Click here to download

Computer simulations without simulative programs in executable cell biology. Hypothesis discovery and justification
Journal Title: PARADIGMI 
Author/s: Nicola Angius 
Year:  2015 Issue: Language: English 
Pages:  16 Pg. 67-82 FullText PDF:  118 KB
DOI:  10.3280/PARA2015-003005
(DOI is like a bar code for intellectual property: to have more infomation:  clicca qui   and here 

The application of formal methods to the examination of reactive programs simulating cell systems’ behaviours in current computational biology is taken to shed new light on the simulative approaches in Artificial Intelligence and Artificial Life. First, it is underlined how reactive programs simulating many cell systems’ behaviours are more profitably examined by means of executable models of the simulating program’s executions. Those models turn out to be representations of both the simulating reactive program and of the simulated cell system. Secondly, it is highlighted how discovery processes of significant regular behaviours of the simulated system are carried out performing algorithmic verifications on the formal model representing the biological phenomena of interest. Finally, a distinctive methodological trait of current computational biology is recognized in that the advanced model-based hypotheses are not corroborated or falsified by testing the simulative program, which is not even encoded, but rather by performing wet experiments aiming at the observation of behaviours corresponding to paths in the model either satisfying or violating the hypotheses under evaluation.
Keywords: Artificial life, Computer simulation, Executable biology, Model-Based reasoning, Model checking, Philosophy of computer science

  1. Ammann P. and Offutt J. (2008). Introduction to software testing. Cambridge: Cambridge University Press.
  2. Angius N. (2013). Model-based abductive reasoning in automated software testing. Logic Journal of IGPL, 21, 6: 931-942.
  3. Angius N. (2014). The problem of justification of empirical hypotheses in software testing. Philosophy & Technology, 27, 3: 423-439.
  4. Baier C. and Katoen J.P. (2008). Principles of model checking. Cambridge (MA): MIT Press.
  5. Bernardo M., de Vink E., Di Pierro A., and Wiklicky H., eds. (2013). Formal methods for dynamical systems. 13th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, Lecture Notes in Computer Science, 7938. Berlin: Springer., DOI: 10.1007/978-3-642-38874-3
  6. Brim L., Češka M. and Šafránek D. (2013). Model checking of biological systems. Formal Methods for Dynamical Systems, Berlin-Heidelberg: Springer.
  7. Callahan J., Schneider F. and Easterbrook S. (1996). Automated software testing using model-checking. Proceedings SPIN workshop, 353: 118-127.
  8. Clarke E. M., Grumberg O. and Peled D. (1999). Model checking. Cambridge (MA): MIT Press.
  9. Cordeschi R. (2002). The discovery of the artificial: behavior, mind and machines before and beyond cybernetics. Dordrecht: Kluwer., DOI: 10.1007/978-94-015-9870-5
  10. Datteri E. and Tamburrini G. (2007). Biorobotic experiments for the discovery of biological mechanisms. Philosophy of Science, 74, 3: 409-430.
  11. Dijkstra E.W. (1970). Notes on structured programming. Technical Report 70-WSK-03, Department of Mathematics, The Netherlands: Technical University Einhdoven.
  12. Fisher J. and Henzinger T.A. (2007). Executable cell biology. Nature Biotechnology, 25, 11: 1239-1249.
  13. Fisher J., Harel D. and Henzinger T.A. (2011). Biology as reactivity. Communications of the ACM, 54, 10: 72-82.
  14. Kitano H. (2002). Computational systems biology. Nature, 420, 6912: 206-210.
  15. Klipp E., Liebermeister W., Wierling C., Kowald A., Lehrach H. and Herwig, R. (2013). Systems biology. Weinheim: John Wiley & Sons.
  16. Kröger F. and Merz S. (2008). Temporal logic and state systems. Berlin: Springer Science & Business Media.
  17. Magnani, L. (2004). Model-based and manipulative abduction in science. Foundations of Science, 9, 3: 219-247.
  18. Magnani, L. Nersessian, N. and Thagard P. (1999). Model-based reasoning in scientific discovery. Dordrecht: Kluwer.
  19. Monin J.F. (2012). Understanding formal methods. Berlin: Springer Science & Business Media.
  20. Newell A. and Simon H.A. (1972). Human problem solving. Englewood Cliffs (NJ): Prentice-Hall.
  21. Rosenblueth A. and Wiener N. (1945). The role of models in science. Philosophy of Science, 12, 4: 316-321.
  22. Rosenblueth A., Wiener N. and Bigelow, J. (1943). Behavior, purpose and teleology. Philosophy of Science, 10, 1: 18-24.
  23. Schaub M.A., Henzinger T.A. and Fisher J. (2007). Qualitative networks: A symbolic approach to analyze biological signaling networks. BMC Systems Biology, 1, 1: 4.
  24. Shapiro S. (1997). Splitting the difference: the historical necessity of synthesis in software engineering. Annals of the History of Computing, IEEE International Conferences, 19, 1: 20-54.
  25. Swoyer C. (1991). Structural representation and surrogative reasoning. Synthese, 87, 3: 449-508.
  26. Tamburrini G. and Datteri E. (2005). Machine experiments and theoretical modelling: from cybernetic methodology to neuro-robotics. Minds and Machines, 15, 3-4: 335-358.
  27. Turner R. (2013). The philosophy of computer science. The Stanford Encyclopedia of Philosophy. Stanford: Stanford University.
  28. Van Leeuwen J. (1990). Handbook of theoretical computer science. Volume B: formal models and semantics. Cambridge (MA): MIT Press.
  29. Vardi M.Y. and Wolper P. (1986), An automata-theoretic approach to automatic program verification (preliminary report), 1st Annual Symposium on Logic in Computer Science (LICS), IEEE: 332-344.

Nicola Angius, in "PARADIGMI" 3/2015, pp. 67-82, DOI:10.3280/PARA2015-003005


FrancoAngeli is a member of Publishers International Linking Association a not for profit orgasnization wich runs the CrossRef service, enabing links to and from online scholarly content