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  • Perspective
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Molecular systems engineering of synthetic cells

Nature Chemistry volume 18, pages 14–22 (2026)Cite this article

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Abstract

Building synthetic versions of biological cells from the bottom up offers an unprecedented opportunity to understand the rules of life and harness cellular capabilities in biotechnology. Whereas substantial progress has been made in recapitulating elementary cell functions, we argue that accelerating the engineering of synthetic cells requires a shift in research practices. The dominant approach—rationally designing and integrating functional modules—becomes restrictive when dealing with the massively complex biochemical pathways associated with life, especially when design principles remain unclear. We advocate moving away from theoretical rational design towards a data-driven model that is centred on library generation. Inspired by a systems chemistry perspective, this strategy prioritizes the systematic creation and distribution of composition–function libraries. To enable this, experimental strategies must integrate high-throughput synthetic cell generation, automation and closed-feedback control of workflows. Broad adoption will also require greater emphasis on quantitative benchmarking, and the de-skilling of techniques, supporting effective laboratory-to-laboratory collaboration.

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Fig. 1: Strategies for the integration of basic synthetic cell functions to engineer multi-functional synthetic cells that show living-cell capabilities.
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Fig. 2: Two broad models for the design of synthetic cells are the functional module view and the systems chemistry view.
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Fig. 3: Idealized automatic pipeline for studying bottom-up synthetic cell reaction networks.
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Fig. 4: Efficient distribution of synthetic cell technologies across laboratories can be accelerated through the adoption of quantitative benchmarking.
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References

  1. Elani, Y. & Seddon, J. M. What it means to be alive: a synthetic cell perspective. Interface Focus https://doi.org/10.1098/rsfs.2023.0036 (2023).

  2. Sampson, K., Sorenson, C. & Adamala, K. P. Preparing for the future of precision medicine: synthetic cell drug regulation. Synth. Biol. 9, ysae004 (2024).

    Google Scholar 

  3. Rothschild, L. J. et al. Building synthetic cells—from the technology infrastructure to cellular entities. ACS Synth. Biol. 13, 974–997 (2024).

    PubMed  PubMed Central  Google Scholar 

  4. Boyd, M. A., Thavarajah, W., Lucks, J. B. & Kamat, N. P. Robust and tunable performance of a cell-free biosensor encapsulated in lipid vesicles. Sci. Adv. 9, eadd6605 (2023).

    PubMed  PubMed Central  Google Scholar 

  5. Osawa, M., Anderson, D. E. & Erickson, H. P. Reconstitution of contractile FtsZ rings in liposomes. Science 320, 792–794 (2008).

    PubMed  PubMed Central  Google Scholar 

  6. Zhan, P., Jahnke, K., Liu, N. & Göpfrich, K. Functional DNA-based cytoskeletons for synthetic cells. Nat. Chem. 14, 958–963 (2022).

    PubMed  PubMed Central  Google Scholar 

  7. Berhanu, S., Ueda, T. & Kuruma, Y. Artificial photosynthetic cell producing energy for protein synthesis. Nat. Commun. 10, 1325 (2019).

    PubMed  PubMed Central  Google Scholar 

  8. Deng, N.-N., Yelleswarapu, M., Zheng, L. & Huck, W. T. S. Microfluidic assembly of monodisperse vesosomes as artificial cell models. JACS 139, 587–590 (2017).

    Google Scholar 

  9. Steinkühler, J. et al. Controlled division of cell-sized vesicles by low densities of membrane-bound proteins. Nat. Commun. 11, 905 (2020).

    PubMed  PubMed Central  Google Scholar 

  10. Peruzzi, J. A., Galvez, N. R. & Kamat, N. P. Engineering transmembrane signal transduction in synthetic membranes using two-component systems. Proc. Natl Acad. Sci. USA 120, 2218610120 (2023).

    Google Scholar 

  11. Rampioni, G. et al. Synthetic cells produce a quorum sensing chemical signal perceived by Pseudomonas aeruginosa. Chem. Commun. 54, 2090–2093 (2018).

    Google Scholar 

  12. Göpfrich, K., Platzman, I. & Spatz, J. P. Mastering complexity: towards bottom-up construction of multifunctional eukaryotic synthetic cells. Trends Biotechnol. 36, 938–951 (2018).

    PubMed  PubMed Central  Google Scholar 

  13. Guindani, C., da Silva, L. C., Cao, S., Ivanov, T. & Landfester, K. Synthetic cells: from simple bio-inspired modules to sophisticated integrated systems. Angew. Chem. 134, e202110855 (2022).

    Google Scholar 

  14. Elani, Y. Interfacing living and synthetic cells as an emerging frontier in synthetic biology. Angew. Chem. 133, 5662–5671 (2021).

    PubMed  Google Scholar 

  15. Hürtgen, D., Härtel, T., Murray, S. M., Sourjik, V. & Schwille, P. Functional modules of minimal cell division for synthetic biology. Adv. Biosyst. 3, 1800315 (2019).

    Google Scholar 

  16. Pogodaev, A. A., Lap, T. T. & Huck, W. T. S. The dynamics of an oscillating enzymatic reaction network is crucially determined by side reactions. ChemSystemsChem https://doi.org/10.1002/syst.202000033 (2021).

  17. van Roekel, H. W. H. et al. Programmable chemical reaction networks: emulating regulatory functions in living cells using a bottom-up approach. Chem. Soc. Rev. 44, 7465–7483 (2015).

    PubMed  Google Scholar 

  18. Jakštaitė, M., Zhou, T., Nelissen, F., Huck, W. T. S. & van Sluijs, B. Active learning maps the emergent dynamics of enzymatic reaction networks. Preprint at https://doi.org/10.26434/chemrxiv-2024-vxfkz (2024).

  19. Cardinale, S. & Arkin, A. P. Contextualizing context for synthetic biology – identifying causes of failure of synthetic biological systems. Biotechnol. J. 7, 856–866 (2012).

    PubMed  PubMed Central  Google Scholar 

  20. Peng, Z. et al. Lipid vesicle-based molecular robots. Lab Chip 24, 996–1029 (2024).

    PubMed  PubMed Central  Google Scholar 

  21. Ghosh, S. et al. Exploring emergent properties in enzymatic reaction networks: design and control of dynamic functional systems. Chem. Rev. 124, 2553–2582 (2024).

    PubMed  PubMed Central  Google Scholar 

  22. Baltussen, M. G., van de Wiel, J., Lia Fernandez Regueiro, C., Jakstaite, M. & Huck, W. T. S. A Bayesian approach to extracting kinetic information from artificial enzymatic networks. Anal. Chem. 94, 7311–7318 (2022).

    PubMed  PubMed Central  Google Scholar 

  23. Borsley, S. Membrane transport, molecular machines, and Maxwell’s demon. ChemSystemsChem https://doi.org/10.1002/syst.202400004 (2024).

  24. Mattia, E. & Otto, S. Supramolecular systems chemistry. Nat. Nanotechnol. 10, 111–119 (2015).

    PubMed  Google Scholar 

  25. Ragazzon, G. & Prins, L. J. Energy consumption in chemical fuel-driven self-assembly. Nat. Nanotechnol. 13, 882–889 (2018).

    PubMed  Google Scholar 

  26. Semenov, S. N. et al. Rational design of functional and tunable oscillating enzymatic networks. Nat. Chem. 7, 160–165 (2015).

    PubMed  Google Scholar 

  27. Maguire, O. R., Wong, A. S. Y., Westerdiep, J. H. & Huck, W. T. S. Early warning signals in chemical reaction networks. Chem. Commun. 56, 3725–3728 (2020).

    Google Scholar 

  28. Sun, M., Deng, J. & Walther, A. Communication and cross-regulation between chemically fueled sender and receiver reaction networks. Angew. Chem. Int. Ed. 62, e202214499 (2023).

    Google Scholar 

  29. Jahnke, K. & Göpfrich, K. Engineering DNA-based cytoskeletons for synthetic cells. Interface Focus 13, 20230028 (2023).

    PubMed  PubMed Central  Google Scholar 

  30. Samanta, A., Baranda Pellejero, L., Masukawa, M. & Walther, A. DNA-empowered synthetic cells as minimalistic life forms. Nat. Rev. Chem. 8, 454–470 (2024).

    PubMed  Google Scholar 

  31. Burns, J. R. et al. Lipid-bilayer-spanning DNA nanopores with a bifunctional porphyrin anchor. Angew. Chem. Int. Ed. 52, 12069–12072 (2013).

    Google Scholar 

  32. Langecker, M., Arnaut, V., List, J. & Simmel, F. C. DNA nanostructures interacting with lipid bilayer membranes. Acc. Chem. Res. 47, 1807–1815 (2014).

    PubMed  Google Scholar 

  33. Zadeh, J. N. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2011).

    PubMed  Google Scholar 

  34. Poppleton, E., Romero, R., Mallya, A., Rovigatti, L. & Šulc, P. OxDNA.org: a public webserver for coarse-grained simulations of DNA and RNA nanostructures. Nucleic Acids Res. 49, W491–W498 (2021).

    PubMed  PubMed Central  Google Scholar 

  35. Pandi, A. et al. A versatile active learning workflow for optimization of genetic and metabolic networks. Nat. Commun. 13, 3876 (2022).

    PubMed  PubMed Central  Google Scholar 

  36. van Sluijs, B. et al. Iterative design of training data to control intricate enzymatic reaction networks. Nat. Commun. 15, 1602 (2024).

    PubMed  PubMed Central  Google Scholar 

  37. Gan, R. et al. High-throughput regulatory part prototyping and analysis by cell-free protein synthesis and droplet microfluidics. ACS Synth. Biol. https://doi.org/10.1021/acssynbio.2c00050 (2022).

  38. Kuruma, Y. & Ueda, T. The PURE system for the cell-free synthesis of membrane proteins. Nat. Protoc. 10, 1328–1344 (2015).

    PubMed  Google Scholar 

  39. Moga, A., Yandrapalli, N., Dimova, R. & Robinson, T. Optimization of the inverted emulsion method for high-yield production of biomimetic giant unilamellar vesicles. ChemBioChem 20, 2674–2682 (2019).

    PubMed  PubMed Central  Google Scholar 

  40. Deshpande, S., Caspi, Y., Meijering, A. E. C. & Dekker, C. Octanol-assisted liposome assembly on chip. Nat. Commun. 7, 10447 (2016).

    PubMed  PubMed Central  Google Scholar 

  41. Fletcher, M. & Elani, Y. On-the-fly microfluidic control of giant vesicle compositions validated by DNA surface charge sensors. ACS Nano 19, 13768–13778 (2025).

    PubMed  PubMed Central  Google Scholar 

  42. Kohyama, S., Frohn, B. P., Babl, L. & Schwille, P. Machine learning-aided design and screening of an emergent protein function in synthetic cells. Nat. Commun. 15, 2010 (2024).

    PubMed  PubMed Central  Google Scholar 

  43. Weakly, H. M. J. et al. Several common methods of making vesicles (except an emulsion method) capture intended lipid ratios. Biophys. J. 124, 1548 (2025).

    PubMed  PubMed Central  Google Scholar 

  44. Supramaniam, P. et al. Measuring encapsulation efficiency in cell-mimicking giant unilamellar vesicles. ACS Synth. Biol. 12, 1227–1238 (2023).

    PubMed  PubMed Central  Google Scholar 

  45. Arter, W. E. et al. Biomolecular condensate phase diagrams with a combinatorial microdroplet platform. Nat. Commun. 13, 7845 (2022).

    PubMed  PubMed Central  Google Scholar 

  46. Kumar, A. et al. Multispectral live-cell imaging with uncompromised spatiotemporal resolution. Nat. Photon. 19, 1146–1156 (2025).

    Google Scholar 

  47. Fletcher, M. et al. DNA-based optical quantification of ion transport across giant vesicles. ACS Nano 16, 17128–17138 (2022).

    PubMed  PubMed Central  Google Scholar 

  48. Al Nahas, K. et al. Measuring thousands of single-vesicle leakage events reveals the mode of action of antimicrobial peptides. Anal. Chem. 94, 9530–9539 (2022).

    PubMed  PubMed Central  Google Scholar 

  49. Genot, A. J. et al. High-resolution mapping of bifurcations in nonlinear biochemical circuits. Nat. Chem. 8, 760–767 (2016).

    PubMed  Google Scholar 

  50. Sugiura, H. et al. Pulse-density modulation control of chemical oscillation far from equilibrium in a droplet open-reactor system. Nat. Commun. 7, 10212 (2016).

    PubMed  PubMed Central  Google Scholar 

  51. Rapp, J. T., Bremer, B. J. & Romero, P. A. Self-driving laboratories to autonomously navigate the protein fitness landscape. Nat. Chem. Eng. 1, 97–107 (2024).

    PubMed  PubMed Central  Google Scholar 

  52. Staufer, O. et al. Science Forum: Building a community to engineer synthetic cells and organelles from the bottom-up. eLife 10, e73556 (2021).

    PubMed  PubMed Central  Google Scholar 

  53. Frischmon, C., Sorenson, C., Winikoff, M. & Adamala, K. P. Build-a-cell: engineering a synthetic cell community. Life 11, 1176 (2021).

    PubMed  PubMed Central  Google Scholar 

  54. Zhu, K. K. et al. Magnetic modulation of biochemical synthesis in synthetic cells. J. Am. Chem. Soc. 146, 13176–13182 (2024).

    PubMed  PubMed Central  Google Scholar 

  55. Rifaie-Graham, O. et al. Photoswitchable gating of non-equilibrium enzymatic feedback in chemically communicating polymersome nanoreactors. Nat. Chem. 15, 110–118 (2023).

    PubMed  Google Scholar 

  56. Ashkenasy, G., Hermans, T. M., Otto, S. & Taylor, A. F. Systems chemistry. Chem. Soc. Rev. 46, 2543–2554 (2017).

    PubMed  Google Scholar 

  57. Lin, T.-Y. et al. Microsoft COCO: common objects in context. Preprint at https://arxiv.org/abs/1405.0312 (2014).

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Acknowledgements

M.F. and Y.E. were supported in this project by a BBSRC award BB/Z514895/1 and BB/W00125X/1. B.D is supported by a PhD studentship awarded by the EPSRC Centre for Doctoral Training in Chemical Biology - Innovation for the Life Sciences, grant ref EP/S023518/1. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Chemical Engineering, Imperial College London, London, UK

    Marcus Fletcher, Bradley Diggines & Yuval Elani

  2. Department of Chemistry, Imperial College London, London, UK

    Bradley Diggines

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  1. Marcus Fletcher
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  2. Bradley Diggines
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  3. Yuval Elani
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Contributions

M.F. conceived the Perspective. B.D. and Y.E. discussed and refined the ideas. M.F. wrote the manuscript. B.D. contributed Fig. 3. All authors commented on the manuscript.

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Correspondence to Yuval Elani.

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Fletcher, M., Diggines, B. & Elani, Y. Molecular systems engineering of synthetic cells. Nat. Chem. 18, 14–22 (2026). https://doi.org/10.1038/s41557-025-02019-z

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