See below for our recent publications in peer-reviewed journals, books, and patents. Also, icons to the right filter publications by major topics. Public presentations and lectures can also be downloaded from here.
‡corresponding/senior author, * equal contribution
For a current list, click here.
Preprints under peer review
Gopalakrishnan S., Johnson W., Valderrama-Gomez M.A., Icten E., Tat J., Ingram M., Shek C.F., Chan P.K., Schlegel F., Rolandi P., Kontoravdi C., Lewis N.E. COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling. bioRxiv (2023)
Gopalakrishnan S., Johnson W., Valderrama-Gomez M.A., Icten E., Tat J., Lay F., Diep J., Gomez N., Stevens J., Schlegel F., Rolandi P., Kontoravdi C., Lewis N.E. Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks. bioRxiv (2023)
Liang C., Murray S., Li Y., Lee R., Low A., Sasaki S., Chiang A.W.T., Lin W.J., Mathews J., Barnes W. Lewis N.E. LipidSIM: inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, systematic Markov Modeling framework. bioRxiv (2023).
Masson H.O., Samoudi M., Robinson C.M., Kuo C.C., Weiss L., Shams-Ud-Doha K., Campos A.R., Tejwani V., Dahodwala H., Menard P., Voldborg B.G., Sharfstein S.T., Lewis N.E. Inferring secretory and metabolic pathway activity from omic data with secCellFie. bioRxiv (2023).
Masson H.O., Kuo C.C., Malm M., Lundqvist M., Sievertsson Å, Berling A., Tegel H., Hober S., Uhlén M., Grassi L., Hatton D., Rockberg J.‡, Lewis N.E.‡ Deciphering the determinants of recombinant protein yield across the human secretome, bioRxiv (2022).
Siddharth T., Lewis N.E. Predicting pathways for old and new metabolites through clustering. arXiv (2022).
Armingol E., Larsen R.O., Cequeira M, Baghdassarian H., Lewis N.E. Unraveling the coordinated dynamics of protein- and metabolite-mediated cell-cell communication. bioRxiv (2022)
2023
160. Kim SH, Shin S, Baek M, Xiong K, Karottki KJLC, Hefzi H, Grav LM, Pedersen LE, Kildegaard HF, Lewis NE, Lee JS, Lee GM. A metabolic CRISPR-Cas9 screen in Chinese hamster ovary cells identifies glutamine-sensitive genes. Metabolic Engineering, in press (2023). doi:10.1016/j.ymben.2023.09.006
155. Choi YM, Choi DH, Lee YQ, Koduru L, Lewis, NE, Lakshmanan M, Lee, D.Y. Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations. Computational and Structural Biotechnology Journal , in press (2023). doi: 10.1016/j.csbj.2023.07.025
152. Masson H.O., Karottki K.J.L.C., Tat J., Hefzi H.‡, Lewis N.E.‡ From Observational to Actionable: rethinking Omics in Biologics Production, Trends in Biotechnology, 41(9), 1127-1138 (2023). preprint doi: 10.1016/j.tibtech.2023.03.009
150. Masson H.O., Borland D., Reilly J., Telleria A., Shrivastava S., Watson M., Bustillo L., Li Z., Capps L., Kellman B.P., King Z.A., Richelle A., Lewis N.E.‡, Robasky K.‡ ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data. STAR Protocols, 4 (1), 102069 (2023) doi: 10.1016/j.xpro.2023.102069
149. Ha TK*, Òdena A*, Karottki KJLC, Kim CL, Hefzi H, Lee GM, Kildegaard HF, Nielsen LK, Grav LM, Lewis NE. Enhancing CHO cell productivity through a dual selection system in glutamine free medium. Biotechnology & Bioengineering, 120 (4), 1159-1166. (2023). doi: 10.1002/bit.28318, Preprint
148. Gopalakrishnan S., Joshi C.J., Valderrama Gomez M., Icten E., Rolandi P., Johnson W., Kontoravdi C., Lewis N.E. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data, Metabolic Engineering, 75, 181-191 (2023). doi: 10.1016/j.ymben.2022.12.003, bioRxiv preprint
2022
145. Kenefake D., Armingol E., Lewis N.E., Pistikopoulos E.N. An Improved Algorithm for Flux Variability Analysis. BMC Bioinformatics, 23, 550 (2022).
140. Joshi C.J., Ke W., Drangowska-Way A., O’Rourke E.J.‡, Lewis N.E.‡ What are housekeeping genes? PLoS Computational Biology, 18:e1010295 (2022).
137. Kellman B.P.*, Richelle A.*, Yang J.Y.E., Chapla D.G., Chiang A.W.T., Najera J., Liang C, Fürst A, Bao B., Koga N., Mohammad M.A., Bruntse A.B., Haymond M.W., Moremen K.W., Bode L., Lewis N.E.. Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration. Nature Communications, 13, 2455 (2022). doi: 10.1038/s41467-022-29867-4
131. Savizi I.S.P., Maghsoudi N., Motamedian E., Lewis N.E., Shojaosadati S.A. Valine feeding reduces ammonia production through rearrangement of metabolic fluxes in central carbon metabolism of CHO cells. Applied Microbiology and Biotechnology, 106, 1113–1126 (2022). doi:10.1007/s00253-021-11755-4, Authorea preprint
2021
126. Khaleghi M.K., Savizi I.S.P., Lewis N.E., Shojaosadati S.A. Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters. Biotechnology Journal, 16:2100212. (2021). doi: 10.22541/au.162083622.21592768/v1
120. Richelle, A., Kellman, B.P., Wenzel, A.T., Chiang, A.W.T., Reagan, T., Gutierrez, J.M., Joshi, C., Li, S., Liu, J.K., Masson, H., Lee, J., Li, Z., Heirendt, L., Trefois, C., Juarez, E.F., Bath, T., Borland, D., Mesirov, J.P., Robasky, K., Lewis, N.E. Model-based assessment of mammalian cells metabolic functionalities from omics data. Cell Reports Methods, 1:100040 (2021). doi: 10.1016/j.crmeth.2021.100040
119. Samoudi M, Masson H, Kuo CC, Robinson C, Lewis NE. From omics to cellular mechanisms in mammalian cell factory development, Curr Opin in Chem Eng, 32: 100688 (2021). doi: 10.1016/j.coche.2021.100688
118. Chiang A.W.T.‡, Baghdassarian H.M., Kellman B.P., Bao B., Sorrentino J.T., Liang C., Kuo C.C., Masson H.O., Lewis N.E. Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy. Journal of Biomedical Science, 28:50 (2021). doi: 10.1186/s12929-021-00746-2, PMCID: PMC8218521
117. Savizi I.S.P., Motamedian E., Maghsoudi N., Lewis N.E., Jimenez del Val I., Shojaosadati S.A. An integrated modular framework for modeling the effect of ammonium on the sialylation process of monoclonal antibodies produced by CHO cells. Biotechnology Journal, 16:2100019 (2021). doi: 10.1002/biot.202100019
115. Karottki, K.J.L.C., Hefzi, H., Li, S., Pedersen, L.E., Spahn, P., Ruckerbauer, D., Bort, J.H., Thomas, A., Lee, J.S., Borth, N., Lee, G.M., Kildegaard, H.F.‡, Lewis, N.E.‡ A metabolic CRISPR-Cas9 screen in Chinese hamster ovary cells identifies glutamine-sensitive genes. Metabolic Engineering, 66:114-122 (2021). doi: 10.1016/j.ymben.2021.03.017. bioRxiv doi: 10.1101/2020.05.07.081604, PMCID: PMC8193919
114. Schinn S-M, Morrison C, Wei W, Zhang L, Lewis NE. Systematic evaluation of parameterization for genome-scale metabolic models of cultured mammalian cells. Metabolic Engineering, 66:21-30 (2021). bioRxiv doi:10.1101/2020.06.24.169938.
113. Granados, J.C., Richelle, A., Gutierrez, J.M., Zhang, P., Bhatnagar, V., Lewis, N.E., Nigam, S.K. Coordinate regulation of systemic and kidney tryptophan metabolism by the drug transporters OAT1 and OAT3. Journal of Biological Chemistry. 296:100575 (2021). doi: 10.1016/j.jbc.2021.100575, PMCID: PMC8102410
112. Schinn S.M., Morrison C., Wei W., Zhang L., Lewis N.E. A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures. Biotechnology & Bioengineering, 118:2118-2123 (2021). doi: 10.1002/bit.27714 bioRxiv doi: 10.1101/2020.09.02.279687
103. Fouladiha, H., Marashi, S.A., Li, S., Li, Z., Masson, H.O., Vaziri, B., Lewis, N.E. Systematically gap-filling the genome-scale model of CHO cells. Biotechnology Letters, 43:73–87 (2021). doi: 10.1007/s10529-020-03021-w, bioRxiv: 10.1101/2020.01.27.921296
2020
102. Martino, C.*, Kellman, B.P.*, Sandoval, D.R.*, Clausen, T.M., Marotz, C., Song, S.J., Wandro, S., Zaramela, L., Benítez, R.A.S., Zhu, Q., Armingol, E., Vázquez-Baeza, Y., McDonald, D., Sorrentino, J., Taylor, B., Belda-Ferre, P., Liang, C., Zhang, Y., Schifanella, L., Klatt, N.R., Havulinna, A.S., Jousilahti, P., Huang, S., Haiminen, N., Parida, L., Kim, H.C., Swafford, A.D., Zengler, K., Cheng, S., Inouye, M., Niiranen, T., Jain, M., Salomaa, V., Esko, J.D.‡, Lewis, N.E.‡, Knight, R.‡ Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity. bioRxiv, (2020). DOI: 10.1101/2020.08.17.238444. News Coverage: Medical News
95. Saba*, J. A. , Ke*, W. , Yao, C. , Drangowska-Way, A. , Joshi, C., Mony, V.K., Hilzendeger, M., Liu, X., Liu, J., Benjamin, S., Locasale, J., Patti, G., Lewis, N.E.‡, O’Rourke, E.J.‡ Dietary serine enhances chemotherapeutic toxicity in C. elegans through promoting thymidine deficiency in the microbiota. Nature Communications, 11:2587 (2020). doi: 10.1038/s41467-020-16220-w News coverage: UVAToday
94. Szeliova, D., Ruckerbauer, D.E., Galleguillos, S.N., Petersen, L.B., Natter, K., Hanscho, M., Troyer, C. Causon, T., Schoeny, H., Christensen, H.B., Lee, D.Y., Lewis, N.E., Koellensperger, G., Hann, S., Nielsen, L., Borth, N., Zanghellini, J. What CHO is made of: Variations in the biomass composition of Chinese hamster ovary cell lines. Metabolic Engineering, 61:288-300 (2020). doi: 10.1016/j.ymben.2020.06.002
92. Joshi, C., Schinn, S., Richelle, A., Shamie, I., O’Rourke, E., Lewis, N.E. StanDep: capturing transcriptomic variability improves context-specific metabolic models. PLoS Computational Biology, 16(5): e1007764. (2020). doi: 10.1371/journal.pcbi.1007764
90. Fouladiha, H., Marashi, S.A., Torkashvand, F., Mahboudi, F., Lewis, N.E., Vaziri, B. A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production. Bioprocess and Biosystems Engineering, 43, 1381–1389 (2020). doi: 10.1007/s00449-020-02332-6
88. Gutierrez, J.M.*, Feizi, A.*, Li, S., Kallehauge, T.B., Grav, L.M., Hefzi, H., Ley, D., Baycin Hizal, D., Betenbaugh, M.J., Voldborg, B., Kildegaard, H.F., Lee, G.M., Palsson, B.O., Nielsen, J., Lewis, N.E. Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion. Nature Communications, 11:68 (2020). doi: 10.1038/s41467-019-13867-y News coverage: ScienceNews.dk
85. Lieven, C., Beber, M.E., Olivier, B.G., Bergmann, F.T., Babaei, P., Bartell, J.A., Blank, L.M., Chauhan, S., Correia, K., Diener, C., Dräger, A., Ebert, B.E., Edirisinghe, J.N., Fleming, R.M.T., Garcia-Jimenez, B., van Helvoirt, W., Henry, C., Hermjakob, H., Herrgard, M.J., Kim, H.U., King, Z., Koehorst, J.J., Klamt, S., Klipp, E., Lakshmanan, M., Le Novere, N., Lee, D.Y., Lee, S.Y., Lee, S., Lewis, N.E., Ma, H., Machado, D., Mahadevan, R., Maia, P., Mardinoglu, A., Medlock, G.L., Monk, J., Nielsen, J., Nielsen, L.K., Nogales, J., Nookaew, I., Resendis, O., Palsson, B.O., Papin, J.A., Patil, K.R., Price, N.D., Richelle, A., Rocha, I., Schaap, P., Sheriff, R.S.M., Shoaie, S., Sonnenschein, N., Teusink, B., Vilaca, P., Vik, J.O., Wodke, J.A., Xavier, J.C., Yuan, Q., Zakhartsev, M., Zhang, C. Memote: A community driven effort towards a standardized genome-scale metabolic model test suite. Nature Biotechnology, 38:272–276 (2020). doi: 10.1038/s41587-020-0446-y
2019
81. Li, S.*, Richelle, A.*, Lewis, N.E. Enhancing product and bioprocess attributes using genome-scale models of CHO metabolism. Cell Culture Engineering: Recombinant Protein Production , p.73 (2019). ISBN: 978-3-527-34334-8
80. Cyrielle, C., Joshi, C., Lewis, N.E., Laetitia, M., Andersen, M.R.Adaption of Generic Metabolic Models to Specific Cell Lines for Improved Modeling of Biopharmaceutical Production and Prediction of Processes. Cell Culture Engineering: Recombinant Protein Production , p.127 (2019). ISBN: 978-3-527-34334-8
79. Richelle, A., Joshi, C., Lewis, N.E. Assessing key decisions for transcriptomic data integration in biochemical networks. PLoS Computational Biology, 15(7): e1007185 (2019). doi: 10.1371/journal.pcbi.1007185
74. Richelle, A., Chiang, A.W.T., Kuo, C.C., Lewis, N.E. Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions. PLoS Computational Biology, 15: e1006867 (2019). doi: 10.1371/journal.pcbi.1006867
70. Heirendt, L., Arreckx, S., Pfau, T., Mendoza, S.N., Richelle, A., Heinken, A., Haraldsdottir, H.S., Keating, S.M., Vlasov, V., Wachowiak, J., Magnusdottir, S., Ng, C.Y., Preciat, G., Zagare, A., Chan, S.H.J., Aurich, M.K., Clancy, C.M., Modamio, J., Sauls, J.T., Noronha, A., Bordbar, A., Cousins, B., El Assal, D.C., Ghaderi, S., Ahookhosh, M., Ben Guebila, M., Apaolaza, I., Kostromins, A., Le, H.M., Ma, D., Sun, Y., Valcarcel, L.V., Wang, L., Yurkovich, J.T., Vuong, P.T., El Assal, L.P., Hinton, S., Bryant, W.A., Aragon Artacho, F.J., Planes, F.J., Stalidzans, E., Maass, A., Santosh Vempala, Hucka, M., Saunders, M.A., Maranas, C.D., Lewis, N.E., Sauter, T., Palsson, B.Ø., Thiele, I., Fleming, R.M.T. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. Nature Protocols, 14:639-702 (2019). doi: 10.1038/s41596-018-0098-2 ArXiv: 1710.04038
2018
66. Lee, J.S., Park, J.H., Ha, T.K., Samoudi, M., Lewis, N.E., Palsson, B.O., Kildegaard, H.F., Lee, G.M. Revealing key determinants of clonal variation in transgene expression in recombinant CHO cells using targeted genome editing, ACS Synthetic Biology, 7 (12):2867-2878 (2018). doi: 10.1021/acssynbio.8b00290
65. Brunk, E.*, Chang, R.L., Xia, J., Hefzi, H., Yurkovich, J., Kim, D., Buckmiller, E., Wang, H.H., Yang, C., Palsson, B.Ø., Church, G.M.‡, Lewis, N.E.*‡ Characterizing post-translational modifications in prokaryotic metabolism using a multi-scale workflow, Proc. Nat. Acad. Sci. USA, 115 (43): 11096-11101 (2018). bioRxiv doi: 10.1101/180646
62. Witting, M.A., Hastings, J., Rodriguez, N., Joshi, C.J., Hattwell, J.P., Ebert, P.R., van Weeghel, M., Wakelam, M., Houtkooper, R., Mains, A., Le Novère, N., Sadykoff, S., Schroeder, F.,Lewis, N.E., Schirra, H.J., Kaleta, C., Casanueva, O. Modeling meets Metabolomics – The WormJam Consensus Model as basis for Metabolic Studies in the model organism Caenorhabditis elegans, Frontiers in Molecular Biosciences, 5:96 (2018) . doi:10.3389/fmolb.2018.00096
59. Kuo, C.C., Chiang, A.W.T., Shamie, I., Samoudi, M., Gutierrez, J.M., Lewis, N.E.‡ The emerging role of systems biology for engineering protein production in CHO cells. Current Opinion in Biotechnology, 51:64–69 (2018). doi: 10.1016/j.copbio.2017.11.015
58. Abdel-Haleem, A.M., Hefzi, H., Mineta, K., Gao, X., Gojobori, T., Palsson, B.O., Lewis, N.E., Jamshidi, N. Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting. PLoS Computational Biology, 14(1): e1005895 (2018). doi: 10.1371/journal.pcbi.1005895
2017
55. Abdel-Haleem, A.M., Lewis, N.E., Jamshidi, N., Mineta, K., Gao, X., Gojobori, T. The emerging facets of noncancerous Warburg effect. Frontiers in Endocrinology, 8:297 (2017). DOI: 10.3389/fendo.2017.00279
54. Richelle, A., Lewis, N.E.. Improvements in protein production in mammalian cells from targeted metabolic engineering. Current Opinion in Systems Biology, 6:1-6 (2017). DOI: 10.1016/j.coisb.2017.05.019
52. Opdam, S.*, Richelle, A.*, Kellman, B., Li, S., Zielinski, D.C., Lewis, N.E.‡ A systematic evaluation of methods for tailoring genome-scale metabolic models. Cell Systems, 4:1-12 (2017). DOI:10.1016/j.cels.2017.01.010
46. Hastings, J., et al. WormJam: Consensus C. elegans metabolic reconstruction and metabolomics community. Worm, 6:e1373939 (2017). DOI:10.1080/21624054.2017.1373939
2016
45. Hefzi, H.*, Ang, K.S.*, Hanscho, M.*, Bordbar, A., Ruckerbauer, D., Lakshmanan, M., Orellana, C.A., Baycin-Hizal, D., Huang, H., Ley, D., Martínez, V.S., Kyriakopoulos, S., Jiménez, N.E., Zielinski, D.C., Quek, L.E., Wulff, T., Arnsdorf, J., Li, S., Lee, J.S., Paglia, G., Loira, N., Spahn, P.N., Pedersen, L.E., Gutierrez, J.M., King, Z.A., Lund, A.M., Nagarajan, H., Thomas, A., Abdel-Haleem, A.M., Zanghellini, J., Kildegaard, H.F., Voldborg, B.G., Gerdtzen, Z.P., Betenbaugh, M.J., Palsson, B.O., Andersen, M.R., Nielsen, L.K., Borth, N.‡, Lee, D.Y.‡, Lewis, N.E.‡ A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism. Cell Systems, 3, 434-443 (2016). DOI:10.1016/j.cels.2016.10.020, News coverage: phys.org, UCSD Health Sciences, Nordic Life Science News, Novo Nordisk Fonden
43. Swainston, N., Smallbone, K., Hefzi, H., Dobson, P.D., Brewer, J., Hanscho, M., Zielinski, D.C., Ang, K.S., Gardiner, N.J., Gutierrez, J.M., Kyriakopoulos, S., Lakshmanan, M., Li, S., Liu, J.K., Martínez, V.S., Orellana, C.A., Quek, L.E., Thomas, A., Zanghellini, J., Borth, N., Lee, D.Y., Nielsen, L.K., Kell, D.B., Lewis, N.E., Mendes, P. Recon 2.2: from reconstruction to model of human metabolism. Metabolomics, 12:109 (2016). DOI: 10.1007/s11306-016-1051-4
42. Huang, S., Chong, N., Lewis, N.E., Jia, W., Xie, G., Garmire, L.X. Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis. Genome Medicine, 8(1):1 (2016). DOI: 10.1186/s13073-016-0289-9
40. King, Z.A., Lu, J., Dräger, A., Miller, P., Federowicz, S., Lerman, J., Ebrahim, A., Palsson, B.O., Lewis, N.E.‡ BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models. Nucleic Acids Research, 44(D1):D515-22 (2016). DOI: 10.1093/nar/gkv1049 BiGG Models website
2015
37. Ebrahim, A., Almaas, E., Bauer, E., Bordbar, A., Burgard, A.P., Chang, R.L., Dräger, A., Famili, I., Feist, A.M., Fleming, R.M.T., Fong, S.S., Hatzimanikatis, V., Herrgård, M.J., Holder, A., Hucka, M., Hyduke, D., Jamshidi, N., Lee, S.Y., Le Novère, N., Lerman, J.A., Lewis, N.E., Ma, D., Mahadevan, R., Maranas, C., Nagarajan, H., Navid, A., Nielsen, J., Nielsen, L.K., Nogales, J., Noronha, A., Pal, C., Palsson, B.O., Papin, J.A., Patil, K.R., Price, N.D., Reed, J., Saunders, M., Senger, R.S., Sonnenschein, N., Sun, Y., Thiele, I. Do genome‐scale models need exact solvers or clearer standards?. Molecular Systems Biology, 11: 831 (2015). DOI: 10.15252/msb.20156157
35. King, Z.A., Dräger, A., Ebrahim, A., Sonnenschein, N., Lewis, N.E., Palsson, B.O. Escher: A web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Computational Biology, 11:e1004321 (2015). DOI: 10.1371/journal.pcbi.1004321
34. Swann, J., Jamshidi, N., Lewis, N.E., Winzeler, E.A.. Systems analysis of host–parasite interactions. WIREs: Systems Biology and Medicine, 7(6), 381–400 (2015). DOI: 10.1002/wsbm.1311
33. Gutierrez, J.M., Lewis, N.E.‡ Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling. Biotechnology Journal, 10:939–949 (2015). DOI: 10.1002/biot.201400647
32. Rodriguez, R.*, Thomas, A.*, Watanabe, L., Vazirabad, I.Y., Kofia, V., Gómez, H.F., Mittag, F., Matthes, J., Rudolph, J., Wrzodek, F., Netz, E., Diamantikos, A., Eichner, J., Keller, R., Wrzodek, C., Fröhlich, S., Lewis, N.E., Myers, C.J., Le Novère, N., Palsson, B.O., Hucka, M., Dräger, A. JSBML 1.0: providing a smorgasbord of options to encode systems biology models. Bioinformatics, 31(20):3383-3386 (2015). DOI: 10.1093/bioinformatics/btv341
2014
27. Kumar, A., Harrelson, T., Lewis, N.E., Gallagher, E., LeRoith, D., Shiloach, J., Betenbaugh, M.J.Multi-tissue computational modeling analyzes pathophysiology of Type 2 Diabetes in MKR mice. PLoS One, 9(7): e102319. DOI: 10.1371/journal.pone.0102319
26. Bordbar, A., Nagarajan, H., Lewis, N.E., Schellenberger, J., Latif, H., Federowicz, S., Ebrahim, A., Palsson, B.O. Minimal metabolic pathway structure is consistent with associated biomolecular interactions. Molecular Systems Biology, 10:737 (2014). DOI: 10.15252/msb.20145243
2013
24. Lewis, N.E.‡, Abdel-Haleem, A.M. The evolution of genome-scale models of cancer metabolism. Front. Physiol. 4:237 (2013). DOI: 10.3389/fphys.2013.00237 ‡ corresponding author
21. Hyduke, D.R., Lewis, N.E., Palsson, B.Ø. Analysis of omics data with genome-scale models of metabolism. Molecular BioSystems, 9:167 (2013). doi: 10.1039/c2mb25453k
2012
20. Nam, H.J.*, Lewis, N.E.‡*, Lerman, J.A., Lee, D.H., Chang, R.L., Kim, D., Palsson, B.Ø.‡ Network context and selection in the evolution to enzyme specificity. Science. 337:1101-1104 (2012). ‡ corresponding author, * equal contribution
19. Noor, E.‡, Lewis, N.E.‡, Milo, R. A proof for loop-law constraints in stoichiometric metabolic networks. BMC Systems Biology. 6:140 (2012). Highly Accessed. ‡ corresponding author
17. Hefzi, H., Palsson, B.Ø., Lewis, N.E.‡. Reconstruction of genome-scale metabolic networks. In Handbook of Systems Biology. 229 (2013). doi:10.1016/B978-0-12-385944-0.00012-5 ‡ corresponding author
16. Lerman, J.A., Hyduke, D.R., Latif, H., Portnoy, V.A., Lewis, N.E., Orth, J.D., Schrimpe-Rutledge, A.C., Smith, R.D., Adkins, J.N., Zengler, K.A., Palsson, B.Ø. In silico method for modelling metabolism and gene product expression at genome scale. Nature Communications. 3:929 (2012).
15. Lewis, N.E., Nagarajan, H., Palsson, B.Ø. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology.10:291-305 (2012). Click here for accompanying website with a catalog of constraint-based methods.
2011
13. Schellenberger, J., Que, R., Fleming, R.T., Thiele, I., Orth, J., Feist, A.M., Zielinski , D.C., Bordbar, A., Lewis, N.E., Rahmanian, S., Kang, J., Hyduke, D., Palsson, B.Ø. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nature Protocols, 6:1290-307 (2011).
12. Nam, H.J.*, Conrad, T.M., Lewis, N.E.*‡. The role of cellular objectives and selective pressures in metabolic pathway evolution. Current Opinion in Biotechnology, 22:595-600 (2011). * equal contribution, ‡ corresponding author
11. Conrad, T.M., Lewis, N.E., Palsson, B.Ø. Microbial Laboratory Evolution in the Era of Genome-Scale Science. Molecular Systems Biology, 7:509 (2011).
10. Schellenberger, J., Lewis, N.E., Palsson, B.Ø. Elimination of thermodynamically infeasible loops in steady state metabolic models. Biophysical Journal, 100:544-53 (2011).
2010
9. Lewis, N.E., Schramm, G., Bordbar, A., Schellenberger, J., Andersen, M.P., Cheng, J.K., Patel, N., Yee, A., Lewis, R.A., Eils, R., König, R., Palsson, B.Ø. Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nature Biotechnology, 28:1279–1285 (2010). Paper highlighted by Nature Methods (January 2011).
7. Bordbar, A., Lewis, N.E., Schellenberger, J., Palsson, B.Ø., Jamshidi, N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Molecular Systems Biology, 6:422 (2010).
6. Portnoy, V.A., Scott, D.A., Lewis, N.E., Tarasova, Y., Osterman, A.L., Palsson, B.Ø. Deletion of genes encoding cytochrome oxidases and quinol monooxygenase blocks the aerobic-anaerobic shift in Escherichia coli K-12 MG1655. Appl. Environ. Microbiol., 76:6529-40 (2010).
5. Lewis, N.E., Hixson, K.K., Conrad, T.M., Lerman, J.A., Charusanti, P., Polpitiya, A.D., Adkins, J.N., Schramm, G., Purvine, S.O., Lopez-Ferrer, D., Weitz, K.K., Eils, R., König, R., Smith, R.D., Palsson, B.Ø. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular Systems Biology, 6:390 (2010).
4. Bar-Even, A., Noor, E., Lewis, N.E., Milo, R. Design and analysis of synthetic carbon fixation pathways. Proc. Natl. Acad. Sci. USA., 107:8889-8894 (2010).
2009
3. Lewis, N.E., Cho, B.K., Knight, E.M. Palsson, B. Ø. Gene expression profiling and the use of genome-scale in silico models of Escherichia coli for analysis: providing context for content. J. Bacteriol., 191:3437-44 (2009).
2. Lewis, N.E., Jamshidi, N., Thiele, I. & Palsson, B.Ø. Metabolic systems biology: a constraint-based approach. In Encyclopedia of Complexity and Systems Science 5535 (Springer, New York, 2009). DOI:10.1007/978-3-642-27737-5_329-2
2004
Patents and applications
11. Hefzi, H., Lewis, N.E., Karottki, K.J.L.C., Kildegaard, H. Asparaginase Based Selection System for Heterologous Protein Expression in Mammalian Cells. Patent pending.
3. Hefzi, H., Lewis, N.E. Mammalian cells devoid of lactate dehydrogenase activity Patent US11242510B2.
1. Herrgard, M. J., Pedersen, L.E., Lewis, N.E., Bruntse, A.B. Methods for modeling Chinese hamster ovary (CHO) cell metabolism. WO Patent WO2015010088-A1.
