Biological Immunity and Software Resilience: Two Faces of the Same Coin?
Biological systems modeling and simulation is an important stream of research for both biologists and computer scientists. On the one hand, biologists ask for systemic approaches to model biological systems to the purpose of simulating them on a computer and predicting their behavior, which is resilient by nature. This would limit as much as possible the number of experiments in laboratory, which are known to be expensive, often impracticable, hardly reproducible, and slow. On the other hand, beyond facing the development challenges related to the achievement of the resilience to be offered by biological system simulators, computer scientists ask for a well-established engineering methodology to systematically deal with the peculiarities of software resilient systems, in their more general sense. In line with this, in this paper we report on our preliminary study of immune systems (a particular kind of biological systems) aimed at devising software abstractions that enable the systematic modeling of resilient systems and their automated treatment. We propose a bio-inspired concept architecture for structuring resilient systems based on the Akka implementation of the widely-known Actor Model, which supports scalable and resilient concurrent computation. To the best of our knowledge, this work represents a first preliminary step towards devising a bio-inspired paradigm for engineering the development of resilient software systems.
KeywordsActor Model Undesired Behavior Resilient System Concept Architecture Asynchronous Message
- 1.Bio-pepa: A framework for the modelling and analysis of biological systems. Theoretical Computer Science, 410(33–34), 3065–3084 (2009)Google Scholar
- 3.Chandra, A.: Synergy between biology and systems resilience, master’s thesis, missouri university of science and technology (2010)Google Scholar
- 9.Faeder, J., Blinov, M., Hlavacek, W.: Rule-based modeling of biochemical systems with bionetgen. In: Maly, I.V. (ed.) Systems Biology, volume 500 of Methods in Molecular Biology, pp. 113–167. Humana Press (2009)Google Scholar
- 15.Hewitt, C., Bishop, P., Steiger, R.: A universal modular ACTOR formalism for artificial intelligence. In: Proceedings of the 3rd International Joint Conference on Artificial Intelligence. pp. 235–245. Standford, CA, August 1973Google Scholar
- 16.Hofmeyr, S.A.: An interpretative introduction to the immune system. In: Design Principles for the Immune System and Other Distributed Autonomous Systems, pp. 3–26. Oxford University Press (2000)Google Scholar
- 17.Höller, A., Kajtazovic, N., Preschern, C., Kreiner, C.: Formal fault tolerance analysis of algorithms for redundant systems in early design stages. In: Majzik, I., Vieira, M. (eds.) SERENE 2014. LNCS, vol. 8785, pp. 71–85. Springer, Heidelberg (2014) Google Scholar
- 18.Majzik, I., Vieira, M. (eds.): SERENE 2014. LNCS, vol. 8785. Springer, Heidelberg (2014)Google Scholar
- 19.Janeway Jr., C., Travers, P., Walport, M., et al.: Immunobiology: The Immune System in Health and Disease, 5th edn. Garland Science, USA (2013) Google Scholar
- 22.Laibinis, L., Klionskiy, D., Troubitsyna, E., Dorokhov, A., Lilius, J., Kupriyanov, M.: Modelling resilience of data processing capabilities of CPS. In: Majzik, I., Vieira, M. (eds.) SERENE 2014. LNCS, vol. 8785, pp. 55–70. Springer, Heidelberg (2014) Google Scholar
- 27.Sackmann, A., Heiner, M., Koch, I.: Application of petri net based analysis techniques to signal transduction pathways, BMC Bioinform. 7–482 (2006)Google Scholar
- 29.Srivastavawz, R., Youw, L., Summersy, J., Yin, J.: on stochastic vs. deterministic modeling of intracellular viral kinetics (2002)Google Scholar
- 30.Wang, R.-S., Saadatpour, A., Albert, R.: Boolean modeling in systems biology: an overview of methodology and applications. Phys. Biol. 9(5) (2012)Google Scholar
- 31.Watanabe, Y., Ishiguro, A., Shirai, Y., Uchikawa, Y.: Emergent construction of behavior arbitration mechanism based on the immune system. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, pp. 481–486 (1998)Google Scholar