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  2023 (2)
IDEAL COMMUNITIES. Segets, D; Lin, W; Peukert, W; and Lerche, D CEP. 2023.
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Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale. Hu, M.; Dinh, H. V; Shen, Y.; Suthers, P. F; Foster, C. J; Call, C. M; Ye, X.; Pratas, J.; Fatma, Z.; Zhao, H.; and others Metabolic Engineering. 2023.
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  2022 (22)
Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions. Chowdhury, N. B.; Schroeder, W. L; Sarkar, D.; Amiour, N.; Quilleré, I.; Hirel, B.; Maranas, C. D; and Saha, R. Journal of Experimental Botany, 73(1): 275–291. 2022.
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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 Current Research in Biotechnology, 4: 1–9. 2022.
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Quantifying the propagation of parametric uncertainty on flux balance analysis. Dinh, H. V; Sarkar, D.; and Maranas, C. D Metabolic engineering, 69: 26–39. 2022.
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Functional Analysis of H+-Pumping Membrane-Bound Pyrophosphatase, ADP-Glucose Synthase, and Pyruvate Phosphate Dikinase as Pyrophosphate Sources in Clostridium thermocellum. Kuil, T.; Hon, S.; Yayo, J.; Foster, C.; Ravagnan, G.; Maranas, C. D; Lynd, L. R; Olson, D. G; and van Maris, A. J. Applied and environmental microbiology, 88(4): e01857–21. 2022.
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Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model. Foster, C.; Boorla, V. S.; Dash, S.; Gopalakrishnan, S.; Jacobson, T. B; Olson, D. G; Amador-Noguez, D.; Lynd, L. R; and Maranas, C. D Metabolic engineering, 69: 286–301. 2022.
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Measuring thermodynamic preferences to form non-native conformations in nucleic acids using ultraviolet melting. Rangadurai, A.; Shi, H.; Xu, Y.; Liu, B.; Abou Assi, H.; Boom, J. D; Zhou, H.; Kimsey, I. J; and Al-Hashimi, H. M Proceedings of the National Academy of Sciences, 119(24): e2112496119. 2022.
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Multiple spillovers from humans and onward transmission of SARS-CoV-2 in white-tailed deer. Kuchipudi, S. V; Surendran-Nair, M.; Ruden, R. M; Yon, M.; Nissly, R. H; Vandegrift, K. J; Nelli, R. K; Li, L.; Jayarao, B. M; Maranas, C. D; and others Proceedings of the National Academy of Sciences, 119(6): e2121644119. 2022.
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Toward low-cost biological and hybrid biological/catalytic conversion of cellulosic biomass to fuels. Lynd, L. R; Beckham, G. T; Guss, A. M; Jayakody, L. N; Karp, E. M; Maranas, C.; McCormick, R. L; Amador-Noguez, D.; Bomble, Y. J; Davison, B. H; and others Energy & Environmental Science, 15(3): 938–990. 2022.
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Metabolic engineering of Rhodotorula toruloides IFO0880 improves C16 and C18 fatty alcohol production from synthetic media. Schultz, J C.; Mishra, S.; Gaither, E.; Mejia, A.; Dinh, H.; Maranas, C.; and Zhao, H. Microbial cell factories, 21(1): 1–14. 2022.
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Developmental changes in lignin composition are driven by both monolignol supply and laccase specificity. Zhuo, C.; Wang, X.; Docampo-Palacios, M.; Sanders, B. C; Engle, N. L; Tschaplinski, T. J; Hendry, J. I; Maranas, C. D; Chen, F.; and Dixon, R. A Science advances, 8(10): eabm8145. 2022.
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A CNN model for predicting binding affinity changes between SARS-CoV-2 spike RBD variants and ACE2 homologues. Chen, C.; Boorla, V. S.; Chowdhury, R.; Nissly, R. H; Gontu, A.; Chothe, S. K; LaBella, L.; Jakka, P.; Ramasamy, S.; Vandegrift, K. J; and others bioRxiv. 2022.
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SARS-CoV-2 Omicron (B. 1.1. 529) Infection of Wild White-Tailed Deer in New York City. Vandegrift, K. J; Yon, M.; Surendran Nair, M.; Gontu, A.; Ramasamy, S.; Amirthalingam, S.; Neerukonda, S.; Nissly, R. H; Chothe, S. K; Jakka, P.; and others Viruses, 14(12): 2770. 2022.
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A CNN model for predicting binding affinity changes between SARS-CoV-2 spike RBD variants and ACE2 homologues (preprint). Chen, C.; Boorla, V. S.; Chowdhury, R.; Nissly, R.; Gontu, A.; Chothe, S.; LaBella, L.; Jakka, P.; Ramasamy, S.; Vandegrift, K.; and others . 2022.
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Examining organic acid production potential and growth-coupled strategies in Issatchenkia orientalis using constraint-based modeling. Suthers, P. F; and Maranas, C. D Biotechnology progress, 38(5): e3276. 2022.
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Development and Validation of Indirect Enzyme-Linked Immunosorbent Assays for Detecting Antibodies to SARS-CoV-2 in Cattle, Swine, and Chicken. Gontu, A.; Marlin, E. A; Ramasamy, S.; Neerukonda, S.; Anil, G.; Morgan, J.; Quraishi, M.; Chen, C.; Boorla, V. S.; Nissly, R. H; and others Viruses, 14(7): 1358. 2022.
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Metabolic modeling for the microbiome. Maranas, C.; Sarkar, D.; and Chan, J. . 2022.
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Proteome capacity constraints favor respiratory ATP generation. Shen, Y.; Dinh, H. V; Cruz, E.; Call, C. M; Baron, H.; Ryseck, R.; Pratas, J.; Subramanian, A.; Fatma, Z.; Weilandt, D.; and others bioRxiv. 2022.
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ML helps predict enzyme turnover rates. Boorla, V. S.; Upadhyay, V.; and Maranas, C. D Nature Catalysis, 5(8): 655–657. 2022.
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De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD). Boorla, V. S.; Chowdhury, R.; Ramasubramanian, R.; Ameglio, B.; Frick, R.; Gray, J. J; and Maranas, C. D Proteins: Structure, Function, and Bioinformatics. 2022.
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Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis. Dinh, H. V; and Maranas, C. D bioRxiv. 2022.
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Building Bottom-up Kinetic Models for Optimizing Cell-Free Lignocellulose Degradation Systems. Schroeder, W.; Olson, D.; and Maranas, C. D In 2022 AIChE Annual Meeting, 2022. AIChE
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Tracking Pyrophosphate Metabolism and Evaluating Its Significance in the Bioprocessing of Lignocellulosic Biomass By Clostridium Thermocellum. Schroeder, W.; Kuil, T.; Olson, D.; and Maranas, C. D In 2022 AIChE Annual Meeting, 2022. AIChE
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  2021 (18)
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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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, 63: 13–33. 2021.
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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. Hendry, J. I; Dinh, H. V; Sarkar, D.; Wang, L.; Bandyopadhyay, A.; Pakrasi, H. B; and Maranas, C. D Metabolites, 11(3): 168. 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 PLoS computational biology, 17(9): e1009448. 2021.
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Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2. 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 Proceedings of the National Academy of Sciences, 118(42): e2106480118. 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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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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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. Genome Medicine, 13(1): 1–14. 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. September~23 2021. US Patent App. 17/332,186
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Multiple spillovers and onward transmission of SARS-CoV-2 in free-living and captive white-tailed deer. Kuchipudi, S. V; Surendran-Nair, M.; Ruden, R. M; Yon, M.; Nissly, R. H; Nelli, R. K; Li, L.; Jayarao, B. M; Vandegrift, K. J; Maranas, C. D; and others BioRxiv. 2021.
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Computational redesign of trans-enoyl-CoA reductase from Treponema denticola (tdTER) to generate focused C8-CoA and C10-CoA library. Ghaffari, S.; Chowdhury, R.; Maranas, C. D; and Maranas, C. COMPUTATIONAL REDESIGN OF ENZYMES AND CHANNEL PROTEINS,1. 2021.
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Incorporating Stability Score into Iterative Protein Redesign and Optimization Suite of Programs (IPRO). Ghaffari, S.; Sarkar, D.; Chowdhury, R.; and Maranas, C. D COMPUTATIONAL REDESIGN OF ENZYMES AND CHANNEL PROTEINS,42. 2021.
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Computational redesign of Outer membrane protein F (OmpF) for capturing chemical warfare agent molecules. Boorla, V. S.; Sarkar, D.; Ghaffari, S.; Chowdhury, R.; and Maranas, C. D COMPUTATIONAL REDESIGN OF ENZYMES AND CHANNEL PROTEINS,27. 2021.
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Integrated Computational and Experimental Study to Dissect the Stress Response of Maize Root. Chowdhury, N.; Schroeder, W.; Zhang, D.; Simons, M. N; Hirel, B.; Cahoon, E.; Maranas, C. D; and Saha, R. In 2021 AIChE Annual Meeting, 2021. AIChE
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  2020 (23)
Metabolic model guided strain design of cyanobacteria. Hendry, J. I; Bandyopadhyay, A.; Srinivasan, S.; Pakrasi, H. B; and Maranas, C. D Current opinion in biotechnology, 64: 17–23. 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. 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(1): 73–79. 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). 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 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 (Fv) 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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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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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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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.; Chowdhury, R.; Gopalakrishnan, S.; Dash, S.; Olson, D.; Amador-Noguez, D.; Lynd, L. R; and Maranas, C. D In 2020 Virtual AIChE Annual Meeting, 2020. AIChE
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Biophysical characterization of the SARS-CoV2 spike protein binding with the ACE2 receptor explains increased COVID-19 pathogenesis. Chowdhury, R.; and Maranas, C. D . 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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  2019 (27)
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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Ipro+/-: A Computational Protein Design Tool Allowing NOT ONLY for Amino Acid Changes but Also Insertions and Deletions. Chowdhury, R.; and Maranas, C. D In 2019 AIChE Annual Meeting, 2019. AIChE
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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.; 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 (24)
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: Top-down synthesis of genome minimized strains for bioproduction. Wang, L.; 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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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: de novo design of variable antibody regions against targeted antigen epitopes. Chowdhury, R.; Allan, M. F; and Maranas, C. D Antibodies, 7(3): 23. 2018.
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