Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths

English version

Autoři: James A. R. Marshall ^aff001; Andreagiovanni Reina ^aff001; Thomas Bose ^aff001
Působiště autorů: Department of Computer Science, University of Sheffield, Sheffield, United Kingdom ^aff001
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222906

Souhrn

Collective behaviour is of fundamental importance in the life sciences, where it appears at levels of biological complexity from single cells to superorganisms, in demography and the social sciences, where it describes the behaviour of populations, and in the physical and engineering sciences, where it describes physical phenomena and can be used to design distributed systems. Reasoning about collective behaviour is inherently difficult, as the non-linear interactions between individuals give rise to complex emergent dynamics. Mathematical techniques have been developed to analyse systematically collective behaviour in such systems, yet these frequently require extensive formal training and technical ability to apply. Even for those with the requisite training and ability, analysis using these techniques can be laborious, time-consuming and error-prone. Together these difficulties raise a barrier-to-entry for practitioners wishing to analyse models of collective behaviour. However, rigorous modelling of collective behaviour is required to make progress in understanding and applying it. Here we present an accessible tool which aims to automate the process of modelling and analysing collective behaviour, as far as possible. We focus our attention on the general class of systems described by reaction kinetics, involving interactions between components that change state as a result, as these are easily understood and extracted from data by natural, physical and social scientists, and correspond to algorithms for component-level controllers in engineering applications. By providing simple automated access to advanced mathematical techniques from statistical physics, nonlinear dynamical systems analysis, and computational simulation, we hope to advance standards in modelling collective behaviour. At the same time, by providing expert users with access to the results of automated analyses, sophisticated investigations that could take significant effort are substantially facilitated. Our tool can be accessed online without installing software, uses a simple programmatic interface, and provides interactive graphical plots for users to develop understanding of their models.

Klíčová slova:

Simulation and modeling – Honey bees – Collective human behavior – Dynamical systems – Collective animal behavior – Software tools – Reactants – Reaction kinetics

Zdroje

1. Tyson JJ, Chen KC, Novak B. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Current Opinion in Cell Biology. 2003;15(2):221–231. doi: 10.1016/s0955-0674(03)00017-6 12648679

2. Seeley TD, Visscher PK, Schlegel T, Hogan PM, Franks NR, Marshall JAR. Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science. 2012;335(6064):108–111. doi: 10.1126/science.1210361 22157081

3. Murray JD. Mathematical Biology I: An Introduction. 3rd ed. Springer-Verlag; 2002.

4. Yildiz E, Ozdaglar A, Acemoglu D, Saberi A, Scaglione A. Binary opinion dynamics with stubborn agents. ACM Transactions on Economics and Computation (TEAC). 2013;1(4):19.

5. Israel G. La Mathématisation du Réel. Seuil; 1996.

6. Marshall JAR, Franks NR. Computer modeling in behavioral and evolutionary ecology: whys and wherefores. In: Modeling Biology: Structures, Behavior, Evolution. The Vienna Series in Theoretical Biology. MIT Press; 2007. p. 335–353.

7. van Rossum G, et al. Python 3; 2008. Available from: https://www.python.org/3/reference/; accessed on 2019-06-12 [cited 2019-03-07].

8. Various. Project Jupyter;. Available from: https://jupyter.org [cited 2019-06-12].

9. Various. Binder;. Available from: https://mybinder.org [cited 2019-06-12].

10. Marshall, James A R and Reina, Andreagiovanni and Bose, Thomas. MuMoT online manual; 2019. Available from: https://mumot.readthedocs.io/en/latest/getting_started.html [cited 2019-06-28].

11. Leff A, Rayfield JT. Web-application development using the model/view/controller design pattern. In: Proceedings of the Fifth IEEE International Enterprise Distributed Object Computing Conference. IEEE; 2001. p. 118–127.

12. van Rossum G, Warsaw B, Coghlan N. PEP 8: style guide for Python code. Python.org; 2001. Available from: https://www.python.org/dev/peps/pep-0008/.

13. Lee BD. Ten simple rules for documenting scientific software. PLoS Computational Biology. 2018;14(12):e1006561. doi: 10.1371/journal.pcbi.1006561 30571677

14. Pais D, Hogan PM, Schlegel T, Franks NR, Leonard NE, Marshall JAR. A mechanism for value-sensitive decision-making. PLoS one. 2013;8(9):e73216. doi: 10.1371/journal.pone.0073216 24023835

15. Galla T. Independence and interdependence in the nest-site choice by honeybee swarms: agent-based models, analytical approaches and pattern formation. Journal of Theoretical Biology. 2010;262(1):186–196. doi: 10.1016/j.jtbi.2009.09.007 19761778

16. Jones E, Oliphant T, Peterson P, et al. SciPy: Open source scientific tools for Python; 2001–. Available from: http://www.scipy.org/.

17. Clewley, R H and Sherwood, W E and LaMar, M D and Guckenheimer, J M. PyDSTool: a software environment for dynamical systems modeling; 2007. Available from: https://pydstool.github.io/PyDSTool/ [cited 2019-06-12].

18. van Kampen NG. Stochastic Processes in Physics and Chemistry: Third Edition. Amsterdam: North-Holland; 2007.

19. Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, Rocklin M, et al. SymPy: symbolic computing in Python. PeerJ Computer Science. 2017;3:e103. doi: 10.7717/peerj-cs.103

20. Gillespie DT, Hellander A, Petzold LR. Perspective: Stochastic algorithms for chemical kinetics. The Journal of Chemical Physics. 2013;138(17):170901. doi: 10.1063/1.4801941 23656106

21. Gillespie DT. A general method for numerically simulating stochastic time evolution of coupled chemical reactions. Journal of Computational Physics. 1976;22:403–434. doi: 10.1016/0021-9991(76)90041-3

22. Hagberg AA, Schult DA, Swart PJ. Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conference (SciPy 2008). SciPy; 2008.

23. Erdös P, Rényi A. On random graphs I. Publicationes Mathematicae (Debrecen). 1959;6:290–297.

24. Barabási AL, Albert R. Emergence of scaling in random networks. Science. 1999;286(5439):509–512. doi: 10.1126/science.286.5439.509 10521342

25. Penrose M. Random Geometric Graphs. Oxford studies in probability. Oxford University Press; 2003.

26. Reina A, Valentini G, Fernández-Oto C, Dorigo M, Trianni V. A design pattern for decentralised decision making. PLoS ONE. 2015;10(10):e0140950. doi: 10.1371/journal.pone.0140950 26496359

27. Marshall, James A R and Reina, Andreagiovanni and Bose, Thomas. MuMoT 1.0.0-release. 2019.

28. Adalsteinsson D, McMillen D, Elston TC. Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks. BMC Bioinformatics. 2004;5(1):24. doi: 10.1186/1471-2105-5-24 15113411

29. Ramsey S, Orrell D, Bolouri H. Dizzy: stochastic simulation of large-scale genetic regulatory networks. Journal of Bioinformatics and Computational Biology. 2005;3(02):415–436. doi: 10.1142/S0219720005001132 15852513

30. Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U. In: Computational Modeling of Biochemical Networks Using COPASI. Totowa, NJ: Humana Press; 2009. p. 17–59.

31. Mauch S, Stalzer M. Efficient formulations for exact stochastic simulation of chemical systems. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2011;8(1):27–35. doi: 10.1109/TCBB.2009.47 21071794

32. Thomas P, Matuschek H, Grima R. Intrinsic Noise Analyzer: A software package for the exploration of stochastic biochemical kinetics using the system size expansion. PLoS ONE. 2012;7(6):e38518. doi: 10.1371/journal.pone.0038518 22723865

33. Abel JH, Drawert B, Hellander A, Petzold LR. GillesPy: A Python package for stochastic model building and simulation. IEEE Life Sciences Letters. 2016;2(3):35–38. doi: 10.1109/LLS.2017.2652448 28630888

34. Sanft KR, Wu S, Roh M, Fu J, Lim RK, Petzold LR. StochKit2: software for discrete stochastic simulation of biochemical systems with events. Bioinformatics. 2011;27(17):2457–2458. doi: 10.1093/bioinformatics/btr401 21727139

35. Maarleveld TR, Olivier BG, Bruggeman FJ. StochPy: A comprehensive, user-friendly tool for simulating stochastic biological processes. PLoS ONE. 2013;8(11):e79345. doi: 10.1371/journal.pone.0079345 24260203

36. Drawert B, Hellander A, Bales B, Banerjee D, Bellesia G, Daigle BJ, et al. Stochastic Simulation Service: Bridging the gap between the computational expert and the biologist. PLOS Computational Biology. 2016;12(12):e1005220. doi: 10.1371/journal.pcbi.1005220 27930676

37. Dhooge A, Govaerts W, Kuznetsov YA. MATCONT: A Matlab package for numerical bifurcation analysis of ODEs. SIGSAM Bull. 2004;38(1):21–22. doi: 10.1145/980175.980184

38. Beer RD. Dynamica: a Mathematica package for the analysis of smooth dynamical systems; 2018. Available from: http://mypage.iu.edu/~rdbeer/.

39. Wilensky U. NetLogo. Northwestern University, Evanston, IL: Center for Connected Learning and Computer-Based Modeling; 1999. Available from: http://ccl.northwestern.edu/netlogo/.

40. Kiran M, Richmond P, Holcombe M, Chin LS, Worth D, Greenough C. FLAME: Simulating large populations of agents on parallel hardware architectures. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1. AAMAS’10. Richland, SC: IFAAMAS; 2010. p. 1633–1636.

41. Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G. MASON: A multiagent simulation environment. SIMULATION. 2005;81(7):517–527. doi: 10.1177/0037549705058073

42. Sayama H. PyCX: a Python-based simulation code repository for complex systems education. Complex Adaptive Systems Modeling. 2013;1(2).

43. Medley JK, Choi K, König M, Smith L, Gu S, Hellerstein J, et al. Tellurium notebooks–An environment for reproducible dynamical modeling in systems biology. PLOS Computational Biology. 2018;14(6):e1006220. doi: 10.1371/journal.pcbi.1006220 29906293

44. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics. 2003;19(4):524–531. doi: 10.1093/bioinformatics/btg015 12611808

Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths

Souhrn

Klíčová slova:

Zdroje

PLOS One

Aktuální možnosti diagnostiky a léčby litiáz