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On the Application of the Complexity Zeta Function to Quantify Complexity in Bioengineering Systems

Published: 17 October 2019 Publication History

Abstract

Emergent properties are an issue that effects all aspects of engineering. When behaviors and properties arise in a system, that were not intended, they can have a deleterious effect on system performance. Emergent properties are often the root cause of engineering failures that are otherwise indeterminate. This is particularly true for bioengineering. In bioengineering the engineered system must integrate with biological systems. This creates a complex system of systems that is likely to generate emergent properties. Currently the literature is inadequate on the topic of predicting emergence or even quantifying the complexity of a system. In this paper a methodology for quantifying complexity in a system is posited, thus supporting the ability to predict the probability of emergence.

References

[1]
Giammarco, K. (2017, June). Practical modeling concepts for engineering emergence in systems of systems. In 2017 12th System of Systems Engineering Conference (SoSE) (pp. 1--6). IEEE.
[2]
Li, M. and Vitányi, P., 2013. An introduction to Kolmogorov complexity and its applications. Springer Science & Business Media.
[3]
Tenaillon, O., Silander, O.K., Uzan, J.P. and Chao, L., 2007. Quantifying organismal complexity using a population genetic approach. PloS one, 2(2), p.e217.
[4]
Zumwalt, A., 2005. A new method for quantifying the complexity of muscle attachment sites. The Anatomical Record Part B: The New Anatomist: An Official Publication of the American Association of Anatomists, 286(1), pp.21--28.
[5]
Bargaje, R., Trachana, K., Shelton, M.N., McGinnis, C.S., Zhou, J.X., Chadick, C., Cook, S., Cavanaugh, C., Huang, S. and Hood, L., 2017. Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells. Proceedings of the National Academy of Sciences, 114(9), pp. 2271--2276.
[6]
Li, C. and Wang, J., 2013. Quantifying cell fate decisions for differentiation and reprogramming of a human stem cell network: landscape and biological paths. PLoS computational biology, 9(8), p.e1003165.
[7]
Hauert, S., & Bhatia, S. N. (2014). Mechanisms of cooperation in cancer nanomedicine: towards systems nanotechnology. Trends in biotechnology, 32(9), 448--455.
[8]
Demis, E. C., Aguilera, R., Sillin, H. O., Scharnhorst, K., Sandouk, E. J., Aono, M., ... & Gimzewski, J. K. (2015). Atomic switch networks---nanoarchitectonic design of a complex system for natural computing. Nanotechnology, 26(20), 204003.
[9]
Weisbuch, G., 2018. Complex systems dynamics. CRC Press.
[10]
Shortell, T.M. ed., 2015. INCOSE Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities. John Wiley & Sons.
[11]
Mittal, S. and Rainey, L., 2015, July. Harnessing emergence: The control and design of emergent behavior in system of systems engineering. In Proceedings of the Conference on Summer Computer Simulation (pp. 1--10). Society for Computer Simulation International.
[12]
Rainey, L.B. and Jamshidi, M., 2018. Engineering Emergence: A Modeling and Simulation Approach. CRC Press.
[13]
Karabalin, R.B., Cross, M.C. and Roukes, M.L., 2009. Nonlinear dynamics and chaos in two coupled nanomechanical resonators. Physical Review B, 79(16), p.165309.
[14]
Chialvo, D.R., 2010. Emergent complex neural dynamics. Nature physics, 6(10), p.744.
[15]
Roberts, A.J., 2014. Model emergent dynamics in complex systems (Vol. 20). SIAM.
[16]
Strogatz, S.H., 2018. Nonlinear Dynamics and Chaos with Student Solutions Manual: With Applications to Physics, Biology, Chemistry, and Engineering. CRC Press.
[17]
Mittal, S. and Martín, J.L.R., 2018. Netcentric system of systems engineering with DEVS unified process. CRC Press.
[18]
Mittal, S., Diallo, S. and Tolk, A., 2018. Emergent behavior in complex systems engineering: a modeling and simulation approach. John Wiley & Sons.
[19]
Tolk, A., Diallo, S. and Mittal, S., 2018. Complex systems engineering and the challenge of emergence. Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach, pp.79--97.
[20]
Szabo, C. and Teo, Y.M., 2015. Formalization of weak emergence in multiagent systems. ACM Transactions on Modeling and Computer Simulation (TOMACS), 26(1), p.6.
[21]
Kopetz, H., Höftberger, O., Frömel, B., Brancati, F. and Bondavalli, A., 2015, May. Towards an understanding of emergence in systems-of-systems. In 2015 10th System of Systems Engineering Conference (SoSE) (pp. 214--219). IEEE
[22]
Maier, M.W., 2015. The role of modeling and simulation in system of systems development. In Modeling and simulation support for system of systems engineering applications (pp. 11--41). Wiley.
[23]
Dehaene, S., Lau, H., & Kouider, S. (2017). What is consciousness, and could machines have it? Science, 358(6362), 486--492.
[24]
Fingelkurts, A. A., Fingelkurts, A. A., & Neves, C. F. (2013). Consciousness as a phenomenon in the operational architectonics of brain organization: criticality and self-organization considerations. Chaos, Solitons & Fractals, 55, 13--31.
[25]
Hameroff, S. R., & Penrose, R. (2017). CONSCIOUSNESS IN THE UNIVERSE AN UPDATED REVIEW OF THE "ORCH OR" THEORY. In Biophysics of Consciousness: A Foundational Approach (pp. 517--599).
[26]
Lagercrantz, H., & Padilla, N. (2016). 1 The emergence of consciousness. Neurotechnology and Direct Brain Communication: New insights and responsibilities concerning speechless but communicative subjects, 7.
[27]
Tolk, A., 2019. Limitations and usefulness of computer simulations for complex adaptive systems research. In Summer of Simulation (pp. 77--96). Springer, Cham.
[28]
Mittal, S. and Cane, S.A., 2016, April. Contextualizing emergent behavior in system of systems engineering using gap analysis. In Proceedings of the Modeling and Simulation of Complexity in Intelligent, Adaptive and Autonomous Systems 2016 (MSCIAAS 2016) and Space Simulation for Planetary Space Exploration (SPACE 2016) (p. 12). Society for Computer Simulation International.
[29]
Roberts, A.J., 2014. Model emergent dynamics in complex systems (Vol. 20). SIAM.
[30]
Kremling, A., 2013. Systems biology: mathematical modeling and model analysis. Chapman and Hall/CRC.
[31]
Luo, C. and Wang, X., 2013. Chaos in the fractional-order complex Lorenz system and its synchronization. Nonlinear Dynamics, 71(1-2), pp.241--257.
[32]
Chen, Y. and Yang, Q., 2014. Dynamics of a hyperchaotic Lorenz-type system. Nonlinear Dynamics, 77(3), pp.569--581.
[33]
Hannon, B. and Ruth, M., 2014. Modeling dynamic biological systems. In Modeling dynamic biological systems (pp. 3--28). Springer, Cham.
[34]
Combariza, M.E., Yu, X., Nesbitt, W.S., Mitchell, A. and Tovar-Lopez, F.J., 2015. Nonlinear dynamic modelling of platelet aggregation via microfluidic devices. IEEE Transactions on Biomedical Engineering, 62(7), pp.1718--1727.
[35]
Genot, A.J., Baccouche, A., Sieskind, R., Aubert-Kato, N., Bredeche, N., Bartolo, J.F., Taly, V., Fujii, T. and Rondelez, Y., 2016. High-resolution mapping of bifurcations in nonlinear biochemical circuits. Nature chemistry, 8(8), p.760.
[36]
Xu, T. and Younis, M.I., 2016. Nonlinear dynamics of carbon nanotubes under large electrostatic force. Journal of Computational and Nonlinear Dynamics, 11(2), p.021009.
[37]
Matheny, M.H., Grau, M., Villanueva, L.G., Karabalin, R.B., Cross, M.C. and Roukes, M.L., 2014. Phase synchronization of two anharmonic nanomechanical oscillators. Physical review letters, 112(1), p.014101.
[38]
Prokopenko, M. ed., 2013. Guided self-organization: Inception (Vol. 9). Springer Science & Business Media.
[39]
Rainey, L.B. and Tolk, A., 2015. Overview and Introduction to Modeling and Simulation Support for System of Systems Engineering Applications. Modeling and Simulation Support for System of Systems Engineering Applications, p.1.
[40]
Palsson, B., 2015. Systems biology. Cambridge university press.
[41]
Flumerfelt, S., Schwartz, K.G., Mavris, D. and Briceno, S. eds., 2019. Complex Systems Engineering: Theory and Practice.
[42]
Fosse, E. and Delp, C.L., 2013, March. Systems engineering interfaces: A model based approach. In 2013 IEEE Aerospace Conference (pp. 1--8). IEEE.
[43]
Hirshorn, S.R., Voss, L.D. and Bromley, L.K., 2017. Nasa systems engineering handbook.
[44]
Hilborn, R. C. (2000). Chaos and nonlinear dynamics: an introduction for scientists and engineers. Oxford University Press on Demand.
[45]
Viana, M., 2014. Lectures on Lyapunov exponents (Vol. 145). Cambridge University Press.
[46]
Shen, C., Yu, S., Lü, J. and Chen, G., 2014. Designing hyperchaotic systems with any desired number of positive Lyapunov exponents via a simple model. IEEE Transactions on Circuits and Systems I: Regular Papers, 61(8), pp. 2380--2389.
[47]
Maldacena, J., Shenker, S.H. and Stanford, D., 2016. A bound on chaos. Journal of High Energy Physics, 2016(8), p. 106.

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  • (2020)On the Application of the Complexity Zeta Function to Modelling Complexity and Emergence in Neuro-Engineering2020 10th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC47524.2020.9031249(0431-0436)Online publication date: Jan-2020

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    cover image ACM Other conferences
    AIAM 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing
    October 2019
    418 pages
    ISBN:9781450372022
    DOI:10.1145/3358331
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    Published: 17 October 2019

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    Author Tags

    1. bioengineering
    2. emergence
    3. nanotechnology
    4. nonlinear dynamics

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    • (2020)On the Application of the Complexity Zeta Function to Modelling Complexity and Emergence in Neuro-Engineering2020 10th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC47524.2020.9031249(0431-0436)Online publication date: Jan-2020

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