020.
[22] T. Tamura, “Trimming gene deletion strategies for growth-coupled production in constraint-based metabolic networks: TrimGdel,” IEEE/ACM
Trans. Comput. Biol. Bioinf., vol. 20, no. 2, pp. 1540–1549, 2023.
[23] C. J. Norsigian, “BiGG models 2020: Multi-strain genome-scale models
and expansion across the phylogenetic tree,” Nucleic Acids Res., vol. 48,
no. D1, pp. D402–D406, 2020.
[24] T. Tamura, A. Muto-Fujita, Y. Tohsato, and T. Kosaka, “Gene deletion
algorithms for minimum reaction network design by mixed-integer linear programming for metabolite production in constraint-based models:
GDel_minRN,” J. Comput. Biol., vol. 30, no. 5, pp. 553–568, 2023.
[25] D. Gusfield, Integer Linear Programming in Computational and Systems
Biology: An Entry-Level Text and Course. Cambridge, U.K.: Cambridge
Univ. Press, 2019.
[26] Z. A. King, A. Dräger, A. Ebrahim, N. Sonnenschein, N. E. Lewis, and
B. O. Palsson, “Escher: A web application for building, sharing, and
embedding data-rich visualizations of biological pathways,” PLoS Comput
Biol, vol. 11, no. 8, 2015, Art. no. e1004321.
[27] M. Kanehisa and S. Goto, “KEGG: Kyoto Encyclopedia of genes and
genomes,” Nucleic Acids Res., vol. 28, no. 1, pp. 27–30, 2000.
[28] L. Heirendt et al., “Creation and analysis of biochemical constraint-based
models using the COBRA toolbox v. 3.0,” Nature Protoc., vol. 14, no. 3,
pp. 639–702, 2019.
Takeyuki Tamura (Member, IEEE) received the BE,
ME, and PhD degrees in informatics from Kyoto
University, Japan, in 2001, 2003, and 2006, respectively. He joined Bioinformatics Center, Institute for
Chemical Research, Kyoto University as a postdoctoral fellow in April 2006. He worked as an assistant
professor from December 2007 to September 2017
and started working as an associate professor from
October 2017. His research interests include mathematical metabolic engineering based on algorithm
theory.
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