PanaXea: A Framework for the Development and Parametrization of Agent-Based Models

  • Dario Panada ,
  • Bijan Parsia
  • a,b The University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
Cite as
Panada D., Parsia B. (2021). PanaXea: A Framework for the Development and Parametrization of Agent-Based Models. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 90-98. DOI:


This paper presents a framework for the development of agent-based models aimed at facilitating parameter space exploration by means of established parameter tuning strategies. Such simulations often require a high number of parameters to account for the complexity of the underlying processes. It is often the case that parameter values are not known, or that when they are known measurements are reported with a large margin of error. Despite this, publications in the eld often rely on single values rather than considering larger search spaces for their parameters. It is therefore uncertain whether results obtained are an artifact of a very specic combination of parameter values or truly representative of the underlying phenomenon. Our solution is applicable to any sort of agent-based model and can easily be expanded to incorporate further parameter tuning algorithms. We then tested our framework by reproducing an existing model of angiogenesis and exploring changes in simulation results across parameter values. Our case-study results suggest the aforementioned model is highly sensitive to the choice of parameter values, with even small changes in these causing signicant divergences in results


  1. Agur, Z., Halevi-tobias, K., Kogan, Y., and Shlagman, O. (2016). Employing dynamical computational mod els for personalizing cancer immunotherapy. Expert Opinion on Biological Therapy, 2598(August). 
  2. Anderson, a. R. and Chaplain, M. a. (1998). Contin uous and discrete mathematical models of tumor induced angiogenesis. Bulletin of mathematical biol ogy, 60(5):857–899. 
  3. Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011). Algorithms for Hyper-Parameter Optimization. Ad vances in Neural Information Processing Systems (NIPS), pages 2546–2554. 
  4. Ekins, S., Mestres, J., and Testa, B. (2007). In silico pharmacology for drug discovery : applications to targets and beyond. (December 2006):21–37. 
  5. Kam, Y., Rejniak, K. A., and Anderson, A. R. (2012). Cellular modeling of cancer invasion: Integration of in silico and in vitro approaches. Journal of Cellular Physiology, 227(2):431–438. 
  6. Kather, J. N., Poleszczuk, J., Suarez-Carmona, M., Krisam, J., Charoentong, P., Valous, N. A., Weis, C. A., Tavernar, L., Leiss, F., Herpel, E., Klupp, F., Ulrich, A., Schneider, M., Marx, A., Jäger, D., and Halama, N. (2017). In silico modeling of immunotherapy and stroma-targeting therapies in human colorectal can cer. Cancer Research, 77(22):6442–6452. 
  7. Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., and Balan, G. (2005). Mason: A multiagent simulation environment. Simulation, 81(7):517–527. 
  8. Masad, D. and Kazil, J. (2015). Mesa: An agent-based 
    modeling framework. pages 51–58. 
  9. McDougall, S. R., Anderson, A. R. A., and Chaplain, M.  A. J. (2006). Mathematical modelling of dynamic 
    adaptive tumour-induced angiogenesis: Clinical implications and therapeutic targeting strategies. Jour  nal of Theoretical Biology, 241(3):564–589. 
  10. North, M. J., Collier, N. T., Ozik, J., Tatara, E. R., Macal,  C. M., Bragen, M., and Sydelko, P. (2013). Complex 
    adaptive systems modeling with repast simphony. Complex Adaptive Systems Modeling, 1(1). 
  11. Pickl, M. and Ries, C. H. (2009). Comparison of 3D  and 2D tumor models reveals enhanced HER2 acti 
    vation in 3D associated with an increased response to trastuzumab. Oncogene, 28(3):461–468. 
  12. Romain Reuillon, Mathieu Leclaire, S. R.-C. (2013).  Openmole, a workflow engine specifically tailored 
    for the distributed exploration of simulation models. Future Generation Computer Systems, 29(8):1981 – 
  13. Sinek, J., Frieboes, H., Zheng, X., and Cristini, V.  (2004). Two-dimensional chemotherapy simula 
    tions demonstrate fundamental transport and tumor response limitations involving nanoparticles. 
    Biomedical Microdevices, 6(4):297–309. 
  14. Soodabeh Saeidnia, Azadeh Manayi, M. A. (2013). The  Pros and Cons of the In-silico Pharmaco- toxicology 
    in Drug Discovery and Development. (August). 
  15. Taillandier, P., Gaudou, B., Grignard, A., Huynh, Q.-N.,  Marilleau, N., Caillou, P., Philippon, D., and Drogoul, 
    A. (2019). Building, composing and experimenting  complex spatial models with the GAMA platform. 
    GeoInformatica, 23(2):299–322. 
  16. Wilensky, U. (1999). Netlogo., Center 
    for Connected Learning and Computer-Based  Modeling, Northwestern University, Evanston, IL.