Aickelin, Uwe

Augusto, Douglas A.

Barbosa, Helio J.C.

Figueredo, Grazziela P.

Siebers, Peer-Olaf

Nottingham ePrints

Figueredo, Grazziela P. and Siebers, Peer-Olaf and Augusto, Douglas A. and Barbosa, Helio J.C. and Aickelin, Uwe (2013) The use of agent-based simulation to discover extreme cases in immune-interactions with early-stage cancer scenarios. In: The 12th European Conference on Artificial Life (ECAL 2013), 2-6 Sept 2013, Taormina, Italy. Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/3335/1/Figueredo2013d.pdfCopyright and reuse: The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions.· Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners.· To the extent reasonable and practicable the material made available in Nottingham ePrints has been checked for eligibility before being made available.· Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.· Quotations or similar reproductions must be sufficiently acknowledged.Please see our full end user licence at: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions: The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.ukThe use of Agent-based Simulation to Discover Extreme Cases inImmune-Interactions with Early-Stage Cancer ScenariosGrazziela Figueredo1, Peer-Olaf Siebers1, Douglas Augusto2, Helio Barbosa2 and Uwe Aickelin11Intelligent Modelling and Analysis Research Group, School of Computer Science,The University of Nottingham, NG8 1BB, UK2Laborato´rio Nacional de Computac¸a˜o Cientı´fica-MCT, Petro´polis, Brazilgzf@cs.nott.ac.uk, pos@cs.nott.ac.uk, douglas@lncc.br, hcbm@lncc.br, uxa@cs.nott.ac.ukEarly-stage cancer and its interactions with the immunesystem are still not fully understood. In order to betterunderstand these processes, researchers employ differentmethods. Simulation and in particular, agent-based simu-lation (ABS) have been found useful tools for understand-ing it (Look et al., 1981; Castiglione et al., 1999, 2001;Bonabeau, 2002; Figueredo and Aickelin, 2011; Figueredoet al., 2013a,b).In a previous study (Figueredo et al., 2013b) we have builtan ABS model to study the interplay of immune cells andearly-stage cancer. The model considers interactions be-tween tumour cells and immune effector cells, as well asthe immune-stimulatory and suppressive cytokines IL-2 andTGF-β. IL-2 molecules mediate the immune response to-wards tumour cells. They interfere on the proliferation ofeffector cells according to the number of tumour cells in thesystem. Conversely, TGF-β stimulates tumour growth andsuppresses the immune responses by inhibiting the activa-tion of effector cells and reducing tumour-antigen expres-sion.In order to validate our model, we used a well-establishedmathematical model found in the literature (Arciero et al.,2004). While at average both models do not show a sta-tistical significant difference, some additional trends in theresults of the ABSmodel are observed. As ABS is a stochas-tic simulation method, it was run for multiple times. Insteadof having one solution, as it is the case for a deterministicmathematical model, ABS produces a variety of outcomes.These solutions are usually very similar. In our cases study,however, we could observe some instances which could nothave been observed by using analytical methods (see Fig-ure 1).The use of ABS modelling has therefore led to the dis-covery of additional “rare” patterns, which we would havenot been able to derive by using analytical methods. These“extreme cases” indicate that there might be circumstanceswhere the tumour cells are completely eliminated by theimmune system, without the need of any cancer therapies.We strongly believe that the observed emergent behaviourproduced by stochastic simulation can make a useful con-tribution to assisting immunological research. With theadditional information supplied from the ABS, immunolo-gists can test new hypotheses and further investigate whetherthese extreme cases actually occur in reality and why.Currently, we are working on a methodology for defin-ing experimental conditions that would allow us to observesimilar emergent behaviour in other simulation experimentsrelated to early-stage cancer research. One important aspecthere is to investigate the statistical conditions under whichemergent behaviour starts to appear. The questions we arelooking at are:1. Howmany replications of our stochastic simulation do wehave to run before we can expect to see rare behaviours?2. Is there any regularity in the growth of these rare emergingpatterns?3. What are the factors that need to be considered when pre-dicting the occurrences of emerging patterns (e.g. level ofdynamics in the model)?The patterns obtained in our previous work were a resultof 50 independent runs of the ABS model (Figueredo et al.,2013b). In order to further advance our knowledge regard-ing these patterns we are currently running experiments with10,000 independent runs in order to verify whether there isany regularity in pattern growth. We also intend to validateour results with immunologists. It is hoped that the develop-ment of a methodology to further investigate extreme casescould assist in defining suitable vaccination strategies andthe appropriateness of cancer treatments by the predictionof the possible outcome scenarios and how frequently theytake place.AcknowledgementsThis work was supported by the Advanced Data Anal-ysis Centre (ADAC) at the University of Nottingham.The research leading to these results has also receivedfunding from Conselho Nacional de DesenvolvimentoCientı´fico e Tecnolo´gico (CNPq) and FAPERJ (grant E-26/102.025/2009).(a) Simulation results for tumour cells (b) Simulation results for effector cellsFigure 1: Simulation results: the dashed line (red) shows the mathematical output; the lines in black show exemplar ABS resultsfor 6 runs. As it can be seen, there are some results very close to the mathematical formulation and others presenting morevariability due to the ABS stochastic behaviour. These variations, however, follow the same pathway as the analytical solution.The dashed-dotted line (blue) shows the rare cases determined by the ABS simulations.ReferencesArciero, J. C., Jackson, T. L., and Kirschner, D. E. (2004). A math-ematical model of tumor-immune evasion and siRNA treat-ment. Discrete and continumous dynamical systems - seriesB, 4(1):39–58.Bonabeau, E. (2002). Agent-based modeling: Methods and tech-niques for simulating human systems. In Proceedings of theNational Academy of Sciences of the United States of Amer-ica, volume 99, pages 7280–7287.Castiglione, F., Mannella, G., Motta, S., and Nicosia, G. (1999).A network of cellular automata for the simulation of the im-mune system. Journal of Modern Physics C – Physics andComputer, 10(4):677–686.Castiglione, F., Motta, S., and Nicosia, G. (2001). Pattern recog-nition by primary and secondary response of an artificial im-mune system. Theory in Biosciences, 120(2):93–106.Figueredo, G. P. and Aickelin, U. (2011). Comparing system dy-namics and agent-based simulation for tumour growth andits interactions with effector cells. In Proceedings of the In-ternational Summer Computer Simulation Conference, pages15–22.Figueredo, G. P., Joshi, T. V., Osborne, J. M., Byrne, H. M., andOwen, M. R. (2013a). On-lattice agent-based simulationof populations of cells within the open-source chaste frame-work. Interface Focus, 2(3).Figueredo, G. P., Siebers, P.-O., and Aickelin, U. (2013b). Inves-tigating mathematical models of immuno-interactions withearly-stage cancer under an agent-based modelling perspec-tive. BMC Bioinformatics, 14(6).Look, A. T., Schriber, T. J., Nawrocki, J. F., and Murphy, W. H.(1981). Computer simulation of the cellular immune responseto malignant lymphoid cells: logic of approach, model designand laboratory verification. Immunology, 43(4):677–690.

The use of agent-based simulation to discover extreme cases in immune-interactions with early-stage cancer scenarios

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