SWARM study

SWARM – Small robots With collective behaviour as AI-driven cancer therapies; building Regulations for future nanoMedicines




Cancer Nanomedicine

Cancer occurs when abnormal cells divide in an uncontrolled way. Many cancers can be cured. But in some people cancer can return. Cancer drugs, such as chemotherapy, need to be able to kill all the cancer cells, but this means they can also kill healthy cells. Nanomedicine is the medical application of nanotechnology which works on tiny scales called ‘nanometres’ (one-billionth of a metre). Nanoparticles are nanosized particles that can assist the delivery of chemotherapy drugs to cancer cells. Scientists and Engineers can use simulations for selecting nanoparticles so drugs can more effectively reach the tumour while avoiding side effects.


Using simulations, scientists and engineers are working on adding swarm behaviour (present in social animals such as birds, ants, fish and termites) to nanoparticles and tiny robots (nanobots). Nanoswarms are multiple nanoparticles or nanobots that can interact with each other or their environment to achieve a task (e.g. deliver chemotherapy to a tumour without killing healthy cells), exhibiting collective behaviour inspired by swarm behaviour.

SWARM study – aim & research question

This project is investigating the ethics and regulations of the first in-human clinical trial of nanoswarms. We will be using interviews initially and focus groups in the next phase to explore the attitudes of stakeholders towards this swarm technology in healthcare, combined with ethical/legal analysis to consider how swarm medicine should be regulated in clinical trials. The aim is to explore how nanoswarm medicine should be regulated once this technology is available for first-in human clinical trials.


This study is being organised by Matimba Swana, PhD student in the Trustworthy Autonomous Systems in Functionality Node and Academic Supervisors; Dr. Sabine Hauert, Reader (Associate Professor) in Swarm Engineering and Prof. Jonathan Ives, Professor of Empirical Bioethics & Deputy Director of Centre for Ethics in Medicine.

Would you like to participate?

We are now closed for volunteers. Please check back for future updates.

Five fun facts

  • The word swarm comes from the old English word swearm, ultimately from Proto-Indo-European *swer- (“to buzz, hum”), which is thought to have been spoken as a single language 4500 BC to 2500 BC.
  • A swarm is a large or dense group.
  • Swarm behaviour, or swarming, is a collective behaviour common in biology, from cell colonies to insect swarms and bird flocks.
  • The term “swarm” is applied also to artificial entities which mimic collective behaviours, as in a robot swarm.
  • Swarm behaviour was first simulated on a computer in 1986 with the simulation program boids (an artificial life program which stimulates the flocking behaviour of birds).

The SWARM study is part of a larger UKRI-funded PhD which is part of the Trustworthy Autonomous Systems Node in Functionality research programme, which is a multidisciplinary collaboration between ethicists, sociologists, computer scientists and engineers working together to produce guidelines for the development of trustworthy autonomous systems with evolving functionality.

Research Ethics Approval

This project has been reviewed and approved by the University of Bristol Faculty of Engineering Research Ethics Committee (Ref: 11141).


Nanoswarm Learning modules

Here are four video modules that describe what nanoswarms are, their potential future applications, the ethical and legal challenges and what a future clinical trial could look like.

Module 1 what are nanoswarms?

Objectives: introduce the learner to nanoswarm technology. Learning outcomes: the learner can explain what a nanoswarm is and what the difference is between a nanoparticle, nanorobot and nanoswarm.

Module 2 what are the potential future applications of nanoswarms?

Objectives: cover details of nanoswarm behaviour and how this could be used as a cancer nanomedicine. Learning outcomes: The learner can describe and identify the potential applications of nanoswarm technology.

Module 3 What are the ethical and legal challenges?

Objective: describe social, legal and ethical issues that can arise with nanoswarm technology. Learning outcomes: The learner can demonstrate how to identify future benefits and issues of nanoswarm technology.

Module 4 What could clinical trials look like in future?

Objectives: assess emerging technologies and consider how this impacts clinical trials. Learning outcomes: The learner can give examples of how to identify potential trends, opportunities or threats with emerging technologies.

Find out more

Collective cancer nanomedicines:

  • Publications:
    • Hauert, S., Bhatia, S.N.: Mechanisms of cooperation in cancer nanomedicine: towards systems nanotechnology. Trends in Biotechnology. 32, 448–455 (2014). https://doi.org/10.1016/J.TIBTECH.2014.06.010.
    • Stillman, N.R., Kovacevic, M., Balaz, I., Hauert, S.: In silico modelling of cancer nanomedicine, across scales and transport barriers. npj Computation-al Materials 2020 6:1. 6, 1–10 (2020). https://doi.org/10.1038/s41524-020-00366-8.
    • Stillman, N.R., Balaz, I., Tsompanas, M.A., Kovacevic, M., Azimi, S., Lafond, S., Adamatzky, A., Hauert, S.: Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment. npj Com-putational Materials. 7, 150 (2021). https://doi.org/10.1038/S41524-021-00614-5.
    • Swana, M. C., Blee, J., Stillman, N., Ives, J. C. S., & Hauert, S. (2022). Swarms: The Next Frontier for Cancer Nanomedicine. In Cancer, Complexity, Computation (pp. 269-288). (Emergence, Complexity and Computation). Springer Nature. https://doi.org/10.1007/978-3-031-04379-6
    • Swana, M., Ives, J., and Hauert, S. (2022). Future Nanomedicines: building a regulatory framework for the first-in-human nanoswarm cancer clinical trial. In Proceedings of the 16th Forum Global Forum on Bioethics in Research (GFBR 2022)  Ethics of AI in global health research. Cape Town, South Africa. Available at https://www.gfbr.global/wp-content/uploads/2022/12/Matimba-Swana_GFBR-2022_Pecha-Kucha.pdf
    • Swana, M., Ives, J., and Hauert, S. (2022). How should we regulate the first-in-human nanoswarm cancer clinical trial? UKRI TAS AHM EXHIBITION – POSTERS. Available at https://tas.ac.uk/wp-content/uploads/2022/07/How-should-we-regulate-the-first-in-human-nanoswarm-cancer-clini.pdf
    • Swana, M., Ives, J., and Hauert, S. (2023). Swarm Medicine: Developing guidance for in-human testing of emerging swarm-based cancer nanomedicines. In Proceedings of the First International Symposium on Trustworthy Autonomous Systems (TAS ’23). Association for Computing Machinery, New York, NY, USA, Article 28, 1–4. https://doi.org/10.1145/3597512.3599699
    • Simó, C., Serra-Casablancas, M., Hortelao, A.C. et al. Urease-powered nanobots for radionuclide bladder cancer therapy. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-023-01577-y
  • Lectures and workshops:

Overview on medical robots:

  • Publications:
    • Ceylan, H., Yasa, I.C., Kilic, U., Hu, W., Sitti, M.: Translational prospects of untethered medical microrobots. Progress in Biomedical Engineering. 1, 012002 (2019). https://doi.org/10.1088/2516-1091/AB22D5.
    • King, A Miniature medical robots step out from sci-fi. Nature article. https://www.nature.com/articles/d41586-022-00859-0
    • Saadeh, Y., Vyas, D.: Nanorobotic Applications in Medicine: Current Proposals and Designs. Am J Robot Surg. 1, 4 (2014). https://doi.org/10.1166/AJRS.2014.1010.
    • Schmidt, C.K., Medina-Sánchez, M., Edmondson, R.J., Schmidt, O.G.: Engineering microrobots for targeted cancer therapies from a medical perspective. Nature Communications 2020 11:1. 11, 1–18 (2020). https://doi.org/10.1038/s41467-020-19322-7.

Industry digital twin clinical trials:

Unlearn.AI create AI generated digital twins for clinical trials.

  • Publications:
    • Fisher, C.K., Smith, A.M., Walsh, J.R. et al. Machine learning for comprehensive forecasting of Alzheimer’s Disease progression. Sci Rep 9, 13622 (2019). https://doi.org/10.1038/s41598-019-49656-2
    • Walsh, J.R., Roumpanis, S., Bertolini, D. and Delmar, P. (2022), Evaluating Digital Twins for Alzheimer’s Disease using Data from a Completed Phase 2 Clinical Trial. Alzheimer’s Dement., 18: e065386. https://doi.org/10.1002/alz.065386
  • Webinar:

Please email swarm-study@bristol.ac.uk if you have any questions or would like more information about the Swarm study.