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

Lin W.J., Chiang A.W.T., Zhou E.H., Liang C., Liu C.H., Ma W.L., Cheng W.C., Lewis N.E. iLipidome: enhancing statistical power and interpretability using hidden biosynthetic interdependencies in the lipidome, bioRxiv (2024).

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).

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).

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)

2024

171. 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. Metabolic Engineering, 82:183-192 (2024).

170. 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. Metabolic Engineering, 82:110-122 (2024).

165. 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. Metabolic Engineering, 81, 273-285 (2024). bioRxiv preprint

163. Baghdassarian H., Lewis N.E. Resource Allocation in Mammalian Systems. Biotechnology Advances, 71, 108305 (2024). Preprint

162. Siddharth T., Lewis N.E. Predicting pathways for old and new metabolites through clustering. Journal of Theoretical Biology, 578, 111684 (2024). preprint

2023

160. Kim S.H., Shin S.H., Baek M, Xiong K., Karottki K.J.L.C., Hefzi H., Grav L.M., Pedersen L.E., Kildegaard H.F., Lewis N.E., Lee J.S., Lee G.M. Identification of hyperosmotic stress-responsive genes in Chinese hamster ovary cells via genome-wide virus-free CRISPR/Cas9 screening. Metabolic Engineering, 80:66-77 (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, 21:3736-3745 (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.