Tobias Helms
Skip Abstract Section
Skip Supplemental Material SectionAbstract
The domain-specific modeling and simulation language ML-Rules is aimed at facilitating the description of cell biological systems at different levels of organization. Model states are chemical solutions that consist of dynamically nested, attributed entities. The model dynamics are described by rules that are constrained by arbitrary functions, which can operate on the entities’ attributes, (nested) solutions, and the reaction kinetics. Thus, ML-Rules supports an expressive hierarchical, variable structure modeling of cell biological systems. The formal syntax and semantics of ML-Rules show that it is firmly rooted in continuous-time Markov chains. In addition to a generic stochastic simulation algorithm for ML-Rules, we introduce several specialized algorithms that are able to handle subclasses of ML-Rules more efficiently. The algorithms are compared in a performance study, leading to conclusions on the relation between expressive power and computational complexity of rule-based modeling languages.
Supplemental Material
Available for Download
zip
tomacs-2015-0116.r1_supplementary.zip (11.9 MB)
supplementary zip file
zip
helms.zip (230.8 KB)
Supplemental movie, appendix, image and software files for, Semantics and Efficient Simulation Algorithms of an Expressive Multilevel Modeling Language
- J. Banks and L. Chwif. 2011. Warnings about simulation. Journal of Simulation 5, 4, 279--291. Google Scholar
Cross Ref
- Fernando J. Barros. 1997. Modeling formalisms for dynamic structure systems. ACM Transactions on Modeling and Computer Simulation 7, 4, 501--515. DOI:http://dx.doi.org/10.1145/268403.268423 Google ScholarDigital Library
- Marco Beccuti, Mary Ann Blätke, Martin Falk, Simon Hardy, Monika Heiner, Carsten Maus, Sebastian Nähring, and Christian Rohr. 2015. Dictyostelium discoideum: Aggregation and synchronisation of amoebas in time and space. Dagstuhl Reports: Multiscale Spatial Computational Systems Biology (Dagstuhl Seminar 14481) 4, 11, 195--214. DOI:http://dx.doi.org/10.4230/DagRep.4.11.138Google Scholar
- Arne T. Bittig, Fiete Haack, Carsten Maus, and Adelinde M. Uhrmacher. 2011. Adapting rule-based model descriptions for simulating in continuous and hybrid space. In Proceedings of the 9th International Conference on Computational Methods in Systems Biology (CMSB’11). 161--170. Google ScholarDigital Library
- Arne T. Bittig, Florian Reinhardt, Simone Baltrusch, and Adelinde M. Uhrmacher. 2014. Predictive modelling of mitochondrial spatial structure and health. In Proceedings of the 12th International Conference on Computational Methods in Systems Biology (CMSB’14). 252--255. Google ScholarCross Ref
- Michael L. Blinov, James R. Faeder, Byron Goldstein, and William S. Hlavacek. 2004. BioNetGen: Software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20, 17, 3289--3291. Google ScholarDigital Library
- J. T. Bonner and L. J. Savage. 1947. Evidence for the formation of cell aggregates by chemotaxis in the development of the slime mold Dictyostelium discoideum. Journal of Experimental Zoology 106, 1, 1--26. Google ScholarCross Ref
- Luca Bortolussi, Rocco De Nicola, Vashti Galpin, Stephen Gilmore, Jane Hillston, Diego Latella, Michele Loreti, and Mieke Massink. 2015. CARMA: Collective adaptive resource-sharing Markovian agents. In Proceedings of the 13th Workshop on Quantitative Aspects of Programming Languages and Systems (QAPL’15). 16--31. DOI:http://dx.doi.org/10.4204/EPTCS.194.2 Google ScholarCross Ref
- Olivier Bouissou and Alexandre Chapoutot. 2012. An operational semantics for Simulink’s simulation engine. In Proceedings of the 13th ACM International Conference on Languages, Compilers, Tools, and Theory for Embedded Systems (LCTES’12). 129--138. Google ScholarDigital Library
- Daniel S. Calovi, Leonardo G. Brunnet, and Rita M. C. de Almeida. 2010. cAMP diffusion in Dictyostelium discoideum: A Green’s function method. Physical Review E 82, 1, 011909. DOI:http://dx.doi.org/10.1103/PhysRevE.82.011909 Google ScholarCross Ref
- Yang Cao, Daniel T. Gillespie, and Linda R. Petzold. 2005. The slow-scale stochastic simulation algorithm. Journal of Chemical Physics 122, 1, 14416. DOI:http://dx.doi.org/10.1063/1.1824902 Google ScholarCross Ref
- Yang Cao, Daniel T. Gillespie, and Linda R. Petzold. 2006. Efficient step size selection for the tau-leaping simulation method. Journal of Chemical Physics 124, 4, 044109. DOI:http://dx.doi.org/10.1063/1.2159468 Google ScholarCross Ref
- Yang Cao, Hong Li, and Linda Petzold. 2004. Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. Journal of Chemical Physics 121, 9, 4059--4067. DOI:http://dx.doi.org/10.1063/1.1778376 Google ScholarCross Ref
- Troels Christoffer Damgaard, Espen Højsgaard, and Jean Krivine. 2012. Formal cellular machinery. Electronic Notes in Theoretical Computer Science 284, 55--74. DOI:http://dx.doi.org/10.1016/j.entcs.2012.05.015 Google ScholarDigital Library
- Vincent Danos, Jérôme Feret, Walter Fontana, and Jean Krivine. 2007. Scalable simulation of cellular signaling networks. In Proceedings of the 5th Asian Symposium on Programming Languages and Systems (APLAS’07). 139--157. Google ScholarCross Ref
- Vincent Danos and Cosimo Laneve. 2004. Formal molecular biology. Theoretical Computer Science 325, 1, 69--110. Google ScholarDigital Library
- E. Weinan, D. Liu, and E. Vanden-Eijnden. 2005. Nested stochastic simulation algorithm for chemical kinetic systems with disparate rates. Journal of Chemical Physics 123, 19, 194107. DOI:http://dx.doi.org/10.1063/1.2109987 Google ScholarCross Ref
- James R. Faeder. 2011. Toward a comprehensive language for biological systems. BMC Biology 9, 1, 68.Google ScholarCross Ref
- James R. Faeder, Michael L. Blinov, and William S. Hlavacek. 2009. Rule-based modeling of biochemical systems with BioNetGen. Methods in Molecular Biology 500, 113--167. Google ScholarCross Ref
- Jasmin Fisher and Thomas A. Henzinger. 2007. Executable cell biology. Nature Biotechnology 25, 11, 1239--1249. DOI:http://dx.doi.org/10.1038/nbt1356 Google ScholarCross Ref
- Michael A. Gibson and Jehoshua Bruck. 2000. Efficient exact stochastic simulation of chemical systems with many species and many channels. Journal of Chemical Physics 104, 9, 1876--1889. DOI:http://dx.doi.org/10.1021/jp993732q Google ScholarCross Ref
- Daniel T. Gillespie. 1977. Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81, 25, 2340--2361. Google ScholarCross Ref
- Leonard A. Harris, Justin S. Hogg, and James R. Faeder. 2009. Compartmental rule-based modeling of biochemical systems. In Proceedings of the 2009 Winter Simulation Conference (WSC’2009). 908--919. Google ScholarCross Ref
- Tobias Helms, Roland Ewald, Stefan Rybacki, and Adelinde M. Uhrmacher. 2015. Automatic runtime adaptation for component-based simulation algorithms. ACM Transactions on Modeling and Computer Simulation 26, 1, 7:1--7:24. DOI:http://dx.doi.org/10.1145/2821509 Google ScholarDigital Library
- Tobias Helms, Martin Luboschik, Heidrun Schumann, and Adelinde M. Uhrmacher. 2013. An approximate execution of rule-based multi-level models. In Proceedings of the 11th International Conference on Computational Methods in Systems Biology (CMSB’13). 19--32. Google ScholarDigital Library
- Espen Højsgaard and Jean Krivine. 2011. Towards Scalable Simulation of Stochastic Bigraphs. Technical Report TR-2011-148. IT University of Copenhagen.Google Scholar
- Michael Hucka, Lucian P. Smith, Darren J. Wilkinson, Frank T. Bergmann, Stefan Hoops, Sarah M. Keating, Sven Sahle, and James C. Schaff. 2010. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core. Available at http://dx.doi.org/10.1038/npre.2010.4123.1 Google ScholarCross Ref
- Matthias Jeschke, Roland Ewald, and Adelinde M. Uhrmacher. 2011. Exploring the performance of spatial stochastic simulation algorithms. Journal of Computational Physics 230, 7, 2562--2574. DOI:http://dx.doi.org/10.1016/j.jcp.2010.12.030 Google ScholarDigital Library
- Mathias John. 2010. Reaction Constraints for the Pi-Calculus—A Language for the Stochastic and Spatial Modeling of Cell-Biological Processes. Ph.D. Dissertation. University of Rostock.Google Scholar
- Mathias John, Cédric Lhoussaine, Joachim Niehren, and Adelinde M. Uhrmacher. 2008. The attributed pi calculus. In Computational Methods in Systems Biology. Lecture Notes in Computer Science, Vol. 5307. Springer, 83--102. Google ScholarDigital Library
- Mathias John, Cédric Lhoussaine, Joachim Niehren, and Adelinde Uhrmacher. 2010. The attributed pi calculus with priorities. ACM Transactions on Computational Systems Biology 5945, 12, 13--76.Google ScholarCross Ref
- Mathias John, Cédric Lhoussaine, Joachim Niehren, and Cristian Versari. 2011. Biochemical reaction rules with constraints. In Proceedings of the 20th European Symposium on Programming (ESOP’11). 338--357. Google ScholarCross Ref
- Simon L. Peyton Jones. 2003. Haskell 98 Language and Libraries: The Revised Report. Cambridge University Press, Cambridge, England.Google Scholar
- L. N. Joppa, G. McInerny, R. Harper, L. Salido, K. Takeda, K. O’Hara, D. Gavaghan, and S. Emmott. 2013. Troubling trends in scientific software use. Science 340, 6134, 814--815. Google ScholarCross Ref
- Jongrae Kim, Pat Heslop-Harrison, Ian Postlethwaite, and Declan G. Bates. 2007. Stochastic noise and synchronisation during Dictyostelium aggregation make cAMP oscillations robust. PLoS Computational Biology 3, 11, e218. DOI:http://dx.doi.org/10.1371/journal.pcbi.0030218 Google ScholarCross Ref
- Jean Krivine, Robin Milner, and Angelo Troina. 2008. Stochastic bigraphs. Electronic Notes in Theoretical Computer Science 218, 73--96. DOI:http://dx.doi.org/10.1016/j.entcs.2008.10.006 Google ScholarDigital Library
- Tomas G. Kurtz. 1981. Approximation of Population Processes. SIAM. Google ScholarCross Ref
- Carsten Maus. 2013. Toward Accessible Multilevel Modeling in Systems Biology: A Rule-Based Language Concept. Ph.D. Dissertation. University of Rostock.Google Scholar
- Carsten Maus, Stefan Rybacki, and Adelinde M. Uhrmacher. 2011. Rule-based multi-level modeling of cell biological systems. BMC Systems Biology 5, 166. DOI:http://dx.doi.org/10.1186/1752-0509-5-166 Google ScholarCross Ref
- Orianne Mazemondet, Mathias John, Stefan Leye, Arndt Rolfs, and Adelinde M. Uhrmacher. 2012. Elucidating the sources of β-catenin dynamics in human neural progenitor cells. PLoS ONE 7, 8, e42792.Google ScholarCross Ref
- Orianne Mazemondet, Mathias John, Carsten Maus, Adelinde M. Uhrmacher, and Arndt Rolfs. 2009. Integrating diverse reaction types into stochastic models: A signaling pathway case study in the imperative π-calculus. In Proceedings of the 2009 Winter Simulation Conference (WSC’09). Google ScholarCross Ref
- S. Nähring, R. Ewald, A. M. Uhrmacher, and C. Maus. 2013. From standardized modeling formats to modeling languages and back—an exploration based on SBML and ML-Rules. In Proceedings of the 2013 Winter Simulation Conference (WSC’13). 1359--1370. DOI:http://dx.doi.org/10.1109/WSC.2013.6721522 Google ScholarCross Ref
- Nicolas Oury and Gordon D. Plotkin. 2013. Multi-level modelling via stochastic multi-level multiset rewriting. Mathematical Structures in Computer Science 23, 2, 471--503. Google ScholarCross Ref
- Ernest H. Page. 1994. Simulation Modeling Methodology: Principles and Etiology of Decision Support. Ph.D. Dissertation. Virginia Polytechnic Institute and State University.Google Scholar
- Terence Parr. 2013. The Definitive ANTLR 4 Reference (2nd ed.). Pragmatic Bookshelf, Raleigh, NC.Google Scholar
- O. Parvu, D. Gilbert, M. Heiner, F. Liu, N. Saunders, and S. Shaw. 2015. Spatial-temporal modelling and analysis of bacterial colonies with phase variable genes. ACM Transactions on Modeling and Computer Simulation 25, 2, Article No. 13. Google ScholarDigital Library
- Benjamin C. Pierce. 2002. Types and Programming Languages. MIT Press, Cambridge, MA.Google Scholar
- Corrado Priami. 1995. Stochastic π-calculus. Computer Journal 38, 7, 578--589. Google ScholarCross Ref
- Corrado Priami and Paola Quaglia. 2005. Beta binders for biological interactions. In Proceedings of the 2005 International Conference on Computational Methods in Systems Biology (CMSB’05). 20--33. Google ScholarDigital Library
- Corrado Priami, Paola Quaglia, and Alessandro Romanel. 2009. BlenX static and dynamic semantics. In Proceedings of the 20th International Conference on Concurrency Theory (CONCUR’09). 37--52. Google ScholarDigital Library
- Aviv Regev, Ekaterina M. Panina, William Silverman, Luca Cardelli, and Ehud Shapiro. 2004. BioAmbients: An abstraction for biological compartments. Theoretical Computer Science 325, 1, 141--167. Google ScholarDigital Library
- Alexander Steiniger and Adelinde M. Uhrmacher. 2016. Intensional couplings in variable structure models: An exploration based on multi-level-DEVS. ACM Transactions on Modeling and Computer Simulation 26, 2, 9:1--9:27. DOI:http://dx.doi.org/10.1145/2818641 Google ScholarDigital Library
- Vo Hong Thanh, Corrado Priami, and Roberto Zunino. 2014. Efficient rejection-based simulation of biochemical reactions with stochastic noise and delays. Journal of Chemical Physics 141, 13, 134116. DOI:http://dx.doi.org/10.1063/1.4896985 Google ScholarCross Ref
- John J. Tyson. 1991. Modeling the cell division cycle: cdc2 and cyclin interactions. Proceedings of the National Academy of Sciences 88, 16, 7328--7332. Google ScholarCross Ref
- Adelinde M. Uhrmacher. 2001. Dynamic structures in modeling and simulation: A reflective approach. ACM Transactions on Modeling and Computer Simulation 11, 2, 206--232. DOI:http://dx.doi.org/10.1145/384169.384173 Google ScholarDigital Library
- Arie van Deursen, Paul Klint, and Joost Visser. 2000. Domain-specific languages: An annotated bibliography. ACM SIGPLAN Notices 35, 6, 26--36. Google ScholarDigital Library
- Tom Warnke, Tobias Helms, and Adelinde M. Uhrmacher. 2015. Syntax and semantics of a multi-level modeling language. In Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS’15). 133--144. DOI:http://dx.doi.org/10.1145/2769458.2769467 Google ScholarDigital Library
- Richard J. Youle and Alexander M. van der Bliek. 2012. Mitochondrial fission, fusion, and stress. Science 337, 6098, 1062--1065. DOI:http://dx.doi.org/10.1126/science.1219855 Google ScholarCross Ref
- Bernard P. Zeigler. 1984. Theory of discrete event specified models: Modularity, hierarchy, experimental frames. International Journal of General Systems 10, 1 (1984), 57--84. DOI:http://dx.doi.org/10.1080/03081078408934871 Google ScholarCross Ref
Index Terms
- Semantics and Efficient Simulation Algorithms of an Expressive Multilevel Modeling Language
Recommendations
Syntax and Semantics of a Multi-Level Modeling Language
SIGSIM PADS '15: Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete SimulationThe domain specific modeling and simulation language ML-Rules makes it possible to describe cell biological systems at different levels of organization. A model is formed by attributed and dynamically nested species, with reactions that are constrained ...
Replicated Computations Results (RCR) Report for “Semantics and Efficient Simulation Algorithms for an Expressive Multi-Level Modeling Language”
Special Issue on PADS 2015‘Semantics and Efficient Simulation Algorithms on an Expressive Multi-Level Modeling Language,” by Helms et al. presents new work on the domain-specific modelling and simulation language ML-Rules [Maus et al. 2011]. For the first time, the language is ...
A review of using multilevel modeling in e-learning research
AbstractImproving e-learning involves various levels of supports. Accordingly, researchers usually adopt complex research designs with a multilevel structure or repeated measurements to capture a heuristic view of learners’ perceptions, ...
Highlights- Hierarchical Linear Modeling in e-learning has significantly increased recently.
Comments