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New Challenges to Philosophy of Science pp 151–161Cite as

Simulation and System Understanding

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Part of the book series: The Philosophy of Science in a European Perspective ((PSEP,volume 4))

Abstract

Systems biology is based on a mathematized understanding of molecular biological processes. Because genetic networks are so complex, a system understanding is required that allows for the appropriate modelling of these complex networks and its products up to the whole-cell scale. Since 2000 standardizations in modelling and simulation techniques have been established to support the community-wide endeavors for whole-cell simulations. The development of the Systems Biology Markup Language (SBML), in particular, has helped systems biologists achieve their goal. This paper explores the current developments of modelling and simulation in systems biology. It discusses the question as to whether an appropriate system understanding has been developed yet, or whether advanced software machineries of whole-cell simulations can compensate for the lack of system understanding.

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Notes

  1. 1.

    Christopher Surridge (Ed.), “Nature inside: Computational Biology”, in: Nature 420, 2002, 205-250, here p. 205.

  2. 2.

    Cf. Masaru Tomita, “Whole-cell Simulation: A Grand Challenge of the 21st Century”, in: TRENDS in Biotechnology 19, 6, 2001, pp. 205-210.

  3. 3.

    Tomita 2001, loc. cit., here p. 208.

  4. 4.

    Michael Hucka et al., “The Systems Biology Markup Language (SBML): A Medium for Representation and Exchange of Biochemical Network Models”, in: Bioinformatics 19, 2003, pp. 524–531, here p. 525.

  5. 5.

    Hucka et al. 2003, loc. cit., here p. 524. SBML is organized as a community-wide open standard based on open workshops and an editorial team for updates, which is elected to a 3-year non-renewable term. (Cf. http://sbml.org/).

  6. 6.

    Nature Precedings: The Systems Biology Markup Language (SBML) Collection, (accessed on 6 January 2012). URL: http://precedings.nature.com/collections/sbml. Another description language for modeling is CellML.

  7. 7.

    The example, and its notations, is taken from the initial SBML paper. Cf. Hucka et al., 2003, loc. cit., p. 526 ff.

  8. 8.

    Ibid., p. 528.

  9. 9.

    Ibid., p. 528.

  10. 10.

    Tomita 2001, loc. cit., here p. 210. Cf. Jörg Stelling, et al., “Towards a Virtual Biological Laboratory”, in: Hiroaki Kitano (Ed.), Foundations of Systems Biology. Cambridge (Mass.): The MIT Press 2001, pp. 189-212.

  11. 11.

    Hiroaki Kitano, “Systems Biology: Toward System-level Understanding of Biological Systems”, in: Hiroaki Kitano, 2001, op. cit., pp. 1-38, here p. 6.

  12. 12.

    Alisdair R. Fernie, et al., “Metabolite Profiling: From Diagnostics to Systems Biology”, in: Nature Reviews Molecular Cell Biology 5, 2004, pp. 763-769, here p. 764.

  13. 13.

    Fernie et al., 2004, here p. 765.

  14. 14.

    Ibid., p. 768.

  15. 15.

    Tomita 2001, loc. cit., here p. 210.

  16. 16.

    J. S. Edwards, R. U. Ibarra, B. O. Palsson, “In Silico Predictions of Escherichia coli Metabolic Capabilities are Consistent with Experimental Data”, in: Nature Biotechnology 19, 2001, pp. 125–130, here p. 125.

  17. 17.

    “As a result of the incomplete set of constraints on the metabolic network (that is, kinetic constant constraints and gene expression constraints are not considered), FBA does not yield a unique solution for the flux distribution. Rather, FBA provides a solution space that contains all the possible steady-state flux distributions that satisfy the applied constraints. Subject to the imposed constraints, optimal metabolic flux distributions can be determined from the set of all allowable flux distributions using linear programming (LP).” (Edwards, Ibarra, Palsson, 2001, loc cit., here p. 125).

  18. 18.

    “[…] a crucial and obvious challenge is to determine how these, often disparate and complex, details can explain the cellular process under investigation. The ideal way to meet this challenge is to integrate and organize the data into a predictive model.” (Boris M. Slepchenko et al., “Quantitative Cell Biology with the Virtual Cell”, in: TRENDS in Cell Biology 13, 11, 2003, pp. 570-576, here p. 570). Olaf Wolkenhauer and Ursula Klingmüller have expanded the definition of systems biology given in the first paragraph of this paper by adding “the integration of data, obtained from experiments at various levels and associated with the ‘omics family’ of technologies.” (Olaf Wolkenhauer, Ursula Klingmüller, “Systems Biology: From a Buzzword to a Life Science Approach”, in: BIOforum Europe 4, 2004, pp. 22-23, here p. 22).

  19. 19.

    Tomita 2001, loc. cit., here p. 210.

  20. 20.

    Masaru Tomita, “Towards Computer Aided Design (CAD) of Useful Microorganisms”, in: Bioinformatics 17, 12, 2001a, pp. 1091-1092.

  21. 21.

    Kouichi Takahashi et al., “Computational Challenges in Cell Simulation: A Software Engineering Approach”, in: IEEE Intelligent Systems 5, 2002, pp. 64-71, here p. 64.

  22. 22.

    Cf. E-Cell: Homepage, (accessed on 6 January 2012). URL: http://www.e-cell.org/ecell.

  23. 23.

    Virtual Cell: Homepage at the Center for Cell Analysis & Modeling, (accessed on 6 January 2012). URL: http://www.nrcam.uchc.edu/.

  24. 24.

    Cf. Gabriele Gramelsberger, Johann Feichter, “Modeling the Climate System”, in: Gabriele Gramelsberger, Johann Feichter (Eds.), Climate Change and Policy. The Calculability of Climate Change and the Challenge of Uncertainty. Heidelberg: Springer 2011, p. 44 ff.

  25. 25.

    A DAE combines one ordinary differential equation (ODE) for each enzyme reaction, a stochimetric matrix, and algebraic equations for constraining the system.

  26. 26.

    Cf. Takahashi et al., 2002, loc. cit., p. 66 ff.

  27. 27.

    Ibid., p. 64.

  28. 28.

    Cf. Ibid., p. 64 ff.

  29. 29.

    Leslie M. Loew, et al., “The Virtual Cell Project”, in: Systems Biomedicine, 2010, pp. 273-288, here p. 274.

  30. 30.

    Loew, et al., 2010, loc. cit., here p. 274.

  31. 31.

    Kitano 2001, op cit., here p. xiii referring to Norbert Wiener: Cybernetics or Control and Communication in the Animal and the Machine. New York: John Wiley & Sons 1948 and Ludwig von Bertalanffy, General System Theory. Foundations, Development, Applications. New York: Braziller 1968.

  32. 32.

    von Bertalanffy, 1968, op. cit., here p. 55.

  33. 33.

    Hiroaki Kitano, “Computational Systems Biology”, in: Nature 420, 2002, pp. 206-210, here p. 54.

  34. 34.

    Kitano, 2002, loc cit., here p. 206.

  35. 35.

    Cf. Ulrich Krohs, Georg Toepfer (Eds.), Philosophie der Biologie. Frankfurt: Suhrkamp 2005.

  36. 36.

    Cf. Ludwig von Bertalanffy, Theoretische Biologie. Berlin: Bornträger 1932; Ludwig von Bertalanffy, Biophysik des Fließgleichgewichts. Berlin: Akademie Verlag 1952; Wiener, 1948, op. cit.; Walter B. Cannon, “Organization for Physiological Homeostasis”, in: Physiological Review 9, 1929, p. 397; Walter B. Cannon, The Wisdom of the Body. New York: Norton 1932.

  37. 37.

    von Bertalanffy 1986, op. cit., here p. 163.

  38. 38.

    Ibid., p. 158.

  39. 39.

    Ibid., p. 159.

  40. 40.

    Ibid., pp. 67 and 55.

  41. 41.

    Herman H. Goldstine, John von Neumann, “Planning and Coding Problems for an Electronic Computing Instrument” (1947), Part II, vol. 1, in: John von Neumann, Collected Work, vol. V: Design of Computers, Theory of Automata and Numerical Analysis, Oxford: Pergamon Press 1963, pp. 80-151, here p. 100. Cf. Gabriele Gramelsberger, “From Computation with Experiments to Experiments with Computation”, in: Gabriele Gramelsberger (Ed.), From Science to Computational Sciences. Studies in the History of Computing and its Influence on Today’s Sciences. Zurich: Diaphanes, pp. 131-142.

  42. 42.

    Goldstine, Neumann, 1947, loc. cit., here pp. 81-82.

  43. 43.

    Interestingly, the object-oriented programming paradigm – introduced in 1967 with Simula 67 for physical simulations – was advanced for the programming language C++, which originated in the telecommunication industry (Bell labs) in response to the demand for more complex structures for organizing network traffic. Cf. Terry Shinn, “When is Simulation a Research-Technology? Practices, Markets and Lingua Franca”, in: Johannes Lenhard, Günter Küppers, Terry Shinn (Eds.), Simulation: Pragmatic Construction of Reality. Dordrecht: Springer, 2006, pp. 187-203.

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  1. FU Institute of Philosophy, Freie Universität Berlin, Habelschwerdter Allee 30, D-14195, Berlin, Germany

    Gabriele Gramelsberger

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  1. Gabriele Gramelsberger
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Correspondence to Gabriele Gramelsberger .

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Editors and Affiliations

  1. Aarhus University, Ny Munkegade 1520, Aarhus, 8000, Denmark

    Hanne Andersen

  2. Inst. History & Foundations of Science, Utrecht University, Budapestlaan 6, Utrecht, 3584 CD, Netherlands

    Dennis Dieks

  3. , Faculty of Humanities, University of A Coruña, Dr. Vazquez Cabrera street, w/n, Ferrol, 15.403, Spain

    Wenceslao J. Gonzalez

  4. , Philosophy School of Social Science, University of Manchester, Manchester, M13 9PL, United Kingdom

    Thomas Uebel

  5. Centre Artificial Intelligence (CENTRA), Department of Computer Science, New University of Lisbon, Monte de Caparica, Lisbon, 2829-516, Portugal

    Gregory Wheeler

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Gramelsberger, G. (2013). Simulation and System Understanding. In: Andersen, H., Dieks, D., Gonzalez, W., Uebel, T., Wheeler, G. (eds) New Challenges to Philosophy of Science. The Philosophy of Science in a European Perspective, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5845-2_13

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