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The Lewis CELL Labs – (Cell Engineering and Ligand Linguistics Labs) 

No cell is alone. Normal physiology and diseases arise as cells collaborate, collude, and compete. As cells communicate and interact, what are they saying to each other, how do they regulate their communication, and can we harness these processes and signals for therapeutic purposes?  

At the Lewis CELL labs, we develop systems biology tools and computational models, artificial intelligence (AI) and machine learning (ML) methods; and advanced experimental technologies, including lab automation, robotics and single cell omics. We study cellular communication, cell-cell interactions, and the molecular processes regulating them, thus shedding light on disease and guiding efforts to engineer cells for therapeutics. 

Cell-cell interactions in neurodevelopment, immunology and pediatric health

Many diseases and disorders stem from disrupted cell communication. We use systems biology and machine learning to discover the aberrant signaling seen in autism spectrum disorders1-3. Our algorithms parse through single cell RNA-seq and multi–omics to decipher the cell-cell communication in neurodevelopment, immune cell responses and tissue microenvironment4-9. 

Systems glycobiology and glycoprotein design

Most cell-cell communication involves protein receptors on plasma membranes and secreted signaling proteins. As these proteins are synthesized in the mammalian secretory pathway, most are decorated with large and complex sugar polymers, or glycans. These carbohydrate chains can have a substantial impact on cell-cell communication, host-pathogen interactions, e.g., SARS-CoV-2 infection10, host-microbiome interactions, immune cell functions, and cancer. We deploy computational and experimental techniques to study the physiological importance of glycans, how variations are implicated in pediatric diseases11 or cancer12, how cells make critical glycans13, or how glycosylation can be engineered to obtain more potent and safer protein drugs14-16. In these efforts, we are advancing systems biology, machine learning, and artificial intelligence techniques15,17-20 , developing novel sequencing technologies, and using CRISPR technologies to understand how glycans are regulated21,22.  

Cell engineering for therapeutic production and digital biomanufacturing

Ultimately, by deciphering cell-cell communication and its regulation, we hope to develop therapeutics for complex diseases and technologies that can improve the safety, efficacy, and affordability of potent drugs. We use systems biology, genome editing and robotics-assisted automation to engineer cells that are used for the bioproduction of potent drugs and gene therapies. To do this we focus particularly on the secretory pathway23-25, which produces most signals and receptors used for cell-cell communication. To study and engineer the secretory pathway, we are using experimental techniques to decipher what regulates protein secretion26-28. We are also developing genome-scale computational models of the pathway29, novel algorithms for studying the pathway30, and using these to engineer cell factories31 or optimize bioproduction32. These are further enhanced when studied and engineered alongside other processes, such as cellular metabolism33-36.  

Digital biomanufacturing represents a new era of biologics production, transforming cell line development and bioprocessing by integrating generative AI pipelines, that driven analytics with robotics and lab automation across both upstream and downstream biomanufacturing37,38. To support this effort, we developed a community-consensus CHO metabolic model39 and mapped out networks of the protein secretion pathway29,40 , which serves as a foundational tool for predictive modeling and rational engineering. We also introduced a hybrid framework that integrates experimental data with dynamic bioprocess modeling to accurately predict metabolic shifts and cell culture trajectories to guide cell line engineering efforts41,42.  

Through robotic AI-assisted analytics and data-driven modeling, we envision a future in which trial-and-error is replaced by predictive, adaptive, and scalable manufacturing, delivering high-quality biologics to the clinic faster, more efficiently, and at lower cost.  

 

Click on the links below for details on current Research Projects, Resources, and Open Positions.

Research Projects

Resources

Open Positions

 

 

References 

  1. Courchesne, E., Pramparo, T., Gazestani, V.H., Lombardo, M.V., Pierce, K., and Lewis, N.E. (2019). The ASD Living Biology: from cell proliferation to clinical phenotype. Mol Psychiatry 24, 88–107. 10.1038/s41380-018-0056-y.
  2. Gazestani, V.H., Pramparo, T., Nalabolu, S., Kellman, B.P., Murray, S., Lopez, L., Pierce, K., Courchesne, E., and Lewis, N.E. (2019). A perturbed gene network containing PI3K–AKT, RAS–ERK and WNT–β-catenin pathways in leukocytes is linked to ASD genetics and symptom severity. Nature Neuroscience 22, 1624–1634. 10.1038/s41593-019-0489-x.
  3. Bao, B., Zahiri, J., Gazestani, V.H., Lopez, L., Xiao, Y., Kim, R., Wen, T.H., Chiang, A.W.T., Nalabolu, S., Pierce, K., et al. (2023). A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years. Molecular Psychiatry 28, 822–833. 10.1038/s41380-022-01826-x.
  4. Armingol, E., Officer, A., Harismendy, O., and Lewis, N.E. (2021). Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet 22, 71–88. 10.1038/s41576-020-00292-x.
  5. Armingol, E., Baghdassarian, H.M., Martino, C., Perez-Lopez, A., Aamodt, C., Knight, R., and Lewis, N.E. (2022). Context-aware deconvolution of cell-cell communication with Tensor-cell2cell. Nat Commun 13, 3665. 10.1038/s41467-022-31369-2.
  6. Armingol, E., Ghaddar, A., Joshi, C.J., Baghdassarian, H., Shamie, I., Chan, J., Her, H.L., Berhanu, S., Dar, A., Rodriguez-Armstrong, F., et al. (2022). Inferring a spatial code of cell-cell interactions across a whole animal body. PLoS Comput Biol 18, e1010715. 10.1371/journal.pcbi.1010715.
  7. Armingol, E., Larsen, R.O., Cequeira, M., Baghdassarian, H., and Lewis, N.E. (2022). Unraveling the coordinated dynamics of protein- and metabolite-mediated cell-cell communication. bioRxiv, 2022.2011.2002.514917. 10.1101/2022.11.02.514917.
  8. Armingol, E., Baghdassarian, H.M., and Lewis, N.E. (2024). The diversification of methods for studying cell-cell interactions and communication. Nat Rev Genet 25, 381–400. 10.1038/s41576-023-00685-8.
  9. Baghdassarian, H., Dimitrov, D., Armingol, E., Saez-Rodriguez, J., and Lewis, N.E. (2023). Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. bioRxiv. 10.1101/2023.04.28.538731.
  10. Martino, C., Kellman, B.P., Sandoval, D.R., Clausen, T.M., Cooper, R., Benjdia, A., Soualmia, F., Clark, A.E., Garretson, A.F., Marotz, C.A., et al. (2025). SARS-CoV-2 infectivity can be modulated through bacterial grooming of the glycocalyx. mBio 16, e04015–04024. doi:10.1128/mbio.04015-24.
  11. Autran, C.A., Kellman, B.P., Kim, J.H., Asztalos, E., Blood, A.B., Spence, E.C.H., Patel, A.L., Hou, J., Lewis, N.E., and Bode, L. (2018). Human milk oligosaccharide composition predicts risk of necrotising enterocolitis in preterm infants. Gut 67, 1064–1070. 10.1136/gutjnl-2016-312819.
  12. Bao, B., Kellman, B.P., Chiang, A.W.T., Zhang, Y., Sorrentino, J.T., York, A.K., Mohammad, M.A., Haymond, M.W., Bode, L., and Lewis, N.E. (2021). Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis. Nature Communications 12, 4988. 10.1038/s41467-021-25183-5.
  13. Kellman, B.P., Richelle, A., Yang, J.Y., Chapla, D., Chiang, A.W.T., Najera, J.A., Liang, C., Fürst, A., Bao, B., Koga, N., et al. (2022). Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration. Nat Commun 13, 2455. 10.1038/s41467-022-29867-4.
  14. Kulakova, L., Li, K.H., Chiang, A.W.T., Schwoerer, M.P., Suzuki, S., Schoffelen, S., Elkholy, K.H., Chao, K.L., Shahid, S., Kumar, B., et al. (2025). Glycoengineering of the hepatitis C virus E2 glycoprotein improves biochemical properties and enhances immunogenicity. npj Vaccines 10, 121. 10.1038/s41541-025-01161-6.
  15. Kellman, B.P., Mariethoz, J., Zhang, Y., Shaul, S., Alteri, M., Sandoval, D., Jeffris, M., Armingol, E., Bao, B., Lisacek, F., et al. (2024). Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network. bioRxiv. 10.1101/2024.05.15.594334.
  16. Kellman, B.P., Sandoval, D., Zaytseva, O.O., Brock, K., Baboo, S., Nachmanson, D., Irvine, E.B., Armingol, E., Mih, N., Zhang, Y., et al. (2024). Protein structure, a genetic encoding for glycosylation. bioRxiv, 2024.2005.2015.594261. 10.1101/2024.05.15.594261.
  17. Kellman, B.P., and Lewis, N.E. (2021). Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication. Trends Biochem Sci 46, 284–300. 10.1016/j.tibs.2020.10.004.
  18. Li, H., Chiang, A.W.T., and Lewis, N.E. (2022). Artificial intelligence in the analysis of glycosylation data. Biotechnol Adv 60, 108008. 10.1016/j.biotechadv.2022.108008.
  19. Yom, A., Chiang, A., and Lewis, N.E. (2024). Boltzmann Model Predicts Glycan Structures from Lectin Binding. Analytical Chemistry 96, 8332–8341. 10.1021/acs.analchem.3c04992.
  20. Li, H., Peralta, A.G., Schoffelen, S., Hansen, A.H., Arnsdorf, J., Schinn, S.M., Skidmore, J., Choudhury, B., Paulchakrabarti, M., Voldborg, B.G., et al. (2024). LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling. bioRxiv. 10.1101/2024.03.27.587044.
  21. Weiss, R.J., Spahn, P.N., Chiang, A.W.T., Liu, Q., Li, J., Hamill, K.M., Rother, S., Clausen, T.M., Hoeksema, M.A., Timm, B.M., et al. (2021). Genome-wide screens uncover KDM2B as a modifier of protein binding to heparan sulfate. Nat Chem Biol 17, 684–692. 10.1038/s41589-021-00776-9.
  22. Weiss, R.J., Spahn, P.N., Toledo, A.G., Chiang, A.W.T., Kellman, B.P., Li, J., Benner, C., Glass, C.K., Gordts, P., Lewis, N.E., and Esko, J.D. (2020). ZNF263 is a transcriptional regulator of heparin and heparan sulfate biosynthesis. Proc Natl Acad Sci U S A 117, 9311–9317. 10.1073/pnas.1920880117.
  23. Kuo, C.C., Chiang, A.W., Shamie, I., Samoudi, M., Gutierrez, J.M., and Lewis, N.E. (2018). The emerging role of systems biology for engineering protein production in CHO cells. Curr Opin Biotechnol 51, 64–69. 10.1016/j.copbio.2017.11.015.
  24. Malm, M., Kuo, C.C., Barzadd, M.M., Mebrahtu, A., Wistbacka, N., Razavi, R., Volk, A.L., Lundqvist, M., Kotol, D., Tegel, H., et al. (2022). Harnessing secretory pathway differences between HEK293 and CHO to rescue production of difficult to express proteins. Metab Eng 72, 171–187. 10.1016/j.ymben.2022.03.009.
  25. Wu, M.Y.M., Rocamora, F., Samoudi, M., Robinson, C.M., Kuo, C.-C., Pristovšek, N., Grav, L.M., Kildegaard, H.F., Lee, G.M., Campos, A.R., and Lewis, N.E. (2025). Improving Recombinant Antibody Production Using FcBAR: An In Situ Approach to Detect and Amplify Protein-Protein Interactions. bioRxiv, 2025.2006.2012.659199. 10.1101/2025.06.12.659199.
  26. Samoudi, M., Kuo, C.C., Robinson, C.M., Shams-Ud-Doha, K., Schinn, S.M., Kol, S., Weiss, L., Petersen Bjorn, S., Voldborg, B.G., Rosa Campos, A., and Lewis, N.E. (2021). In situ detection of protein interactions for recombinant therapeutic enzymes. Biotechnol Bioeng 118, 890–904. 10.1002/bit.27621.
  27. Masson, H.O., Kuo, C.-C., Malm, M., Lundqvist, M., Sievertsson, Å., Berling, A., Tegel, H., Hober, S., Uhlén, M., Grassi, L., et al. (2022). Deciphering the determinants of recombinant protein yield across the human secretome. bioRxiv, 2022.2012.2012.520152. 10.1101/2022.12.12.520152.
  28. Masson, H.O., Samoudi, M., Robinson, C.M., Kuo, C.C., Weiss, L., Shams Ud Doha, K., Campos, A., Tejwani, V., Dahodwala, H., Menard, P., et al. (2024). Inferring secretory and metabolic pathway activity from omic data with secCellFie. Metab Eng 81, 273–285. 10.1016/j.ymben.2023.12.006.
  29. Gutierrez, J.M., Feizi, A., Li, S., Kallehauge, T.B., Hefzi, H., Grav, L.M., Ley, D., Baycin Hizal, D., Betenbaugh, M.J., Voldborg, B., et al. (2020). Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion. Nat Commun 11, 68. 10.1038/s41467-019-13867-y.
  30. Kuo, C.C., Chiang, A.W.T., Baghdassarian, H.M., and Lewis, N.E. (2021). Dysregulation of the secretory pathway connects Alzheimer’s disease genetics to aggregate formation. Cell Syst 12, 873–884.e874. 10.1016/j.cels.2021.06.001.
  31. Kol, S., Ley, D., Wulff, T., Decker, M., Arnsdorf, J., Schoffelen, S., Hansen, A.H., Jensen, T.L., Gutierrez, J.M., Chiang, A.W.T., et al. (2020). Multiplex secretome engineering enhances recombinant protein production and purity. Nat Commun 11, 1908. 10.1038/s41467-020-15866-w.
  32. Schinn, S.M., Morrison, C., Wei, W., Zhang, L., and Lewis, N.E. (2021). A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures. Biotechnol Bioeng 118, 2118–2123. 10.1002/bit.27714.
  33. Hefzi, H., Ang, K.S., Hanscho, M., Bordbar, A., Ruckerbauer, D., Lakshmanan, M., Orellana, C.A., Baycin-Hizal, D., Huang, Y., Ley, D., et al. (2016). A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism. Cell Syst 3, 434–443.e438. 10.1016/j.cels.2016.10.020.
  34. Richelle, A., and Lewis, N.E. (2017). Improvements in protein production in mammalian cells from targeted metabolic engineering. Curr Opin Syst Biol 6, 1–6. 10.1016/j.coisb.2017.05.019.
  35. Ha, T.K., Òdena, A., Karottki, K.J.C., Kim, C.L., Hefzi, H., Lee, G.M., Faustrup Kildegaard, H., Nielsen, L.K., Grav, L.M., and Lewis, N.E. (2023). Enhancing CHO cell productivity through a dual selection system using Aspg and Gs in glutamine free medium. Biotechnol Bioeng 120, 1159–1166. 10.1002/bit.28318.
  36. Hefzi, H., Martínez-Monge, I., Marin de Mas, I., Cowie, N.L., Toledo, A.G., Noh, S.M., Karottki, K.J.C., Decker, M., Arnsdorf, J., Camacho-Zaragoza, J.M., et al. (2025). Multiplex genome editing eliminates lactate production without impacting growth rate in mammalian cells. Nat Metab 7, 212–227. 10.1038/s42255-024-01193-7.
  37. Park, S.Y., Choi, D.H., Song, J., Lakshmanan, M., Richelle, A., Yoon, S., Kontoravdi, C., Lewis, N.E., and Lee, D.Y. (2024). Driving towards digital biomanufacturing by CHO genome-scale models. Trends Biotechnol 42, 1192–1203. 10.1016/j.tibtech.2024.03.001.
  38. Kavoni, H., Savizi, I.S.P., Lewis, N.E., and Shojaosadati, S.A. (2025). Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches. Biotechnol Adv 78, 108480. 10.1016/j.biotechadv.2024.108480.
  39. Giusto, P.D., Choi, D.-H., Antonakoudis, A., Duraikannan, V.G., Craveur, P., Cowie, N.L., Ganapathy, T., Ramesh, K., Benavidez-López, S., Orellana, C.A., et al. (2025). A community-consensus reconstruction of Chinese Hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free CHO cells. bioRxiv, 2025.2004.2010.647063. 10.1101/2025.04.10.647063.
  40. Masson, H., Tat, J., Di Giusto, P., Antonakoudis, A., Shamie, I., Baghdassarian, H., Samoudi, M., Robinson, C.M., Kuo, C.-C., Koga, N., et al. (2024). A reconstruction of the mammalian secretory pathway identifies mechanisms regulating antibody production. bioRxiv, 2024.2011.2014.623668. 10.1101/2024.11.14.623668.
  41. Gopalakrishnan, S., Johnson, W., Valderrama-Gomez, M.A., Icten, E., Tat, J., Ingram, M., Fung Shek, C., Chan, P.K., Schlegel, F., Rolandi, P., et al. (2024). COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling. Metab Eng 82, 183–192. 10.1016/j.ymben.2024.02.012.
  42. Gopalakrishnan, S., Johnson, W., Valderrama-Gomez, M.A., Icten, E., Tat, J., Lay, F., Diep, J., Gomez, N., Stevens, J., Schlegel, F., et al. (2024). Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks. Metab Eng 85, 94–104. 10.1016/j.ymben.2024.07.009.