Skip to main content
SpringerLink
Log in

Discrete Event Multi-level Models for Systems Biology

  • Chapter
Book cover Transactions on Computational Systems Biology I

Part of the book series: Lecture Notes in Computer Science ((TCSB,volume 3380))

Abstract

Diverse modeling and simulation methods are being applied in the area of Systems Biology. Most models in Systems Biology can easily be located within the space that is spanned by three dimensions of modeling: continuous and discrete; quantitative and qualitative; stochastic and deterministic. These dimensions are not entirely independent nor are they exclusive. Many modeling approaches are hybrid as they combine continuous and discrete, quantitative and qualitative, stochastic and deterministic aspects. Another important aspect for the distinction of modeling approaches is at which level a model describes a system: is it at the “macro” level, at the “micro” level, or at multiple levels of organization. Although multi-level models can be located anywhere in the space spanned by the three dimensions of modeling and simulation, clustering tendencies can be observed whose implications are discussed and illustrated by moving from a continuous, deterministic quantitative macro model to a stochastic discrete-event semi-quantitative multi-level model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kitano, H.: Systems Biology: A Brief Overview. Science 295, 1662–1664 (2002)

    Article  Google Scholar 

  2. Wolkenhauer, O.: Systems biology: the reincarnation of systems theory applied in the biology? Briefings in Bioinformatics 2, 258–270 (2001)

    Article  Google Scholar 

  3. Chabrier-Rivier, N., Fages, F., Soliman, S.: The Biochemical Abstract Machine Biocham. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 172–191. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Hucka, M., Finney, A., Sauro, H., Bolouri, H.: The erato systems biology work¬bench: Architectural evolution. In: Yi, T.M., Hucka, M., Morohashi, M., Kitano, H. (eds.) The Proceedings of the 2nd International Conference on Systems Biology (2001)

    Google Scholar 

  5. Hucka, M., Finney, A., Sauro, H.M., Bolouri, H., Doyle, J.C., Kitano, H., Arkin, A., Bornstein, B.J., Bray, D., Cornish-Bowden, A., Cuellar, A.A., Dronov, S., Gilles, E.D., Ginkel, M., Gor, V., Goryanin, I.I., Hedley, W.J., Hodgman, T., Hofmeyr, J.H., Hunter, P.J., Juty, N., Kasberger, J.L., Kremling, A., Kummer, U., Le Novere, N., Loew, L.M., Lucio, D., Mendes, P., Minch, E., Mjolsness, E.D., Nakayama, Y., Nelson, M.R., Nielsen, P.F., Sakurada, T., Schaff, J.C., Shapiro, B., Shimizu, T.S., Spence, H.D., Stelling, J., Takahashi, K., Tomita, M., Wagner, J., Wang, J.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003)

    Article  Google Scholar 

  6. Cuellar, A., Lloyd, C., Nielsen, P., Bullivant, D., Nickerson, D., Hunter, P.: An overview of CellML: 1.1, A Biological Model Description Language. Simulation -Transactions of the SCS 79, 740–747 (2003)

    Article  Google Scholar 

  7. Domach, M.M., Leung, S.K., Cahn, R.E., Cocks, G.G., Shuler, M.L.: Computer model for glucose-limited growth of a single cell of Escherchia coli B/r-A. Biotechnology and Bioengineering 26, 203–216 (1984)

    Article  Google Scholar 

  8. Teusink, B., Passarge, J., Reijenga, C.A., Esgalhado, E., van der Weijden, C.C., Schepper, M., Walsh, M.C., Bakker, B.M., van Dam, B., van Dam, K., Wester-hoff, H.V., Snoep, J.L.: Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. European Journal of Biochemistry 267, 5313–5329 (2000)

    Article  Google Scholar 

  9. Hynne, F., Donø, S., Sørenson, P.G.: Full-scale model of glycolysis in Saccha − romyces cerevisiae. Biophysical Chemistry 94, 121–163 (2001)

    Article  Google Scholar 

  10. Santillán, M., Mackey, M.C.: Dynamic regulation of the tryptophan operon: A modeling study an comparison with experimental data. Proceedings of the National Academy of Sciences of the USA 98, 1364–1369 (2001)

    Article  Google Scholar 

  11. Reddy, V.N., Liebman, M.N., Mavrovouniotis, M.L.: Qualitative analysis of bio-chemical reaction systems. Computers in Biology and Medicine 26, 9–24 (1996)

    Article  Google Scholar 

  12. Xia, X.Q., Wise, M.J.: DiMSim: A Discrete-Event Simulator of Metabolic Networks. Journal of Chemical Information and Computer Science 43, 1011–1019 (2003)

    Google Scholar 

  13. Jones, M.E., Berry, M.N., Phillips, J.W.: Futile Cycles Revisited: A Markov Chain Model of Simultaneous Glycolysis and Gluconeogenesis. Journal of Theoretical Biology 217, 509–523 (2002)

    Article  MathSciNet  Google Scholar 

  14. Arkin, A., Ross, J.: Computational functions in biochemical reaction networks. Biophysical Journal 67, 560–578 (1994)

    Article  Google Scholar 

  15. Hjemfelt, A., Ross, J.: Implementation of logic functions and computations by chemical kinetics. Physica D 84, 180–193 (1995)

    Article  Google Scholar 

  16. Bentele, M., Eils, R.: General stochastic hybrid method for the simulation of chemical reaction processes in cells. In: Proceedings of the 2nd International Workshop on Computational Methods in Systems Biology (2004)

    Google Scholar 

  17. Zeigler, B., Praehofer, H., Kim, T.G.: Theory of Modeling and Simulation. Academic Press, London (2000)

    Google Scholar 

  18. Heylighen, F.: Downward Causation. Principia Cybernetica Web, http://pespmc1.vub.ac.be/DOWNCAUS.html (access date: 12.05.2004)

  19. Bunge, M.: Ontology II: A World of Systems. Treatise of Basic Philosophy, vol. 4. Reidel, Dordrecht (1979)

    MATH  Google Scholar 

  20. Campbell, D.: Downward causation in Hierarchically Organized Biological Systems. In: Ayala, F., Dobzhanzky, J. (eds.) Studies in the Philosophy of Biology, pp. 179–186. University of California Press, Berkeley (1974)

    Google Scholar 

  21. Salthe, S.: Evolving Hierarchical Systems. Columbia University Press, Columbia (1985)

    Google Scholar 

  22. Strohmann, R.: Organization becomes cause in the matter. Nature Biotechnology 18, 575–576 (2000)

    Article  Google Scholar 

  23. Whitesides, G., Boncheva, M.: Beyond molecules: Self-assembly of mesoscopic and macroscopic components. PNAS 99, 4769–4774 (2002)

    Article  Google Scholar 

  24. Hartwell, L.H., Hopfield, J.J., Leibler, S., Murray, A.W.: From molecular to modular cell biology. Nature 402, C47–C52 (1999)

    Article  Google Scholar 

  25. Vilar, J.M.G., Guet, C.C., Leibler, S.: Modeling network dynamics: the lac operon, a case study. The Journal of Cell Biology 161, 471–476 (2003)

    Article  Google Scholar 

  26. Kremling, A., Jahreis, K., Lengeler, J.W., Gilles, E.D.: The Organization of Metabolic Reaction Networks: A Signal Oriented Approach to Cellular Models. Metabolic Engineering 2, 190–200 (2000)

    Article  Google Scholar 

  27. Kremling, A., Gilles, E.D.: The Organization of Metabolic Reaction Networks II. Signal Processing in Hierarchical Structured Functional Units. Metabolic Engineering 3, 138–150 (2001)

    Article  Google Scholar 

  28. Kremling, A., Bettenbrock, K., Laube, B., Jahreis, K., Lengeler, J.W., Gilles, E.D.: The Organization of Metabolic Reaction Networks: III. Application for Diauxic Growth on Glucose and Lactose. Metabolic Engineering 3, 362–379 (2001)

    Article  Google Scholar 

  29. Degenring, D., Röhl, M., Uhrmacher, A.: Discrete Event, Multi-Level Simulation of Metabolite Channeling. BioSystems 75, 29–41 (2004)

    Article  Google Scholar 

  30. Minsky, M.: Models, Minds, Machines. In: Proc. IFIP Congress, pp. 45–49 (1965)

    Google Scholar 

  31. Cellier, F.E.: Continuous System Modeling. Springer, New York (1992)

    Google Scholar 

  32. de Jong, H.: Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. Journal of Computational Biology 9, 67–103 (2002)

    Article  Google Scholar 

  33. Mendes, P.: GEPASI: a software package for modelling the dynamics, steady states and control of biochemical and other systems. Computer Applications in the Biosciences 9, 563–571 (1993)

    Google Scholar 

  34. Ginkel, M.A.K., Nutsch, T., Rehner, R., Gilles, E.: Modular modeling of cellular systems with ProMoT/Diva. Bioinformatics 19, 1169–1176 (2003)

    Article  Google Scholar 

  35. Sauro, H.: Jarnac: A system for interactive metabolic analysis. In: Animating the cellular map: Proceedings of the 9th International Meeting on BioThermoKinet-ics. Stellenbosch University Press (2000)

    Google Scholar 

  36. Goryanin, I., Hodgman, T., Selkov, E.: Mathematical simulation and analysis of cellular metabolism and regulation. Bioinformatics 15, 749–758 (1999)

    Article  Google Scholar 

  37. Shapiro, B.E., Levchenko, A., Meyerowitz, E.M., Wold, B.J., Mjolsness, E.D.: Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations. Bioinformatics 19, 677–678 (2003)

    Article  Google Scholar 

  38. Fuss, H.: Simulation of Biological Systems with PetriNets - Introduction to Modelling of Distributed Systems. In: Moller, D. (ed.) Advances in System Analysis, Vieweg, Braunschweig, Wiesbaden, pp. 1–12 (1987)

    Google Scholar 

  39. Goss, P., Peccoud, J.: Biochemistry Quantitative Modeling of Stochastic Systems in Molecular Biology by Using Stochastic Petri Nets. Proceedings of National Academy of Sciences of the USA 95, 6750–6755 (1998)

    Article  Google Scholar 

  40. Zeigler, B.: Multifacetted Modelling and Discrete Event Simulation. Academic Press, London (1984)

    MATH  Google Scholar 

  41. Petri Nets World., http://www.daimi.au.dk/PetriNets/ (access date: 08.11.2004)

  42. Milner, R.: Communicating and Mobile Systems: The π Calculus. Cambridge University Press, Cambridge (1999)

    MATH  Google Scholar 

  43. Priami, C.: The Stochastic pi-Calculus. The Computer Journal 38, 578–589 (1995)

    Article  Google Scholar 

  44. Zeigler, B.: A Note on System Modelling, Aggregation and Reductionism. J. of Biomedical Computing 2, 277–280 (1971)

    Article  Google Scholar 

  45. Uhrmacher, A.: Dynamic Structures in Modeling and Simulation - A Reflective Approach. ACM Transactions on Modeling and Simulation 11, 206–232 (2001)

    Article  Google Scholar 

  46. Uhrmacher, A.M.: Reasoning about Changing Structure: A Modeling Concept for Ecological Systems. International Journal on Applied Artificial Intelligence 9, 157–180 (1995)

    Article  Google Scholar 

  47. Kam, N., Harel, D., Kugler, H., Marelly, R., Pnueli, A., Hubbard, E., Stern, M.: Formal Modelling of C. elegans Development: A Scenario Based Approach. In: Priami, C. (ed.) CMSB 2003. LNCS, vol. 2602, pp. 3–20. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  48. Borland, S., Vangheluwe, H.: Transforming Statecharts to DEVS. In: Summer Computer Simulation Conference, pp. 154–159 (2003)

    Google Scholar 

  49. Danos, V., Laneve, C. (eds.): BioConcur - Workshop on Concurrent Models in Molecular Biology. Electronic Notes in Theoretical Computer Science (2003)

    Google Scholar 

  50. Kuttler, C., Blossey, R., Niehren, J.: Gene Regulation in the Pi Caluculus: Modelling Cooperativity at the Lambda Switch. In: BioConcur 2004. Elsevier, Amsterdam (2004)

    Google Scholar 

  51. Regev, A., Shapiro, E.: Cells as computation. Nature 419, 343 (2002), www.wisdom.weizmann.ac.il~aviv

    Google Scholar 

  52. Lecca, P., Priami, C., Quaglia, P., Rossi, B., Laudanna, C., Constantin, G.: Language Modelling and Simulation of Autoreactive Lymphocytes Recruitment in Inflamed Brain Vessels. SCS Simulation (Submitted)

    Google Scholar 

  53. Van Gend, K.U.K.: STODE - Automatic Stochastic Simulation of Systems Described by differential equations. In: Yi, T.M., Hucka, M., Morohasi, M., Kitano, H. (eds.) Proceedings of the 2nd International Conference on Systems Biology, pp. 326–333. Omnipress, Madison (2001)

    Google Scholar 

  54. Philipps, A., Cardelli, L.: A correct abstract machine for the stochastic pi-calculus. In: Proc. of BIO-CONCUR 2004. Electronic Notes in Theoretical Computer Science. Elsevier, Amsterdam (2004)

    Google Scholar 

  55. Regev, A., Panina, E., Silverman, W., Cardelli, L., Shapiro, E.: BioAmbients: An Abstraction for Biological Compartments. Theoretical Computer Science (2004)

    Google Scholar 

  56. Cardelli, L.: Brane Calculi. In: Proc. of BIO-CONCUR 2003. Electronic Notes in Theoretcial Computer Science. Elsevier, Amsterdam (2003)

    Google Scholar 

  57. Mc Collum, J., Cox, C., Simpson, M., Peterson, G.: Accelerating Gene Regulatory Network Modeling Using Grid-Based Simulation. Simulation - Transactions of the SCS (2004)

    Google Scholar 

  58. Danos, V., Pradalier, S.: Projective brane calculus. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 134–148. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  59. Zeigler, B.: Discrete Event Abstraction: An Emerging Paradigm For modeling complex adaptive systems. In: Booker, L. (ed.) Perspectives on Adaptation in Natural and Artificial Systems - Essays in Honor of John Holland. Oxford University Press, Oxford (2004)

    Google Scholar 

  60. Nutaro, J., Zeigler, B., Jammalamadaka, R., Akerkar, S.: Speeding-Up the Simulation of Continuous Systems with Parallel DEVS: A Gas Shock Wave Example. In: Darema, F. (ed.) Dynamic Data Driven Applications Systems. Academic Publishers (2004)

    Google Scholar 

  61. Chen, M., Hofestädt, R., Freier, A.: A Workable Approach for Modeling and Simulation of Biochemical Processes with Hybrid Petri Net System. In: 1st International MTBio Workshop on Function and Regulation of Cellular Systems: Experiments and Models, Dresden (2001)

    Google Scholar 

  62. Matsuno, H., Fujita, S., Doi, A., Nagasaki, M., Miyano, S.: Towards biopathway modeling and simulation. In: van der Aalst, W.M.P., Best, E. (eds.) ICATPN 2003. LNCS, vol. 2679, pp. 3–22. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  63. Cho, K.H., Johansson, K., Wolkenhauer, O.: A Hybrid Systems Framework for Cellular Processes (2004) (submitted for publication)

    Google Scholar 

  64. Matlab Simulink, http://www.mathworks.com (access date: 08.11.2004)

  65. Liu, J., Lee, E.: A component-based approach to modeling and simulating mixed-signal and hybrid systems. ACM Transactions on Modeling and Computer Simulation 12, 343–368 (2002)

    Article  Google Scholar 

  66. Henzinger, T.: The theory of hybrid automata. In: Proceedings of the 11th Annual Symposium on Logic in Computer Science (LICS), pp. 278–292. IEEE Computer Society Press, Los Alamitos (1996)

    Chapter  Google Scholar 

  67. Alur, R., Belta, C., Ivancic, F., Kumar, V., Rubin, H., Schug, J., Sokolsky, O., Webb, J.: Visual programming for modeling and simulation of biomolecular reg- ulatory networks. In: International Conference on High Performance Computing (2002)

    Google Scholar 

  68. Mishra, B., Policriti, A.: Systems Biology and Automata. In: 3rd Workshop on Computation of Biochemical Pathways and Genetic Networks, Springer, Heidelberg (2003)

    Google Scholar 

  69. Belta, C., Finin, P., Habets, L., Halasz, A., Irnieliniksi, M., Kurnar, V., Rubin, H.: Understanding the bacterial stringent response using reachability analysis of hybrid systems. In: Alur, R., Pappas, G.J. (eds.) HSCC 2004. LNCS, vol. 2993. Springer, Heidelberg (2004)

    Google Scholar 

  70. Law, A., Kelton, W.: Simulation, Modeling, and Analysis. MCGraw Hill International Editions, New York (1991)

    Google Scholar 

  71. Rao, C.V., Wolf, D.M., Arkin, A.P.: Control, exploitation and tolerance of intra-cellular noise. Nature 420, 231–237 (2002)

    Article  Google Scholar 

  72. Fedoroff, N., Fontana, W.: Small Numbers of Big Molecules. Science 297, 1129–1131 (2002)

    Article  Google Scholar 

  73. Gibson, M.A., Bruck, J.: EfficientExact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. Journal of Physical Chemistry A 104, 1876–1889 (2000)

    Article  Google Scholar 

  74. Gillespie, D.T.: A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions. The Journal of Physical Chemistry B 22, 403–434 (1976)

    MathSciNet  Google Scholar 

  75. Gillespie, D.T.: Exact Stochastic Simulation of Coupled Chemical Reactions. The Journal of Physical Chemistry B 81, 2340–2361 (1977)

    Article  Google Scholar 

  76. Kuo, D., Keasling, J.D.: A Monte Carlo simulation of plasmid replication during the bacterial division cycle. Biotechnology and Bioengineering 52, 633–647 (1996)

    Article  Google Scholar 

  77. Kierzek, A.M.: STOCKS: STOChastic Kinetic Simulations of biochemical systems with Gillespie algorithm. Bioinformatics 18, 470–481 (2002)

    Article  Google Scholar 

  78. Cowan, R.: Stochastic models for DNA replication. In: Shanbhag, D., Rao, C. (eds.) Stochastic Processes. Handbook of Statistics (2003)

    Google Scholar 

  79. Gillespie, D.T.: Approximate accelerated stochastic simulation of chemically reacting systems. The Journal of Chemical Physics 115, 1716–1733 (2001)

    Article  Google Scholar 

  80. Gillespie, D.T., Petzold, L.R.: Improved leap-size selection for accelerated stochastic simulation. The Journal of Chemical Physics 119, 8229–8234 (2004)

    Article  Google Scholar 

  81. Puchulka, J., Kierzek, A.M.: Bridging the Gap between Stochastic and Deterministic Regimes in the Kinetic Simulations of the Biochemical Reaction Networks. Biophysical Journal 86, 1357–1372 (2004)

    Article  Google Scholar 

  82. Kuipers, B.: Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. MIT Press, Cambridge (1994)

    Google Scholar 

  83. Heidtke, K.R., Schulze-Kremer, S.: Design and implementation of a qualitative simulation model of λ phage infection. Bioinformatics 14, 81–91 (1998)

    Article  Google Scholar 

  84. Ideker, T., Lauffenburger, D.: Building with a scaffold: emerging strategies for high- to low-level cellular modeling. Trends in Biotechnology 21, 255–262 (2003)

    Article  Google Scholar 

  85. Thomas, R., Kaufman, M.: Multistationarity, the basis of cell differentiation and memory. I. structural conditions of multistationarity and other nontrivial behavior. Chaos 11, 170–179 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  86. Thomas, R., Kaufman, M.: Multistationarity, the basis of cell differentiation and memory. II. Logical analysis of regulatory networks in terms of feedback circuits. Chaos 11, 180–195 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  87. Tilly, C.: Micro, Macro, or Megrim? Paper for the Göttinger Gespräch zur Geschichtswissenschaft, Microhistory - Macrohistory: Complementary or Incommensurable? (1997)

    Google Scholar 

  88. Knorr-Cetina, K., Cicourel, A. (eds.): Advances in Social Theory and Methodology - Towards an Integration of Micro and Macro Sociologies. Routledge and Kegan Paul, Boston (1981)

    Google Scholar 

  89. Troitzsch, K.: Multilevel Simulation. In: Troitzsch, K., Mueller, U., Gilbert, G., Doran, J. (eds.) Social Science Microsimulation, pp. 107–120. Springer, Heidelberg (1996)

    Google Scholar 

  90. Kokai, G., Toth, Z., Vanyi, R.: Modelling blood vessels of the eye with parametric L-systems using evolutionary algorithms. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J.C. (eds.) AIMDM 1999. LNCS (LNAI), vol. 1620, pp. 433–442. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  91. Garcia-Olivares, A., Villarroel, M., Marijuan, P.C.: Enzymes as molecular automata: a stochastic model of self-oscillatory glycolytic cycles in cellular metabolism. Biosystems 56, 121–129 (2000)

    Article  Google Scholar 

  92. Wurthner, J., Mukhopadhyay, A., Piemann, C.: A cellular automaton model of cellular signal transduction. Computers in Biology and Medicine 30, 1–21 (2000)

    Article  Google Scholar 

  93. Alber, M., Kiskowski, M., Glazier, J.A., Jiang, Y.: On cellular automaton approaches to modeling biological cells. In: Rosenthal, J., Gilliam, D.S. (eds.) Mathematical Systems Theory in Biology, Communications, Computation and Finance. IMA Volumes in Mathematics and its Applications, vol. 134, pp. 1–39 (2003)

    Google Scholar 

  94. Kniemeyer, O., Buck-Sorlin, G.H., Kurth, W.: Representation of genotype and phenotype in a coherent framework based on extended L-systems. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 625–634. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  95. Swameye, I., Muller, T., Timmer, J., Sandra, O., Klingmuller, U.: Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by data-based dynamic modeling. PNAS 100, 1028–1033 (2003)

    Article  Google Scholar 

  96. Uhrmacher, A.M., Swartout, W.: Agent-Oriented Simulation. In: Obaidat, M., Pa-padimitriou, G. (eds.) Applied System Simulation, Amsterdam. Kluwer Academic Press, Dordrecht (2003)

    Google Scholar 

  97. Uhrmacher, A., Degenring, D.: From macro- to Multi-Level Models in Systems Biology. In: Gauges, R., Kummer, U., Pahle, J., Rost, U. (eds.) Proc. of the 3rd Workshop on Computation of Biochemical Pathways and Genetic Networks (2003)

    Google Scholar 

  98. Kreft, J., Booth, G., Wimpenny, J.: BacSim a simulator for individual based modelling of bacterial colony growth. Microbiology 144, 3275–3287 (1998)

    Article  Google Scholar 

  99. Gregory, R.: An Individual Based Model for Simulating Bacterial Evolution. In: Evolvability and Individuality Workshop, University of Hertfordshire (2002)

    Google Scholar 

  100. Degenring, D., Roohl, M., Uhrmacher, A.M.: Discrete event simulation for a better understanding of metabolite channeling- A system-theoretic approach. In: Priami, C. (ed.) CMSB 2003. LNCS, vol. 2602, pp. 114–126. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  101. Rizzi, M., Baltes, T., Theobald, U., Reuss, M.: In Vivo Analysis of Metabolic Dynamics in Saccheromyces cerevisiae II. Mathematical Model. Biotechnology and Bioengineering 55, 592–608 (1997)

    Article  Google Scholar 

  102. Takahashi, K., Yugi, K., Hashimoto, K., Yamada, Y., Pickett, C., Tomita, M.: Computational challenges in cell simulation. IEEE Intelligent Systems 17, 64–71 (2002)

    Article  Google Scholar 

  103. Henson, M., Müller, D., Reuss, M.: Cell Population Modelling of Yeast Glycolytic Oscillations. Biochemical Journal 368, 433–446 (2002)

    Article  Google Scholar 

  104. Morton-Firth, C.J., Bray, D.: Predicting Temporal Fluctuations in an Intracellu-lar Signalling Pathway. Journal of Theoretical Biology 192, 117–128 (1998)

    Article  Google Scholar 

  105. Anderson, K., Miles, E., Johnson, K.: Serine Modulates Substrate Channeling in Tryptophan Synthase. The Journal of the Biological Chemistry 266, 8020–8033 (1991)

    Google Scholar 

  106. Anderson, K., Kim, A., Quillen, J., Sayers, E., Yand, X., Miles, E.: Kinetic Characterization of Channel Impaired Mutants of Tryptophan Synthase. The Journal of Biological Chemistry 270, 29936–29944 (1995)

    Article  Google Scholar 

  107. Uhrmacher, A.M., Tyschler, P., Tyschler, D.: Modeling Mobile Agents. Future Generation Computer System 17, 107–118 (2000)

    Article  Google Scholar 

  108. Elmquist, H., Mattson, S.: Modelica - The Next Generation Modeling Language - An International Design Effort. In: First World Congress of System Simulation, Singapore (1997)

    Google Scholar 

  109. Takahashi, K., Kaizu, K., Hu, B., Tomita, M.: A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics 20, 538–546 (2004)

    Article  Google Scholar 

  110. Biospi simulator, http://www.wisdom.weizmann.ac.il~biospi (access date: Okt. 2004)

    Google Scholar 

  111. Lynch, N., Segala, R., Vaandraager, F.: Hybrid I/O automata. Technical Report MITLCS-TR-827d, MIT Laboratory for Computer Science (2003)

    Google Scholar 

  112. Anylogic - Simulation Software, http://www.xjtek.com/anylogic/ (access date: May 2004)

  113. Nagasaki, M., Doi, A., Matsuno, H., Miyano, S.: Genomic Object Net: A platform for modeling and simulating biopathways. Applied Bioinformatics (2003)

    Google Scholar 

  114. Biermann, S., Uhrmacher, A., Schumann, H.: Supporting Multi-Level Models in Systems Biology by Visual Methods. In: Proceedings of European Multi-Simulation Conference (2004)

    Google Scholar 

  115. Fujimoto, R.: Parallel and Distributed Simulation Systems. John Wiley and Sons, Chichester (2000)

    Google Scholar 

  116. Zeigler, B.: Statistical Simplification of Neural Nets. Intl. J. of Machine Studies 7, 371–393 (1975)

    Article  MATH  Google Scholar 

  117. Zeigler, B.: Simplification of Biochemical Systems. In: Segel, L. (ed.) Mathematical Models in Molecular and Cellular Biology. Cambridge University Press, Cambridge (1981)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Computer Science, University of Rostock, Albert-Einstein-Str. 21, D-18059, Rostock, Germany

    Adelinde M. Uhrmacher & Daniela Degenring

  2. Arizona Center of Integrative Modeling and Simulation, University of Arizona, Tucson, AZ, 85721, USA

    Bernard Zeigler

Authors
  1. Adelinde M. Uhrmacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniela Degenring
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bernard Zeigler
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Centre for Computational and Systems Biology, The Microsoft Research - University of Trento, Piazza Manci, 17, 38050, Povo, TN, Italy

    Corrado Priami

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Uhrmacher, A.M., Degenring, D., Zeigler, B. (2005). Discrete Event Multi-level Models for Systems Biology. In: Priami, C. (eds) Transactions on Computational Systems Biology I. Lecture Notes in Computer Science(), vol 3380. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32126-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-32126-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25422-5

  • Online ISBN: 978-3-540-32126-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation