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  2021 (12)
Engineering biology approaches for food and nutrient production by cyanobacteria. Liu, D.; Liberton, M.; Hendry, J. I; Aminian-Dehkordi, J.; Maranas, C. D; and Pakrasi, H. B Current Opinion in Biotechnology, 67: 1–6. 2021.
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Gene construct encoding mutant thioesterase, mutant thioesterase encoded thereby, transformed host cell containing the gene construct, and method of using them to produce medium-chain fatty acids. Pfleger, B. F; Hernandez-Lozada, N. J.; Maranas, C.; and Grisewood, M. July~6 2021. US Patent 11,053,480
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Building kinetic models for metabolic engineering. Foster, C. J; Wang, L.; Dinh, H. V; Suthers, P. F; and Maranas, C. D Current Opinion in Biotechnology, 67: 35–41. 2021.
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Modeling Growth Kinetics, Interspecies Cell Fusion, and Metabolism of a Clostridium acetobutylicum/Clostridium ljungdahlii Syntrophic Coculture. Foster, C.; Charubin, K.; Papoutsakis, E. T; and Maranas, C. D Msystems, 6(1): e01325–20. 2021.
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A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers. Metabolites 2021, 11, 168. Hendry, J.; Dinh, H.; Sarkar, D; Wang, L; Bandyopadhyay, A; Pakrasi, H.; and Maranas, C. 2021.
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dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design. Wang, L.; Upadhyay, V.; and Maranas, C. D bioRxiv. 2021.
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Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor. Chen, C.; Boorla, V. S.; Banerjee, D.; Chowdhury, R.; Cavener, V. S; Nissly, R. H; Gontu, A.; Boyle, N. R; Vandegrift, K.; Nair, M. S.; and others bioRxiv. 2021.
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Computationally Prospecting Potential Pathways from Lignin Monomers and Dimers toward Aromatic Compounds. Wang, L.; and Maranas, C. D ACS Synthetic Biology, 10(5): 1064–1076. 2021.
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Dissecting the Metabolic Reprogramming of Maize Root under Nitrogen Limiting Stress Condition. Chowdhury, N. B.; Schroeder, W. L; Sarkar, D.; Amiour, N.; Quillere, I.; Hirel, B.; Maranas, C. D; and Saha, R. bioRxiv. 2021.
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Elucidation of trophic interactions in an unusual single-cell nitrogen-fixing symbiosis using metabolic modeling. Sarkar, D.; Landa, M.; Bandyopadhyay, A.; Pakrasi, H. B; Zehr, J. P; and Maranas, C. D PLoS computational biology, 17(5): e1008983. 2021.
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Recombination and lineage-specific mutations linked to the emergence of SARS-CoV-2. Patiño-Galindo, J. Á.; Filip, I.; Chowdhury, R.; Maranas, C. D; Sorger, P. K; AlQuraishi, M.; and Rabadan, R. BioRxiv. 2021.
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EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on Ensemble Learning. Banerjee, D.; Jindra, M. A; Linot, A. J; Pfleger, B. F; and Maranas, C. D bioRxiv. 2021.
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  2020 (21)
Metabolic flux analysis reaching genome wide coverage: lessons learned and future perspectives. Hendry, J. I; Dinh, H. V; Foster, C.; Gopalakrishnan, S.; Wang, L.; and Maranas, C. D Current Opinion in Chemical Engineering, 30: 17–25. 2020.
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From directed evolution to computational enzyme engineering—A review. Chowdhury, R.; and Maranas, C. D AIChE Journal, 66(3): e16847. 2020.
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K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data. Gopalakrishnan, S.; Dash, S.; and Maranas, C. Metabolic engineering, 61: 197–205. 2020.
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Artificial water channels enable fast and selective water permeation through water-wire networks (vol 135, pg 2152, 2013). Song, W.; Joshi, H.; Chowdhury, R.; Najem, J. S; Shen, Y.; Lang, C.; Henderson, C. B; Tu, Y.; Farell, M.; Pitz, M. E; and others NATURE NANOTECHNOLOGY, 15(2): 162–162. 2020.
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Gene construct encoding mutant thioesterase, mutant thioesterase encoded thereby, transformed host cell containing the gene construct, and method of using them to produce medium-chain fatty acids. Pfleger, B. F; Hernandez-Lozada, N. J.; Maranas, C.; and Grisewood, M. June~23 2020. US Patent 10,689,631
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SNPeffect: identifying functional roles of SNPs using metabolic networks. Sarkar, D.; and Maranas, C. D The Plant Journal, 103(2): 512–531. 2020.
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In vivo thermodynamic analysis of glycolysis in Clostridium thermocellum and Thermoanaerobacterium saccharolyticum using 13C and 2H tracers. Jacobson, T. B; Korosh, T. K; Stevenson, D. M; Foster, C.; Maranas, C.; Olson, D. G; Lynd, L. R; and Amador-Noguez, D. Msystems, 5(2): e00736–19. 2020.
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Challenges of cultivated meat production and applications of genome-scale metabolic modeling. Suthers, P. F; and Maranas, C. D AIChE Journal, 66(6): e16235. 2020.
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Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Chowdhuri, I.; Pal, S. C.; and Chakrabortty, R. Advances in Space Research, 65(5): 1466–1489. 2020.
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Biophysical characterization of the SARS-CoV-2 spike protein 1 binding with the ACE2 receptor and implications for infectivity. Chowdhury, R.; and Maranas, C. D bioRxiv. 2020.
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Lignocellulose-to-Ethanol via Consolidated Bioprocessing with Cotreatment. Lynd, L.; Holwerda, E.; Olson, D. G; Hon, S.; Herring, C.; Kubis, M.; Ghosh, S.; Moynihan, G.; Foster, C.; Maranas, C.; and others In SBFC2020 Symposium on Biomaterials, Fuels and Chemicals, 2020. SIMB
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Development of a genome-scale metabolic model of Clostridium thermocellum and its applications for Integration of multi-omics datasets and computational strain design. Garcia, S.; Thompson, R A.; Giannone, R. J; Dash, S.; Maranas, C. D; and Trinh, C. T Frontiers in bioengineering and biotechnology, 8: 772. 2020.
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Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production. Suthers, P. F; Dinh, H. V; Fatma, Z.; Shen, Y.; Chan, S. H. J.; Rabinowitz, J. D; Zhao, H.; and Maranas, C. D Metabolic engineering communications, 11: e00148. 2020.
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De novo design of high-affinity antibody variable regions (scFv) against the SARS-CoV-2 spike protein. Boorla, V. S.; Chowdhury, R.; and Maranas, C. D BioRxiv. 2020.
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Engineering sensitivity and specificity of AraC-based biosensors responsive to triacetic acid lactone and orsellinic acid. Wang, Z.; Doshi, A.; Chowdhury, R.; Wang, Y.; Maranas, C. D; and Cirino, P. C Protein Engineering, Design and Selection, 33. 2020.
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Computational biophysical characterization of the SARS-CoV-2 spike protein binding with the ACE2 receptor and implications for infectivity. Chowdhury, R.; Boorla, V. S.; and Maranas, C. D Computational and structural biotechnology journal, 18: 2573–2582. 2020.
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IPRO+/-: Computational protein design tool allowing for insertions and deletions. Chowdhury, R.; Grisewood, M. J; Boorla, V. S.; Yan, Q.; Pfleger, B. F; and Maranas, C. D Structure, 28(12): 1344–1357. 2020.
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The importance and future of biochemical engineering. Whitehead, T. A; Banta, S.; Bentley, W. E; Betenbaugh, M. J; Chan, C.; Clark, D. S; Hoesli, C. A; Jewett, M. C; Junker, B.; Koffas, M.; and others Biotechnology and bioengineering, 117(8): 2305–2318. 2020.
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Bacterial colonization reprograms the neonatal gut metabolome. Bittinger, K.; Zhao, C.; Li, Y.; Ford, E.; Friedman, E. S; Ni, J.; Kulkarni, C. V; Cai, J.; Tian, Y.; Liu, Q.; and others Nature microbiology, 5(6): 838–847. 2020.
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Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Suthers, P. F; Foster, C. J; Sarkar, D.; Wang, L.; and Maranas, C. D Metabolic engineering. 2020.
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Assessing the Impact of Allosteric Enzyme Regulations Limiting Ethanol Titer in Clostridium Thermocellum Using a Core Kinetic Model. Foster, C.; Boorla, V. S.; Jacobson, T. B; Chowdhury, R.; Gopalakrishnan, S.; Dash, S.; Olson, D.; Amador-Noguez, D.; Lynd, L. R; and Maranas, C. In 2020 Virtual AIChE Annual Meeting, 2020. AIChE
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  2019 (26)
Reply to Zhou and Li: Plasticity of the genomic haplotype of Synechococcus elongatus leads to rapid strain adaptation under laboratory conditions. Ungerer, J.; Wendt, K. E; Hendry, J. I; Maranas, C. D; and Pakrasi, H. B Proceedings of the National Academy of Sciences, 116(10): 3946–3947. 2019.
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Thermodynamic analysis of the pathway for ethanol production from cellobiose in Clostridium thermocellum. Dash, S.; Olson, D. G; Chan, S. H. J.; Amador-Noguez, D.; Lynd, L. R; and Maranas, C. D Metabolic engineering, 55: 161–169. 2019.
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From 13 c Labeling Data to a Core Metabolism Kinetic Model: A Kinetic Model Parameterization Pipeline. Foster, C.; Gopalakrishnan, S.; Srinivasan, S.; Dash, S.; Antoniewicz, M.; and Maranas, C. D In 2019 AIChE Annual Meeting, 2019. AIChE
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From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline. Foster, C. J; Gopalakrishnan, S.; Antoniewicz, M. R; and Maranas, C. D PLoS computational biology, 15(9): e1007319. 2019.
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Looking Beyond GWAS: Identifying Functional Roles of SNPs Using Metabolic Networks in Arabidopsis and Populus. Sarkar, D.; and Maranas, C. In 2019 AIChE Annual Meeting, 2019. AIChE
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Directed evolution reveals the functional sequence space of an adenylation domain specificity code. Throckmorton, K.; Vinnik, V.; Chowdhury, R.; Cook, T.; Chevrette, M. G; Maranas, C.; Pfleger, B.; and Thomas, M. G. ACS chemical biology, 14(9): 2044–2054. 2019.
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Ipro+/-Computational Protein Design Tool for Predicting Indels Along with Substitutions for Redesign of Channel Proteins and Enzymes Alike. Chowdhury, R.; and Maranas, C. In 2019 AIChE Annual Meeting, 2019. AIChE
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Pareto optimality explanation of the glycolytic alternatives in nature. Ng, C. Y.; Wang, L.; Chowdhury, A.; and Maranas, C. D Scientific reports, 9(1): 1–15. 2019.
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Angstrom-scale separations by design using precision biomimetic membrane. Maranas, C. D; Kumar, M.; Chowdhury, R.; and Ren, T. October~31 2019. US Patent App. 16/397,416
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EcoFABs: advancing microbiome science through standardized fabricated ecosystems. Zengler, K.; Hofmockel, K.; Baliga, N. S; Behie, S. W; Bernstein, H. C; Brown, J. B; Dinneny, J. R; Floge, S. A; Forry, S. P; Hess, M.; and others Nature methods, 16(7): 567–571. 2019.
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Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0. Heirendt, L.; Arreckx, S.; Pfau, T.; Mendoza, S. N; Richelle, A.; Heinken, A.; Haraldsdóttir, H. S; Wachowiak, J.; Keating, S. M; Vlasov, V.; and others Nature protocols, 14(3): 639–702. 2019.
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Gene construct encoding mutant thioesterase, mutant thioesterase encoded thereby, transformed host cell containing the gene construct, and method of using them to produce medium-chain fatty acids. Pfleger, B. F; Hernandez-Lozada, N. J.; Maranas, C.; and Grisewood, M. September~24 2019. US Patent 10,421,951
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Genome-scale fluxome of Synechococcus elongatus UTEX 2973 using transient 13C-labeling data. Hendry, J. I; Gopalakrishnan, S.; Ungerer, J.; Pakrasi, H. B; Tang, Y. J; and Maranas, C. D Plant physiology, 179(2): 761–769. 2019.
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Predicting the longitudinally and radially varying gut microbiota composition using multi-scale microbial metabolic modeling. Chan, S. H.; Friedman, E. S; Wu, G. D; and Maranas, C. D Processes, 7(7): 394. 2019.
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Computational optimization of a thioesterase for selective chain-length product distribution. Jindra, M.; Chowdhury, R.; Pfleger, B.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 257, 2019. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Engineering microbial chemical factories using metabolic models. Sarkar, D.; and Maranas, C. D BMC Chemical Engineering, 1(1): 1–11. 2019.
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A diurnal flux balance model of Synechocystis sp. PCC 6803 metabolism. Sarkar, D.; Mueller, T. J; Liu, D.; Pakrasi, H. B; and Maranas, C. D PLoS computational biology, 15(1): e1006692. 2019.
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Identifying functional roles of SNPs using metabolic networks for improved plant breeding. Maranas, C.; and Sarkar, D. . 2019.
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7 log virus removal in a simple functionalized sand filter. Samineni, L.; Xiong, B.; Chowdhury, R.; Pei, A.; Kuehster, L.; Wang, H.; Dickey, R.; Soto, P. E.; Massenburg, L.; Nguyen, T. H; and others Environmental science & technology, 53(21): 12706–12714. 2019.
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PoreDesigner: A computational tool for the design of membrane pores for separations. Maranas, C.; Chowdhury, R.; and Kumar, M. . 2019.
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A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data. Dinh, H. V; Suthers, P. F; Chan, S. H. J.; Shen, Y.; Xiao, T.; Deewan, A.; Jagtap, S. S; Zhao, H.; Rao, C. V; Rabinowitz, J. D; and others Metabolic engineering communications, 9: e00101. 2019.
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Metabolism of S-lignin by Pseudomonas putida KT2440. Johnson, C. W; Notonier, S.; Dumalo, L.; Abraham, P. E; Hatmaker, E. A; Werner, A.; Amore, A.; Wang, L.; Giannone, R. J; Guss, A. M; and others In SIMB Annual Meeting and Exhibition 2019, 2019. SIMB
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Computational Pathway Design for Funneling Lignin Intermediates to Aromatic Products. Wang, L.; and Maranas, C. In 2019 AIChE Annual Meeting, 2019. AIChE
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Enhanced virus removal in a practical sand filter. Samineni, L.; Xiong, B.; Chowdhury, R.; Nguyen, T.; Maranas, C.; Velegol, D.; Kumar, M.; and Velegol, S. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 258, 2019. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Computational Protein Design Using Optimization Programs and Force-Field Calculations. Chowdhury, R.; Maranas, C.; and Kumar, M. In 2019 AIChE Annual Meeting, 2019. AIChE
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Assessing the Metabolic Capabilities of the Yeast Issatchenkia Orientalis SD108 and Its Application to Biochemical Production. Suthers, P. F; Fatma, Z.; Shen, Y.; Chan, S. H. J.; Dinh, H.; Rabinowitz, J. D; Zhao, H.; and Maranas, C. In 2019 AIChE Annual Meeting, 2019. AIChE
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  2018 (20)
Ensemble cell-wide kinetic modeling of anaerobic organisms to support fuels and chemicals production. Maranas, C. D Technical Report Pennsylvania State Univ., University Park, PA (United States), 2018.
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Engineering of E. coli inherent fatty acid biosynthesis capacity to increase octanoic acid production. Tan, Z.; Yoon, J. M.; Chowdhury, A.; Burdick, K.; Jarboe, L. R; Maranas, C. D; and Shanks, J. V Biotechnology for biofuels, 11(1): 1–15. 2018.
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PoreDesigner for tuning solute selectivity in a robust and highly permeable outer membrane pore. Chowdhury, R.; Ren, T.; Shankla, M.; Decker, K.; Grisewood, M.; Prabhakar, J.; Baker, C.; Golbeck, J. H; Aksimentiev, A.; Kumar, M.; and others Nature communications, 9(1): 1–10. 2018.
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Computationally exploring and alleviating the kinetic bottlenecks of anaerobic methane oxidation. Grisewood, M. J; Ferry, J. G; and Maranas, C. D Frontiers in Environmental Science, 6: 84. 2018.
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Biomimetic Membrane Design Principles for Angstrom Scale Separation. Ren, T.; Chowdhury, R.; Butler, P.; Maranas, C.; and Kumar, M. Biophysical Journal, 114(3): 361a. 2018.
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Elucidation of photoautotrophic carbon flux topology in Synechocystis PCC 6803 using genome-scale carbon mapping models. Gopalakrishnan, S.; Pakrasi, H. B; and Maranas, C. D Metabolic engineering, 47: 190–199. 2018.
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The biochemistry and physiology of respiratory-driven reversed methanogenesis. Nazem-Bokaee, H.; Yan, Z.; Maranas, C. D; and Ferry, J. G In Methane Biocatalysis: Paving the Way to Sustainability, pages 183–197. Springer, 2018.
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Pathway design using de novo steps through uncharted biochemical spaces. Kumar, A.; Wang, L.; Ng, C. Y.; and Maranas, C. D Nature communications, 9(1): 1–15. 2018.
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MinGenome: an in silico top-down approach for the synthesis of minimized genomes. Wang, L.; and Maranas, C. D ACS synthetic biology, 7(2): 462–473. 2018.
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Identification of growth-coupled production strains considering protein costs and kinetic variability. Dinh, H. V; King, Z. A; Palsson, B. O; and Feist, A. M Metabolic engineering communications, 7: e00080. 2018.
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Optmaven-2.0: A Fast Protocol for de novo Design of Antibody Variable Region Against Aspecific Antigen Epitope. Chowdhury, R.; Allan, M. F; and Maranas, C. In 2018 AIChE Annual Meeting, 2018. AIChE
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Metabolic modeling of microbial communities. Chan, S. H. J.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 255, 2018. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Modeling plant metabolism: advancements and future capabilities. Simons-Senftle, M. N; Sarkar, D.; and Maranas, C. D In Engineering Nitrogen Utilization in Crop Plants, pages 57–76. Springer, 2018.
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Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints. Chan, S. H.; Wang, L.; Dash, S.; and Maranas, C. D Bioinformatics, 34(24): 4248–4255. 2018.
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A prospective study on the fermentation landscape of gaseous substrates to biorenewables using Methanosarcina acetivorans metabolic model. Nazem-Bokaee, H.; and Maranas, C. D Frontiers in microbiology, 9: 1855. 2018.
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CORRECTION: Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modelling Approach to Link Leaf Physiology to Kernel Yield. Cañas, R. A; Yesbergenova-Cuny, Z.; Simons, M.; Chardon, F.; Armengaud, P.; Quilleré, I.; Cukier, C.; Gibon, Y.; Limami, A. M; Nicolas, S. D; and others The Plant Cell,tpc–00273. 2018.
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Comparative genomics reveals the molecular determinants of rapid growth of the cyanobacterium Synechococcus elongatus UTEX 2973. Ungerer, J.; Wendt, K. E; Hendry, J. I; Maranas, C. D; and Pakrasi, H. B Proceedings of the National Academy of Sciences, 115(50): E11761–E11770. 2018.
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Exploring the combinatorial space of complete pathways to chemicals. Wang, L.; Ng, C. Y.; Dash, S.; and Maranas, C. D Biochemical Society Transactions, 46(3): 513–522. 2018.
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Predicting the Spatially Differential Gut Microbiota Composition Using Genome-Scale Metabolic Modeling. Chan, S. H. J.; Senftle, M.; and Maranas, C. In 2018 AIChE Annual Meeting, 2018. AIChE
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Developing Genome-Scale Whole-Plant Models for Poplar (Populus deltoides) and Switchgrass (Panicum virgatum). Suthers, P. F; Sarkar, D.; and Maranas, C. In 2018 AIChE Annual Meeting, 2018. AIChE
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  2017 (30)
Identifying the metabolic differences of a fast-growth phenotype in Synechococcus UTEX 2973. Mueller, T. J; Ungerer, J. L; Pakrasi, H. B; and Maranas, C. D Scientific reports, 7(1): 1–8. 2017.
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Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations. Dash, S.; Khodayari, A.; Zhou, J.; Holwerda, E. K; Olson, D. G; Lynd, L. R; and Maranas, C. D Biotechnology for biofuels, 10(1): 1–16. 2017.
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Identification of growth-coupled and kinetically robust production in strain designs using genome-scale models. Dinh, H. V. Ph.D. Thesis, University of California, San Diego, 2017.
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Multilevel engineering of the upstream aromatic module in Saccharomyces cerevisiae for high production of polymer and drug precursors. Suastegui, M.; Shao, Z.; Ng, C.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 253, 2017. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Nature inspired antibody design and optimization. Tessier, P.; Tiller, K.; Rabia, L.; Julian, M.; Li, L.; Ludwig, S.; Garde, S.; Chowdhury, R.; Maranas, C.; and Kumar, S. . 2017.
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Cyanobacterial genome-scale carbon mapping models for genome-scale 13C-metabolic flux elucidation. Gopalakrishnan, S.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 253, 2017. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Systematic Redesigning of E. coli water Channel Porin, Ompf, for Desired Pore Size Using Iterative Protein Redesign and Optimization (IPRO) Suite. Chowdhury, R.; Ren, T.; Decker, K.; Aksimentiev, A.; Kumar, M.; and Maranas, C. In 2017 AIChE Annual Meeting, 2017. AIChE
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A review of computational tools for design and reconstruction of metabolic pathways. Wang, L.; Dash, S.; Ng, C. Y.; and Maranas, C. D Synthetic and systems biotechnology, 2(4): 243–252. 2017.
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SteadyCom: predicting microbial abundances while ensuring community stability. Chan, S. H. J.; Simons, M. N; and Maranas, C. D PLoS computational biology, 13(5): e1005539. 2017.
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Optimization Driven Top-Down Synthesis of Genome Minimized Strains for Bioproduction. Wang, L.; and Maranas, C. D In 2017 AIChE Annual Meeting, 2017. AIChE
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Computational redesign of acyl-ACP thioesterase with improved selectivity towards medium chain fatty acids at high production levels. Maranas, C. D; Grisewood, M. J; Hernandez Lozada, N. J; and Pfleger, B. F . 2017.
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Clostridium butyricum maximizes growth while minimizing enzyme usage and ATP production: metabolic flux distribution of a strain cultured in glycerol. Serrano-Bermúdez, L. M.; Barrios, A. F. G.; Maranas, C. D; and Montoya, D. BMC systems biology, 11(1): 1–13. 2017.
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Method for designing overall stoichiometric conversions and intervening metabolic reactions. Chowdhury, A.; and Maranas, C. D April~27 2017. US Patent App. 15/146,602
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Constructing Predictive Kinetic Models of Metabolism with Transcriptional Regulation. Dash, S.; Gopalakrishnan, S.; Foster, C.; and Maranas, C. D In 2017 AIChE Annual Meeting, 2017. AIChE
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Metabolic Modeling of Microbial Communities. Maranas, C. D In 2017 AIChE Annual Meeting, 2017. AIChE
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Exploiting the genetic diversity of maize using a combined metabolomic, enzyme activity profiling, and metabolic modeling approach to link leaf physiology to kernel yield. Cañas, R. A; Yesbergenova-Cuny, Z.; Simons, M.; Chardon, F.; Armengaud, P.; Quilleré, I.; Cukier, C.; Gibon, Y.; Limami, A. M; Nicolas, S.; and others The Plant Cell, 29(5): 919–943. 2017.
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De novo design of antibody complementarity determining regions binding a FLAG tetra-peptide. Entzminger, K. C; Hyun, J.; Pantazes, R. J; Patterson-Orazem, A. C; Qerqez, A. N; Frye, Z. P; Hughes, R. A; Ellington, A. D; Lieberman, R. L; Maranas, C. D; and others Scientific reports, 7(1): 1–11. 2017.
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Uncovering and Correcting the Effect of Biomass Molecular Weight Discrepancies in FBA Calculations. Chan, S. H. J.; Cai, J.; Wang, L.; Simons-Senftle, M.; and Maranas, C. In 2017 AIChE Annual Meeting, 2017. AIChE
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Development of a Knowledgebase (MetRxn) of Metabolites, Reactions and Atom Mappings to Accelerate Discovery and Redesign. Maranas, C. D Technical Report Pennsylvania State Univ., University Park, PA (United States), 2017.
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Computational redesign of acyl-ACP thioesterase with improved selectivity toward medium-chain-length fatty acids. Grisewood, M. J; Hernández-Lozada, N. J; Thoden, J. B; Gifford, N. P; Mendez-Perez, D.; Schoenberger, H. A; Allan, M. F; Floy, M. E; Lai, R.; Holden, H. M; and others ACS catalysis, 7(6): 3837–3849. 2017.
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Metabolic engineering of bacteria for production of oleochemicals. Hernandez, N.; Grisewood, M.; Maranas, C.; and Pfleger, B. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 253, 2017. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Standardizing biomass reactions and ensuring complete mass balance in genome-scale metabolic models. Chan, S. H.; Cai, J.; Wang, L.; Simons-Senftle, M. N; and Maranas, C. D Bioinformatics, 33(22): 3603–3609. 2017.
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Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis. Abernathy, M. H; Yu, J.; Ma, F.; Liberton, M.; Ungerer, J.; Hollinshead, W. D; Gopalakrishnan, S.; He, L.; Maranas, C. D; Pakrasi, H. B; and others Biotechnology for biofuels, 10(1): 1–13. 2017.
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Elucidation of Carbon Flux Topology Representing Photoautotrophic Growth in Synechocystis PCC 6803 Using Genome-Scale Isotopic Instationary Metabolic Flux Analysis. Gopalakrishnan, S.; Pakrasi, H. B; and Maranas, C. D In 2017 AIChE Annual Meeting, 2017. AIChE
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Facile affinity maturation of antibody variable domains using natural diversity mutagenesis. Tiller, K. E; Chowdhury, R.; Li, T.; Ludwig, S. D; Sen, S.; Maranas, C. D; and Tessier, P. M Frontiers in immunology, 8: 986. 2017.
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Genome-scale mapping models and algorithms for stationary and instationary MFA-based metabolic flux elucidation. Gopalakrishnan, S.; and Maranas, C. D . 2017.
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Computational modeling of microbial communities. Chan, S. H.; Simons, M.; and Maranas, C. D Systems Biology,163–189. 2017.
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Computational de novo design of antibodies binding to a peptide with high affinity. Poosarla, V. G.; Li, T.; Goh, B. C.; Schulten, K.; Wood, T. K; and Maranas, C. D Biotechnology and bioengineering, 114(6): 1331–1342. 2017.
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Kinetic modeling of metabolism in clostridia. Dash, S.; Khodayari, A.; Stephanopoulos, G.; Lynd, L.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 253, 2017. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Multiscale Systems Engineering II-In Honor of Professor Christodoulos A. Floudas (Invited Talks). Pistikopoulos, E. N; Ierapetritou, M.; and Maranas, C. D In 2017 AIChE Annual Meeting, 2017. AIChE
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  2016 (15)
Computational redesign of acyl-ACP thioesterase for medium-chain fatty acid production. Grisewood, M.; Hernandez-Lozada, N.; Pfleger, B.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 251, 2016. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Metabolic modeling of clostridia: current developments and applications. Dash, S.; Ng, C. Y.; and Maranas, C. D FEMS microbiology letters, 363(4): fnw004. 2016.
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A microbial factory for diverse chemicals. Ng, C. Y.; Chowdhury, A.; and Maranas, C. D Nature biotechnology, 34(5): 513–515. 2016.
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Reversing methanogenesis to capture methane for liquid biofuel precursors. Soo, V. W.; McAnulty, M. J; Tripathi, A.; Zhu, F.; Zhang, L.; Hatzakis, E.; Smith, P. B; Agrawal, S.; Nazem-Bokaee, H.; Gopalakrishnan, S.; and others Microbial cell factories, 15(1): 1–14. 2016.
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de novo Design and in silico Optimization of Antibody-Like Binders Targeting Ebola Viral Antigen. Lihan, M.; Goh, B. C.; Li, T.; Maranas, C. D; and Shulten, K. Biophysical Journal, 110(3): 537a. 2016.
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Optimization methods in metabolic networks. Maranas, C. D; and Zomorrodi, A. R John Wiley & Sons, 2016.
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Kinetic modeling of metabolism in obligatory anaerobes. Dash, S; Khodayari, A; Holwerda, E.; Olson, P; Theisen, M; Liao, J.; Stephanopoulos, G; Lynd, L.; and Maranas, C In 11th Metabolic Engineering Conference 2016, pages 600–601, 2016. AIChE
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Identifying regulatory changes to facilitate nitrogen fixation in the nondiazotroph Synechocystis sp. PCC 6803. Mueller, T. J; Welsh, E. A; Pakrasi, H. B; and Maranas, C. D ACS synthetic biology, 5(3): 250–258. 2016.
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Assessing methanotrophy and carbon fixation for biofuel production by Methanosarcina acetivorans. Nazem-Bokaee, H.; Gopalakrishnan, S.; Ferry, J. G; Wood, T. K; and Maranas, C. D Microbial cell factories, 15(1): 1–13. 2016.
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Diurnal regulation of cellular processes in the cyanobacterium Synechocystis sp. strain PCC 6803: Insights from transcriptomic, fluxomic, and physiological analyses. Saha, R.; Liu, D.; Hoynes-O’Connor, A.; Liberton, M.; Yu, J.; Bhattacharyya-Pakrasi, M.; Balassy, A.; Zhang, F.; Moon, T. S.; Maranas, C. D; and others MBio, 7(3): e00464–16. 2016.
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Development and validation of computational methods for de novo design of antibody variable regions. Li, T.; Poosarla, V.; Wood, T.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 251, 2016. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains. Khodayari, A.; and Maranas, C. D Nature communications, 7(1): 1–12. 2016.
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A hierarchy of modeling frameworks for cyanobacterial metabolism: From gsms to ME models. Mueller, T; Simons, M; Maranas, C.; Gopalakrishnan, S; and Pakrasi, H. In Metabolic Engineering 11, pages 549–550, 2016.
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Farnesoid X receptor signaling shapes the gut microbiota and controls hepatic lipid metabolism. Zhang, L.; Xie, C.; Nichols, R. G; Chan, S. H.; Jiang, C.; Hao, R.; Smith, P. B; Cai, J.; Simons, M. N; Hatzakis, E.; and others MSystems, 1(5): e00070–16. 2016.
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METABOLIC ENGINEERING 11. Mueller, T; Simons, M; Maranas, C.; Gopalakrishnan, S; and Pakrasi, H. METABOLIC ENGINEERING, 11: 11. 2016.
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  2015 (20)
Development of regulated metabolic models for anaerobic organisms. Dash, S.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 249, 2015. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Achieving metabolic flux analysis for S. cerevisiae at a genome-scale: challenges, requirements, and considerations. Gopalakrishnan, S.; and Maranas, C. D Metabolites, 5(3): 521–535. 2015.
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Succinate overproduction: a case study of computational strain design using a comprehensive Escherichia coli kinetic model. Khodayari, A.; Chowdhury, A.; and Maranas, C. D Frontiers in bioengineering and biotechnology, 2: 76. 2015.
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Methane-to-acetate pathway for producing liquid biofuels and biorenewables. Ferry, J. G.; Maranas, C. D; and Wood, T. K. May~28 2015. US Patent App. 14/550,547
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Advances in de novo strain design using integrated systems and synthetic biology tools. Ng, C. Y.; Khodayari, A.; Chowdhury, A.; and Maranas, C. D Current opinion in chemical biology, 28: 105–114. 2015.
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Transferring nitrogen fixing capabilities to an oxygenic photosynthetic organism. Balassy, A.; Bhattacharyya, M.; Hoynes-O'Connor, A.; Immethun, C.; Liberton, M.; Liu, D.; Mueller, T.; Xiao, Y.; Yu, J.; Moon, T. S.; and others In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 249, 2015. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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13C metabolic flux analysis at a genome-scale. Gopalakrishnan, S.; and Maranas, C. D Metabolic engineering, 32: 12–22. 2015.
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The iterative protein redesign and optimization (IPRO) suite of programs. Pantazes, R. J; Grisewood, M. J; Li, T.; Gifford, N. P; and Maranas, C. D Journal of computational chemistry, 36(4): 251–263. 2015.
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160. Improving computational strain design strategies by incorporation of kinetic information. Khodayari, A.; Chowdhury, A.; and Maranas, C. D In Genomic Science Contractors–Grantees Meeting XIII, pages 228, 2015.
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Rational design of a synthetic Entner–Doudoroff pathway for improved and controllable NADPH regeneration. Ng, C. Y.; Farasat, I.; Maranas, C. D; and Salis, H. M Metabolic engineering, 29: 86–96. 2015.
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Cyanobacterial alkanes modulate photosynthetic cyclic electron flow to assist growth under cold stress. Berla, B. M; Saha, R.; Maranas, C. D; and Pakrasi, H. B Scientific reports, 5(1): 1–12. 2015.
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Bilevel optimization techniques in computational strain design. Chowdhury, A.; Zomorrodi, A. R; and Maranas, C. D Computers & Chemical Engineering, 72: 363–372. 2015.
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Do genome-scale models need exact solvers or clearer standards?. Ebrahim, A.; Almaas, E.; Bauer, E.; Bordbar, A.; Burgard, A. P; Chang, R. L; Dräger, A.; Famili, I.; Feist, A. M; Fleming, R. M.; and others Molecular systems biology, 11(10): 831. 2015.
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Using gene essentiality and synthetic lethality information to correct yeast and CHO cell genome-scale models. Chowdhury, R.; Chowdhury, A.; and Maranas, C. D Metabolites, 5(4): 536–570. 2015.
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Editorial overview: systems biology: advances diseases understanding and metabolic engineering. Fendt, S.; and Maranas, C. D Current opinion in biotechnology, 34: v–vi. 2015.
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Designing overall stoichiometric conversions and intervening metabolic reactions. Chowdhury, A.; and Maranas, C. D Scientific reports, 5(1): 1–20. 2015.
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Methane oxidation by anaerobic archaea for conversion to liquid fuels. Mueller, T. J; Grisewood, M. J; Nazem-Bokaee, H.; Gopalakrishnan, S.; Ferry, J. G; Wood, T. K; and Maranas, C. D Journal of Industrial Microbiology and Biotechnology, 42(3): 391–401. 2015.
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Personalized Kinetic Models for Predictive Healthcare. Chowdhury, A.; and Maranas, C. D Cell systems, 1(4): 250–251. 2015.
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Development of a comprehensive Escherichia coli kinetic metabolic model consistent with multiple genetic and environmental perturbations. Khodayari, A.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 249, 2015. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Improving prediction fidelity of cellular metabolism with kinetic descriptions. Chowdhury, A.; Khodayari, A.; and Maranas, C. D Current opinion in biotechnology, 36: 57–64. 2015.
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  2014 (13)
Capturing the response of Clostridium acetobutylicum to chemical stressors using a regulated genome-scale metabolic model. Dash, S.; Mueller, T. J; Venkataramanan, K. P; Papoutsakis, E. T; and Maranas, C. D Biotechnology for biofuels, 7(1): 1–16. 2014.
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OptMAVEn–a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes. Li, T.; Pantazes, R. J; and Maranas, C. D PloS one, 9(8): e105954. 2014.
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Systems metabolic engineering design: fatty acid production as an emerging case study. Tee, T. W.; Chowdhury, A.; Maranas, C. D; and Shanks, J. V Biotechnology and Bioengineering, 111(5): 849–857. 2014.
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Nitrogen-use efficiency in maize (Zea mays L.): from ‘omics’ studies to metabolic modelling. Simons, M.; Saha, R.; Guillard, L.; Clément, G.; Armengaud, P.; Cañas, R.; Maranas, C. D; Lea, P. J; and Hirel, B. Journal of experimental botany, 65(19): 5657–5671. 2014.
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A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Khodayari, A.; Zomorrodi, A. R; Liao, J. C; and Maranas, C. D Metabolic engineering, 25: 50–62. 2014.
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CLCA: maximum common molecular substructure queries within the MetRxn database. Kumar, A.; and Maranas, C. D Journal of chemical information and modeling, 54(12): 3417–3438. 2014.
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Coarse-grained optimization-driven design and piecewise linear modeling of synthetic genetic circuits. Zomorrodi, A. R; and Maranas, C. D European Journal of Operational Research, 237(2): 665–676. 2014.
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Recent advances in the reconstruction of metabolic models and integration of omics data. Saha, R.; Chowdhury, A.; and Maranas, C. D Current opinion in biotechnology, 29: 39–45. 2014.
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d-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities. Zomorrodi, A. R; Islam, M. M.; and Maranas, C. D ACS synthetic biology, 3(4): 247–257. 2014.
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Coupled enzyme reactions performed in heterogeneous reaction media: experiments and modeling for glucose oxidase and horseradish peroxidase in a PEG/citrate aqueous two-phase system. Aumiller Jr, W. M; Davis, B. W; Hashemian, N.; Maranas, C.; Armaou, A.; and Keating, C. D The Journal of Physical Chemistry B, 118(9): 2506–2517. 2014.
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Assessing the Metabolic Impact of Nitrogen Availability Using a Compartmentalized Maize Leaf Genome-Scale Model1 [C][W][OPEN]. Simons, M.; Saha, R.; Amiour, N.; Kumar, A.; Guillard, L.; Clément, G.; Miquel, M.; Li, Z.; Mouille, G.; Lea, P. J; and others . 2014.
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k-OptForce: integrating kinetics with flux balance analysis for strain design. Chowdhury, A.; Zomorrodi, A. R; and Maranas, C. D PLoS computational biology, 10(2): e1003487. 2014.
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De novo antibody design by complementarity determining region modeling. Entzminger, K. C; Pantazes, R. J; Maranas, C. D; and Maynard, J. A In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 247, 2014. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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  2013 (8)
OptZyme: computational enzyme redesign using transition state analogues. Grisewood, M. J; Gifford, N. P; Pantazes, R. J; Li, Y.; Cirino, P. C; Janik, M. J; and Maranas, C. D PloS one, 8(10): e75358. 2013.
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Synthetic biology of cyanobacteria: unique challenges and opportunities. Berla, B. M; Saha, R.; Immethun, C. M; Maranas, C. D; Moon, T. S.; and Pakrasi, H. Frontiers in microbiology, 4: 246. 2013.
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MAPs: a database of modular antibody parts for predicting tertiary structures and designing affinity matured antibodies. Pantazes, R. J; and Maranas, C. D BMC bioinformatics, 14(1): 1–13. 2013.
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Transition state analogue-based computational enzyme design. Grisewood, M; Gifford, N; Janik, M.; and Maranas, C. In Food, Pharmaceutical and Bioengineering Division 2013-Core Programming Area at the 2013 AIChE Annual Meeting: Global Challenges for Engineering a Sustainable Future, 2013. AIChE
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Rapid construction of metabolic models for a family of Cyanobacteria using a multiple source annotation workflow. Mueller, T. J; Berla, B. M; Pakrasi, H. B; and Maranas, C. D BMC systems biology, 7(1): 1–12. 2013.
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Method for determining gene knockouts. Maranas, C. D; Burgard, A. R; and Pharkya, P. June~4 2013. US Patent 8,457,941
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Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks. Zomorrodi, A. R; Lafontaine Rivera, J. G; Liao, J. C; and Maranas, C. D Biotechnology journal, 8(9): 1090–1104. 2013.
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Rapid construction of metabolic models for a family of Cyanobacteria using a multiple source annotation workflow. Maranas, C. D; Pakrasi, H. B; Berla, B. M; and Mueller, T. J . 2013.
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  2012 (14)
Reconstruction and comparison of the metabolic potential of cyanobacteria Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. Saha, R.; Verseput, A. T; Berla, B. M; Mueller, T. J; Pakrasi, H. B; and Maranas, C. D PloS one, 7(10): e48285. 2012.
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OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. Zomorrodi, A. R; and Maranas, C. D PLoS computational biology, 8(2): e1002363. 2012.
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Development of Computational Tools for Metabolic Model Curation, Flux Elucidation and Strain Design. Maranas, C. D Technical Report The Pennsylvania State University, 2012.
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Method for redesign of microbial production systems. Maranas, C. D; Burgard, A. P; and Pharkya, P. January~31 2012. US Patent 8,108,152
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Mathematical optimization applications in metabolic networks. Zomorrodi, A. R; Suthers, P. F; Ranganathan, S.; and Maranas, C. D Metabolic engineering, 14(6): 672–686. 2012.
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MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases. Kumar, A.; Suthers, P. F; and Maranas, C. D BMC bioinformatics, 13(1): 1–13. 2012.
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PubMed ID 23036703. Ranganathan, S.; Teeb, T. W.; Chowdhuryc, A.; Zomorrodic, A. R; Yoon, J. M.; Fu, Y.; Shanksb, J. V; and Maranas, C. D Metabolic Engineering. 2012.
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Computational tools for metabolic engineering. Copeland, W. B; Bartley, B. A; Chandran, D.; Galdzicki, M.; Kim, K. H; Sleight, S. C; Maranas, C. D; and Sauro, H. M Metabolic engineering, 14(3): 270–280. 2012.
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Computational model of VDJ recombination for antibody design. Pantazes, R. J; and Maranas, C. D In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 243, 2012. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks. Brochado, A. R.; Andrejev, S.; Maranas, C. D; and Patil, K. R PLoS computational biology, 8(11): e1002758. 2012.
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Orchestrating hi-fi annotations. Suthers, P. F; and Maranas, C. D Nature chemical biology, 8(10): 810–811. 2012.
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Using OptForce to customize metabolic interventions for the overproduction of fatty acids C6 through C16 in Escherichia coli. Ranganathan, S.; Wang, T. W.; Welsh, J.; Yoon, J. M.; Shanks, J. V; San, K.; and Maranas, C. D In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 243, 2012. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Contrasting the metabolic capabilities of cyanobacterial species for assessing bio-production platform selection. Saha, R.; Berla, B. M; Mueller, T.; Elvitigala, T.; Page, L. E; Pakrasi, H. B; and Maranas, C. D In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 243, 2012. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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An integrated computational and experimental study for overproducing fatty acids in Escherichia coli. Ranganathan, S.; Tee, T. W.; Chowdhury, A.; Zomorrodi, A. R; Yoon, J. M.; Fu, Y.; Shanks, J. V; and Maranas, C. D Metabolic engineering, 14(6): 687–704. 2012.
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  2011 (12)
Recent advances in computational protein design. Pantazes, R. J; Grisewood, M. J; and Maranas, C. D Current opinion in structural biology, 21(4): 467–472. 2011.
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Modeling the effect of temozolomide treatment on orthotopic models of glioma. Vital-Lopez, F. G; Maranas, C. D; and Armaou, A. In Proceedings of the 2011 American Control Conference, pages 2969–2974, 2011. IEEE
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Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA. Xu, P.; Ranganathan, S.; Fowler, Z. L; Maranas, C. D; and Koffas, M. A. Metabolic engineering, 13(5): 578–587. 2011.
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Metabolic reconstruction of the archaeon methanogen Methanosarcina Acetivorans. Kumar, V. S.; Ferry, J. G; and Maranas, C. D BMC systems biology, 5(1): 1–10. 2011.
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MetRxn&: reaction and metabolite standardization and congruency across databases and genome-scale metabolic models. Kumar, A.; Suthers, P; and Maranas, C. In GTL Conference, pages 1–2, 2011.
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Rational design of recombinant phytochemical microbial producers. Koffas, M.; and Maranas, C. In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 241, 2011. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Zea mays i RS1563: a comprehensive genome-scale metabolic reconstruction of maize metabolism. Saha, R.; Suthers, P. F; and Maranas, C. D PloS one, 6(7): e21784. 2011.
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An integrated computational and experimental study to increase the intra-cellular malonyl-CoA: application to flavanone synthesis. Xu, P.; Ranganathan, S.; Maranas, C. D; and Koffas, M. In 2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC), pages 1–2, 2011. IEEE
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Method and system for modeling cellular metabolism. Maranas, C. D; and Burgard, A. P December~27 2011. US Patent 8,086,414
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Model simulations reveal VCAM-1 augment PAK activation rates to amplify p38 MAPK and VE-cadherin phosphorylation. Khanna, P.; Weidert, E.; Vital-Lopez, F.; Armaou, A.; Maranas, C. D; and Dong, C. Cellular and Molecular Bioengineering, 4(4): 656–669. 2011.
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Construction of an E. Coli genome-scale atom mapping model for MFA calculations. Ravikirthi, P.; Suthers, P. F; and Maranas, C. D Biotechnology and bioengineering, 108(6): 1372–1382. 2011.
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Metabolic reconstruction of the archaeon methanogen Methanosarcina Acetivorans. Maranas, C. D; Ferry, J. G; and Kumar, V. S. . 2011.
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  2010 (8)
Method for redesign of microbial production systems. Maranas, C. D; Burgard, A. P; and Pharkya, P. November~2 2010. US Patent 7,826,975
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Ensemble cell-wide kinetic modeling of anaerobic organisms to support fuels and chemicals production. Dash, S.; Khodayari, A.; Holwerda, E. K; Olson, D. G; Islam, M A.; Theisen, M.; Liao, J. C; Stephanopoulos, G.; Lynd, L. R; and Maranas, C. D Proceedings of the National Academy of Sciences of the United States of America, 107(29): 13087–92. 2010.
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Improved computational performance of MFA using elementary metabolite units and flux coupling. Suthers, P. F; Chang, Y. J; and Maranas, C. D Metabolic engineering, 12(2): 123–128. 2010.
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OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. Ranganathan, S.; Suthers, P. F; and Maranas, C. D PLoS computational biology, 6(4): e1000744. 2010.
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Identification of non-native routes and engineering interventions for the microbial production of long-chain alcohols. Ranganathan, S.; Suthers, P. F; and Maranas, C. D In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 239, 2010. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Improving the i MM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. Zomorrodi, A. R; and Maranas, C. D BMC systems biology, 4(1): 1–15. 2010.
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Microbial 1-butanol production: Identification of non-native production routes and in silico engineering interventions. Ranganathan, S.; and Maranas, C. D Biotechnology journal, 5(7): 716–725. 2010.
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OptCDR: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding. Pantazes, R.; and Maranas, C. D Protein Engineering, Design & Selection, 23(11): 849–858. 2010.
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  2009 (11)
Modeling Tools for Metabolic Engineering. Maranas, C. D The Metabolic Pathway Engineering Handbook: Fundamentals, 1. 2009.
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BIOT 200-Using computations to reconstruct, analyze, and redesign metabolism. Suthers, P. F; Zomorrodi, A.; Ranganathan, S.; and Maranas, C. D In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 238, 2009. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Genome-scale gene/reaction essentiality and synthetic lethality analysis. Suthers, P. F; Zomorrodi, A.; and Maranas, C. D Molecular systems biology, 5(1): 301. 2009.
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GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. Kumar, V. S.; and Maranas, C. D PLoS computational biology, 5(3): e1000308. 2009.
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Genome scale reconstruction of a Salmonella metabolic model: comparison of similarity and differences with a commensal Escherichia coli strain. AbuOun, M.; Suthers, P. F; Jones, G. I; Carter, B. R; Saunders, M. P; Maranas, C. D; Woodward, M. J; and Anjum, M. F Journal of Biological Chemistry, 284(43): 29480–29488. 2009.
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OptGraft: A computational procedure for transferring a binding site onto an existing protein scaffold. Fazelinia, H.; Cirino, P. C; and Maranas, C. D Protein Science, 18(1): 180–195. 2009.
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Computational design of Candida boidinii xylose reductase for altered cofactor specificity. Khoury, G. A; Fazelinia, H.; Chin, J. W; Pantazes, R. J; Cirino, P. C; and Maranas, C. D Protein Science, 18(10): 2125–2138. 2009.
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BIOT 94-Computational enzyme redesign for cofactor/substrate specificity. Fazelinia, H.; Khoury, G. A; Cirino, P. C; and Maranas, C. D In ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, volume 238, 2009. AMER CHEMICAL SOC 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
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Analysis of NADPH supply during xylitol production by engineered Escherichia coli. Chin, J. W; Khankal, R.; Monroe, C. A; Maranas, C. D; and Cirino, P. C Biotechnology and bioengineering, 102(1): 209–220. 2009.
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Using Systems Engineering to Reconstruct, Analyze and Redesign Metabolism. Suthers, P. F; Kumar, V. S.; Zomorrodi, A.; Ranganathan, S.; and Maranas, C. D In Computer Aided Chemical Engineering, volume 27, pages 113–115. Elsevier, 2009.
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