Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

npj Systems Biology and Applications
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. npj systems biology and applications
  3. articles
  4. article
In silico modeling of anterior foregut endoderm differentiation towards lung epithelial progenitors
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 26 January 2026

In silico modeling of anterior foregut endoderm differentiation towards lung epithelial progenitors

  • Amirmahdi Mostofinejad1,
  • David A. Romero1,
  • Dana Brinson2,3,
  • Thomas K. Waddell2,3,4,
  • Golnaz Karoubi1,3,5 &
  • …
  • Cristina H. Amon1,2 

npj Systems Biology and Applications , Article number:  (2026) Cite this article

  • 180 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Developmental biology
  • Engineering
  • Mathematics and computing
  • Stem cells
  • Systems biology

Abstract

Directed differentiation of human induced pluripotent stem cells (iPSCs) into anterior foregut endoderm (AFE) and lung progenitors (LPs) has wide-ranging implications for lung developmental biology, disease modeling, and regenerative medicine. We expand on a previously developed mathematical modeling framework and apply it to the directed differentiation of AFE into LPs. A model-based approach guides experimental design, followed by a multistage model inference process: maximum likelihood estimation based on in vitro data and identifiability analyses to eliminate unidentifiable candidates, thereby guiding model selection. To the authors’ knowledge, this is the first mathematical model of the population dynamics of directed differentiation of AFE into LPs. The model suggests that the overall dynamics are primarily driven by AFE proliferation and differentiation into LPs. In silico experiments predict that daily media change nearly doubles LP yields compared to cultures without media replenishment. Moreover, the model suggests that higher split ratios on day 10 enhance yield per input cell, a measure of differentiation efficiency, by 26%. This work provides a blueprint for refining iPSC-based lung lineage differentiation protocols by combining empirical data and mathematical modeling.

Similar content being viewed by others

The in vitro multilineage differentiation and maturation of lung and airway cells from human pluripotent stem cell–derived lung progenitors in 3D

Article 01 March 2021

Directed differentiation of mouse pluripotent stem cells into functional lung-specific mesenchyme

Article Open access 13 June 2023

A distal lung organoid model to study interstitial lung disease, viral infection and human lung development

Article 10 May 2023

Data availability

All data and code used to generate the results in this manuscript are available through https://github.com/amostof/inSilicoAFEPaper.

Code availability

All data and code used to generate the results in this manuscript are available through https://github.com/amostof/inSilicoAFEPaper.

References

  1. Hawkins, F. et al. Prospective isolation of nkx2-1–expressing human lung progenitors derived from pluripotent stem cells. J. Clin. Investig. 127, 2277–2294 (2017).

    Google Scholar 

  2. McCauley, K. B. et al. Efficient derivation of functional human airway epithelium from pluripotent stem cells via temporal regulation of Wnt signaling. Cell Stem Cell 20, 844–857 (2017).

    Google Scholar 

  3. Spence, J. R. et al. Directing differentiation of pluripotent stem cells toward somatic progenitors and functional tissue units. FASEB J. 25, 3775–3785 (2011).

    Google Scholar 

  4. Green, M. D. et al. Generation of anterior foregut endoderm from human embryonic and induced pluripotent stem cells. Nat. Biotechnol. 29, 267–272 (2011).

    Google Scholar 

  5. Jacob, A. et al. Differentiation of human pluripotent stem cells into functional lung alveolar epithelial cells. Cell Stem Cell 21, 472–488 (2017).

    Google Scholar 

  6. Jacob, A. et al. Derivation of self-renewing lung alveolar epithelial type ii cells from human pluripotent stem cells. Nat. Protoc. 14, 3303–3332 (2019).

    Google Scholar 

  7. Yuan, H. et al. Scalable expansion of human pluripotent stem cells under suspension culture condition with human platelet lysate supplementation. Front. Cell Dev, Biol. 11, 1280682 (2023).

    Google Scholar 

  8. Venkatesan, M. et al. Recombinant production of growth factors for application in cell culture. Iscience 25, 105054 (2022).

  9. Möller, J. & Pörtner, R. Digital twins for tissue culture techniques-concepts, expectations, and state of the art. Processes 9, 447 (2021).

    Google Scholar 

  10. Villaverde, A. F., Pathirana, D., Fröhlich, F., Hasenauer, J. & Banga, J. R. A protocol for dynamic model calibration. Brief. Bioinforma. 23, bbab387 (2022).

    Google Scholar 

  11. Geris, L., Lambrechts, T., Carlier, A. & Papantoniou, I. The future is digital: in silico tissue engineering. Curr. Opin. Biomed. Eng. 6, 92–98 (2018).

    Google Scholar 

  12. Hurley, K. et al. Reconstructed single-cell fate trajectories define lineage plasticity windows during differentiation of human PSC-derived distal lung progenitors. Cell Stem Cell 26, 593–608 (2020).

    Google Scholar 

  13. Engle, S. J. & Vincent, F. Small molecule screening in human induced pluripotent stem cell-derived terminal cell types. J. Biol. Chem. 289, 4562–4570 (2014).

    Google Scholar 

  14. Bock, C. et al. Reference maps of human ES and IPS cell variation enable high-throughput characterization of pluripotent cell lines. Cell 144, 439–452 (2011).

    Google Scholar 

  15. Varghese, B., Ling, Z. & Ren, X. Reconstructing the pulmonary niche with stem cells: a lung story. Stem Cell Res. Ther. 13, 161 (2022).

    Google Scholar 

  16. Mostofinejad, A. et al. In silico model development and optimization of in vitro lung cell population growth. PLOS ONE 19, 1–27 (2024).

    Google Scholar 

  17. Mostofinejad, A. et al. In silico modeling of directed differentiation of induced pluripotent stem cells to definitive endoderm. PLOS Comput. Biol. 21, 1–30 (2025).

    Google Scholar 

  18. Lavielle, M.Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools, 1st edn. https://doi.org/10.1201/b17203 (Chapman and Hall/CRC, 2014).

  19. Walter, E. Identifiability of Parametric Models (Elsevier, 2014).

  20. Salmaniw, Y. & Browning, A. P. Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators. J. Math. Biol. 91, 4 (2025).

    Google Scholar 

  21. Simpson, M. J., Browning, A. P., Warne, D. J., Maclaren, O. J. & Baker, R. E. Parameter identifiability and model selection for sigmoid population growth models. J. Theor. Biol. 535, 110998 (2022).

    Google Scholar 

  22. Dong, R., Goodbrake, C., Harrington, H. A. & Pogudin, G. Differential elimination for dynamical models via projections with applications to structural identifiability. J. Applied Algebra Geometry 7, 194–235 (2023).

    Google Scholar 

  23. Rackauckas, C. & Nie, Q. Differentialequations.jl–a performant and feature-rich ecosystem for solving differential equations in Julia.J. Open Research Software 5, 15 (2017).

    Google Scholar 

  24. Bezanson, J., Edelman, A., Karpinski, S. & Shah, V. B. Julia: A fresh approach to numerical computing. SIAM Rev. 59, 65–98 (2017).

    Google Scholar 

  25. Vallat, R. Pingouin: statistics in python. J. Open Source Softw. 3, 1026 (2018).

    Google Scholar 

  26. Stein, M. Large sample properties of simulations using Latin hypercube sampling. Technometrics 29, 143–151 (1987).

    Google Scholar 

  27. Moles, C. G., Mendes, P. & Banga, J. R. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13, 2467–2474 (2003).

    Google Scholar 

  28. Gábor, A. & Banga, J. R. Robust and efficient parameter estimation in dynamic models of biological systems. BMC Syst. Biol. 9, 74 (2015).

    Google Scholar 

  29. Wieland, F.-G., Hauber, A. L., Rosenblatt, M., T”nsing, C. & Timmer, J. On structural and practical identifiability. Opin. Syst. Biol 25, 60–69 (2021).

    Google Scholar 

  30. Raue, A., Karlsson, J., Saccomani, M. P., Jirstrand, M. & Timmer, J. Comparison of approaches for parameter identifiability analysis of biological systems. Bioinformatics 30, 1440–1448 (2014).

    Google Scholar 

  31. VandenHeuvel, D. J. Profilelikelihood.jl (2023).

  32. Simpson, M. J. & Maclaren, O. J. Profile-wise analysis: a profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models. PLoS Comput. Biol. 19, e1011515 (2023).

    Google Scholar 

  33. Farshidfar, S. S. et al. Towards a validated musculoskeletal knee model to estimate tibiofemoral kinematics and ligament strains: comparison of different anterolateral augmentation procedures combined with isolated ACL reconstructions. Biomed. Eng. OnLine 22, 31 (2023).

    Google Scholar 

  34. El Wajeh, M. et al. Can the Kuznetsov model replicate and predict cancer growth in humans?. Bull. Math. Biol. 84, 130 (2022).

    Google Scholar 

  35. Sobol, I. M. Sensitivity analysis for non-linear mathematical models. Math. Model. Comput. Exp. 1, 407–414 (1993).

    Google Scholar 

  36. Bates, D. et al. Juliastats/glm.jl: v1.9.0. https://doi.org/10.5281/zenodo.8345558 (2023).

  37. Liu, Q. et al. Advances in the application of bone morphogenetic proteins and their derived peptides in bone defect repair. Compos. Part B: Eng. 262, 110805 (2023).

    Google Scholar 

  38. Fernandes, R., Barbosa-Matos, C., Borges-Pereira, C., Carvalho, A. L. R. T. d & Costa, S. Glycogen synthase kinase-3 inhibition by chir99021 promotes alveolar epithelial cell proliferation and lung regeneration in the lipopolysaccharide-induced acute lung injury mouse model. Int. J. Mol. Sci. 25, 1279 (2024).

    Google Scholar 

  39. Wilson, H. K., Canfield, S. G., Hjortness, M. K., Palecek, S. P. & Shusta, E. V. Exploring the effects of cell seeding density on the differentiation of human pluripotent stem cells to brain microvascular endothelial cells. Fluids Barriers CNS 12, 1–12 (2015).

    Google Scholar 

  40. McBeath, R., Pirone, D. M., Nelson, C. M., Bhadriraju, K. & Chen, C. S. Cell shape, cytoskeletal tension, and rhoa regulate stem cell lineage commitment. Dev. cell 6, 483–495 (2004).

    Google Scholar 

  41. Peerani, R. et al. Niche-mediated control of human embryonic stem cell self-renewal and differentiation. EMBO J. 26, 4744–4755 (2007).

    Google Scholar 

  42. Huang, H., Ye, K. & Jin, S. Cell seeding strategy influences metabolism and differentiation potency of human induced pluripotent stem cells into pancreatic progenitors. Biotechnol. J. 20, e70022 (2025).

    Google Scholar 

  43. Ptasinski, V. et al. Modeling fibrotic alveolar transitional cells with pluripotent stem cell-derived alveolar organoids. Life Sci. Alliance 6 (2023).

  44. Burridge, P. W., Holmström, A. & Wu, J. C. Chemically defined culture and cardiomyocyte differentiation of human pluripotent stem cells. Curr. Protoc. Hum. Genet. 87, 21–3 (2015).

    Google Scholar 

  45. Stephens, P. A., Sutherland, W. J. & Freckleton, R. P. What is the Allee effect? Oikos 87, 185–190 (1999).

  46. Masters, J. R. & Stacey, G. N. Changing medium and passaging cell lines. Nat. Protoc. 2, 2276–2284 (2007).

    Google Scholar 

  47. Hong, P., Boyd, D., Beyea, S. D. & Bezuhly, M. Enhancement of bone consolidation in mandibular distraction osteogenesis: a contemporary review of experimental studies involving adjuvant therapies. J. Plast. Reconstruct. Aesthetic Surg. 66, 883–895 (2013).

    Google Scholar 

  48. Sharow, K. A., Temkin, B. & Asson-Batres, M. A. Retinoic acid stability in stem cell cultures. Int. J. Dev. Biol. 56, 273–278 (2012).

    Google Scholar 

  49. Charlebois, D. A. & Balázsi, G. Modeling cell population dynamics. In Silico Biol. 13, 21–39 (2019).

    Google Scholar 

  50. Longmire, T. A. et al. Efficient derivation of purified lung and thyroid progenitors from embryonic stem cells. Cell Stem Cell 10, 398–411 (2012).

    Google Scholar 

  51. Huang, S. X. et al. Efficient generation of lung and airway epithelial cells from human pluripotent stem cells. Nat. Biotechnol. 32, 84 (2014).

    Google Scholar 

  52. Suzuki, S. et al. Differentiation of human pluripotent stem cells into functional airway basal stem cells. STAR Protoc. 2, 100683 (2021).

    Google Scholar 

  53. Myers, P. J., Lee, S. H. & Lazzara, M. J. Mechanistic and data-driven models of cell signaling: Tools for fundamental discovery and rational design of therapy. Curr. Opin. Syst. Biol. 28, 100349 (2021).

    Google Scholar 

  54. Pir, P. & Le Novère, N.Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine, 331–350 (Springer New York, 2016).

  55. Ingalls, B. P. Mathematical Modeling in Systems Biology: an Introduction (MIT Press, 2013).

  56. Nishimura, H. et al. Kinetics of glut1 and glut4 glucose transporters expressed in Xenopus oocytes. J. Biol. Chem. 268, 8514–8520 (1993).

    Google Scholar 

  57. Fujii, S. & Beutler, E. High glucose concentrations partially release hexokinase from inhibition by glucose 6-phosphate. Proc. Natl. Acad. Sci. 82, 1552–1554 (1985).

    Google Scholar 

  58. Zhang, B. et al. Cooperative transport mechanism of human monocarboxylate transporter 2. Nat. Commun. 11, 2429 (2020).

    Google Scholar 

  59. Coy, R. et al. Combining in silico and in vitro models to inform cell seeding strategies in tissue engineering. J. R. Soc. Interface 17, 20190801 (2020).

    Google Scholar 

  60. Osiecki, M. J., McElwain, S. D. & Lott, W. B. Modelling mesenchymal stromal cell growth in a packed bed bioreactor with a gas permeable wall. PLoS ONE 13, e0202079 (2018).

  61. Mehrian, M. et al. Maximizing neotissue growth kinetics in a perfusion bioreactor: an in silico strategy using model reduction and Bayesian optimization. Biotechnol. Bioeng. 115, 617–629 (2018).

    Google Scholar 

  62. Marciniak-Czochra, A., Stiehl, T., Ho, A. D., Jäger, W. & Wagner, W. Modeling of asymmetric cell division in hematopoietic stem cells—regulation of self-renewal is essential for efficient repopulation. Stem Cells Dev. 18, 377–386 (2009).

    Google Scholar 

  63. Wodarz, D. Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulation. J. Theor. Biol. 448, 86–93 (2018).

    Google Scholar 

  64. Duchesne, R., Guillemin, A., Crauste, F. & Gandrillon, O. Calibration, selection and identifiability analysis of a mathematical model of the in vitro erythropoiesis in normal and perturbed contexts. In Silico Biol. 13, 55–69 (2019).

    Google Scholar 

  65. Hossain, M. S., Bergstrom, D. & Chen, X. Modelling and simulation of the chondrocyte cell growth, glucose consumption and lactate production within a porous tissue scaffold inside a perfusion bioreactor. Biotechnol. Rep. 5, 55–62 (2015).

    Google Scholar 

  66. Kalami Yazdi, A., Nadjafikhah, M. & Distefano III, J. Combos2: an algorithm to the input–output equations of dynamic biosystems via Gaussian elimination. J. Taibah Univ. Sci. 14, 896–907 (2020).

    Google Scholar 

  67. Raue, A., Becker, V., KlingmĂĽller, U. & Timmer, J. Identifiability and observability analysis for experimental design in nonlinear dynamical models. Chaos: Interdiscip. J. Nonlinear Sci. 20, 045105 (2010).

    Google Scholar 

  68. Storn, R. & Price, K. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997).

    Google Scholar 

  69. Price, K., Storn, R. M. & Lampinen, J. A. Differential Evolution: a Practical Approach to Global Optimization (Springer Science & Business Media, 2006).

  70. Feldt, R. Blackboxoptim.jl. https://github.com/robertfeldt/BlackBoxOptim.jl (2018).

  71. Das, S. & Suganthan, P. N. Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evolut. Comput. 15, 4–31 (2010).

    Google Scholar 

  72. Mashwani, W. K. Enhanced versions of differential evolution: state-of-the-art survey. Int. J. Comput. Sci. Math. 5, 107–126 (2014).

    Google Scholar 

  73. Nelder, J. A. & Mead, R. A simplex method for function minimization. Comput. J. 7, 308–313 (1965).

    Google Scholar 

  74. Johnson, S. G. The NLopt nonlinear-optimization package. https://github.com/stevengj/nlopt (2007).

  75. Wright, S., Nocedal, J. et al. Numerical optimization. Springe. Sci. 35, 7 (1999).

    Google Scholar 

  76. Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2015).

  77. Stoica, P. & Selen, Y. Model-order selection: a review of information criterion rules. IEEE Signal Process. Mag. 21, 36–47 (2004).

    Google Scholar 

  78. Pawitan, Y. In All Likelihood: Statistical Modelling and Inference Using Likelihood (Oxford University Press, 2001).

  79. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edn (Springer, 2009).

  80. Daume, S., Kofler, S., Kager, J., Kroll, P. & Herwig, C. Generic workflow for the setup of mechanistic process models. In Pörtner, R. (ed.) Animal Cell Biotechnology: Methods and Protocols, vol. 2095 of Methods in Molecular Biology, 189–211 (Humana, 2020).

  81. Zhang, X.-Y., Trame, M. N., Lesko, L. J. & Schmidt, S. Sobol sensitivity analysis: a tool to guide the development and evaluation of systems pharmacology models. CPT: Pharmacomet. Syst. Pharmacol. 4, 69–79 (2015).

    Google Scholar 

  82. Brinson, D. Figure 1. Experimental protocol and the lineage models. https://BioRender.com/2uyml6l (2025).

Download references

Acknowledgements

This study is funded in part by a Collaborative Health Research Project (CHRP) grant provided by the Canadian Institutes of Health Research in partnership with the Natural Sciences and Engineering Research Council (158270 to C.H.A. and T.W.). The study is also supported by the New Frontiers in Research Fund Transformation stream (NFRFT-2020-00787 to T.W., C.H.A., G.K., and NFRFT-2022-00447 to C.H.A.), and the University of Toronto’s Medicine by Design initiative, which receives funding from the Canada First Research Excellence Fund (to C.A. and T.W.).

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada

    Amirmahdi Mostofinejad, David A. Romero, Golnaz Karoubi & Cristina H. Amon

  2. Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

    Dana Brinson, Thomas K. Waddell & Cristina H. Amon

  3. Latner Thoracic Surgery Research Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada

    Dana Brinson, Thomas K. Waddell & Golnaz Karoubi

  4. Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada

    Thomas K. Waddell

  5. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada

    Golnaz Karoubi

Authors
  1. Amirmahdi Mostofinejad
    View author publications

    Search author on:PubMed Google Scholar

  2. David A. Romero
    View author publications

    Search author on:PubMed Google Scholar

  3. Dana Brinson
    View author publications

    Search author on:PubMed Google Scholar

  4. Thomas K. Waddell
    View author publications

    Search author on:PubMed Google Scholar

  5. Golnaz Karoubi
    View author publications

    Search author on:PubMed Google Scholar

  6. Cristina H. Amon
    View author publications

    Search author on:PubMed Google Scholar

Contributions

C.A., D.R., T.W., and G.K. designed and supervised the research. A.M. and D.B. performed research and analyzed data. A.M., D.R., and D.B. took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research and the final manuscript.

Corresponding author

Correspondence to Cristina H. Amon.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mostofinejad, A., Romero, D.A., Brinson, D. et al. In silico modeling of anterior foregut endoderm differentiation towards lung epithelial progenitors. npj Syst Biol Appl (2026). https://doi.org/10.1038/s41540-026-00650-1

Download citation

  • Received: 06 March 2025

  • Accepted: 09 January 2026

  • Published: 26 January 2026

  • DOI: https://doi.org/10.1038/s41540-026-00650-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Associated content

Collection

Virtual human development: merging experiments and theory to understand human development

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Collections
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • Aims & Scope
  • Content types
  • Journal Information
  • About the Editors
  • Contact
  • Open Access
  • Calls for Papers
  • Article Processing Charges
  • Editorial policies
  • Journal Metrics
  • About the Partner

Publish with us

  • For Authors and Referees
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

npj Systems Biology and Applications (npj Syst Biol Appl)

ISSN 2056-7189 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics