13th International Conference on Membrane Computing - MTA Sztaki

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T. Hinze, B. Schell, M. Schumann, C. Bodenstein The corresponding experimental setup can be captured by the P meta framework assuming the non-probabilistic P module n = (X, Y, F) from [3] to act as unique neuronal core oscillator from which up to 14 instances are employed: 5 Conclusions Π SCN =(M,P) with M = {(n, 14)} P = {0 :ModuleConnect(n[1] → n[3], {(Y,X)}), 0:ModuleConnect(n[1] → n[4], {(Y,X)}),..., 0:ModuleConnect(n[10] → n[14], {(Y,X)}) 300 : ModuleConnect(n[2] → n[4], {(Y,X)}),..., 300 : ModuleConnect(n[6] → n[10], {(Y,X)})} Assembly and reassembly as well as composition and decomposition of predefined reaction network modules on the fly appears to be a promising strategy in order to achieve complex systems capable of new or extended functionality. Inspired by biological evolution at a granularity of highly conserved genetic ensembles, our P meta framework provides a tool for control and systematic conduction of corresponding studies. Within two application cases, we demonstrated the descriptive capacity behind this approach. Particularly at the edge of straight-forward lines for complex network inference in reverse engineering, a strict utilisation of previously identified modules and their reuse can contribute to explore the abilities of resulting systems in a more effective and efficient way. Further studies will address technical aspects of module integration from different specification platforms. It seems that modules based on ODEs on the one hand and discrete forms along with rewriting rules on the other, needs to interact with each other via appropriate interfaces. To this end, the Infobiotics workbench offers an extensive functionality [5]. A fruitful combination of the universe of P systems featured by their ability to manage dynamical structures with the universe of ODEs featured by a profound toolbox for analytical examination could be an innovative clue. In all simulation studies carried out for this paper, we consistently utilised the Complex Pathway Simulator software CoPaSi (version 4.7) along with the Gepasi Model Extractor for generation of several instances from a common module specification. Corresponding source files are available from the authors upon request. References 1. B.P. Belousov. A periodic reaction and its mechanism. Compilation of Abstracts in Radiation Medicine 147:145, 1959 2. B.W. Bequette. Process control: modeling, design, and simulation. Prentice Hall, 2003 3. S. Bernard, D. Gonze, B. Cajavec, H. Herzel, A. Kramer. Synchronization-induced rhythmicity of circadian oscillators in the suprachiasmatic nucleus. PLoS Comput Biol 3(4):e68, 2007 240
Maintenance of chronobiological information by P system mediated assembly of control units for oscillatory waveforms and frequency 4. L. Bianco, F. Fontana, V. Manca. P systems with reaction maps. <strong>International</strong> Journal of Foundations of Computer Science 17(1):27-48, 2006 5. J. Blakes, J. Twycross, F.J. Romero-Campero, N. Krasnogor. The Infobiotics Workbench: An Integrated in silico Modelling Platform for Systems and Synthetic Biology. Bioinformatics 27(23):3323-3324, 2011 6. A. Cagnacci. Melatonin in Relation to Physiology in Adult Humans. Journal of Pineal Research 21(4):200-213, 1996 7. B.M. Carlson. Principles of Regenerative Biology. Elsevier Academic Press, 2007 8. K.A. Connors. Chemical Kinetics. VCH Publishers, Weinheim, 1990 9. M.B. Elowitz, S. Leibler. A synthetic oscillatory network of transcriptional regulators. Nature 403(6767):335-338, 2000 10. F. Fontana, V. Manca. Discrete solutions to differential equations by metabolic P systems. Theoretical Computer Science 372(2-3):165-182, 2007 11. W. Gerstner, W. Kistler. Spiking Neuron Models – Single Neurons, Populations, Plasticity. Cambridge University Press, 2002 12. B.C. Goodwin. Oscillatory behaviour in enzymatic control processes. Advanced Enzyme Regulation 3:425-438, 1965 13. T. Hinze, T. Lenser, P. Dittrich. A Protein Substructure Based P System for Description and Analysis of Cell Signalling Networks. In H.J. Hoogeboom et al. (Eds.). Proc. WMC7. Lecture Notes in Computer Science 4361:409-423, 2006 14. T. Hinze, T. Lenser, G. Escuela, I. Heiland, S. Schuster. Modelling Signalling Networks with Incomplete Information about Protein Activation States: A P System Framework of the KaiABC Oscillator. G. Păun et al. (Eds.). Proc. WMC10. Lecture Notes in Computer Science 5957:316-334, 2010 15. T. Hinze, R. Fassler, T. Lenser, P. Dittrich. Register Machine Computations on Binary Numbers by Oscillating and Catalytic Chemical Reactions Modelled using Mass-Action Kinetics. <strong>International</strong> Journal of Foundations of Computer Science 20(3):411-426, 2009 16. T. Hinze, C. Bodenstein, B. Schau, I. Heiland, S. Schuster. Chemical Analog Computers for Clock Frequency Control Based on P Modules. In M. Gheorghe et al. (Eds.). Proc. CMC12. Lecture Notes in Computer Science 7184:182-202, 2012 17. R.R. Klevecz. Quantized generation time in mammalian cells as an expression of the cellular clock. Proc. Natl. Acad. Sci USA 73(11):4012-4016, 1976 18. J.W. Lengeler, G. Drews, H.G. Schlegel. Biology of the Prokaryotes. Thieme, 1999 19. V. Manca. Metabolic P Systems for Biochemical Dynamics. Progress in Natural Sciences 17(4):384-391, 2007 20. C.L. Marlatt. The periodical cicada. Bull. U.S. Dept. Agri., Div. Entomol. Bull. 18:52, 1907 21. J.M. Mitchison. The Biology of the Cell Cycle. Cambridge University Press, 1971 22. K. Nath, A.L. Koch. Protein Degradation in Escherichia coli: Measurementof Rapidly and Slowly Decaying Components. The Journal of Biological Chemistry 245(11):2889-2900, 1970 23. A.V. Panfilov, A.V. Holden. Computational Biology of the Heart. John Wiley & Sons, 1997 24. R.J. Reiter. The Melatonin Rhythm: Both a Clock and a Calendar. Cellular and Molecular Life Sciences 49(8):654-664, 1993 25. F.J. Romero-Campero, J. Twycross, M. Camara, M. Bennett, M. Gheorghe, N. Krasnogor. Modular Assembly of Cell Systems Biology Models using P Systems. <strong>International</strong> Journal of Foundations of Computer Science 20(3):427-442, 2009 26. J.J. Tyson, B. Novak. Temporal Organization of the Cell Cycle. Current Biology 18:759-768, 2008 241
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Erzsébet Csuhaj-Varjú Marian Gheo
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Editors Erzsébet Csuhaj-Varjú Dep
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Preface In addition to the texts or
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Contents M.A. Martínez-del-Amor, I
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(Tissue) P systems with decaying ob
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Sorin Istrail, Solomon Marcus of pr
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Sorin Istrail, Solomon Marcus stati
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Sorin Istrail, Solomon Marcus 4.
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Sorin Istrail, Solomon Marcus his f
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Turing’s three pioneering initiat
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Turing’s three pioneering initiat
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MP systems for systems biology tere
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Yu. Rogozhin this obstacle and to g
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Yu. Rogozhin 10. J. Cocke, M. Minsk
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M. Stannett The question arises, wh
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Regular Contributions
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T. Ahmed, G. DeLancy, A. Paun almos
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T. Ahmed, G. DeLancy, A. Paun purpo
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T. Ahmed, G. DeLancy, A. Paun Fig.
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T. Ahmed, G. DeLancy, A. Paun gener
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T. Ahmed, G. DeLancy, A. Paun
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T. Ahmed, G. DeLancy, A. Paun Table
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Asynchronuous and maximally paralle
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A. Alhazov, Yu. Rogozhin With coope
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A. Alhazov, Yu. Rogozhin 2.3 P Syst
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A. Alhazov, Yu. Rogozhin applied, f
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A. Alhazov, Yu. Rogozhin - one extr
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B. Aman, G. Ciobanu formalisms is e
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B. Aman, G. Ciobanu membranes appea
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B. Aman, G. Ciobanu • [ ] recep r
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B. Aman, G. Ciobanu Definition 3. A
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B. Aman, G. Ciobanu { w i , for all
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B. Aman, G. Ciobanu The four transi
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B. Aman, G. Ciobanu A state space i
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On structures and behaviors of spik
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L. Cienciala, L. Ciencialová, M. P
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H. ElGindy, R. Nicolescu, H. Wu pat
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H. ElGindy, R. Nicolescu, H. Wu (b)
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H. ElGindy, R. Nicolescu, H. Wu 8 r
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H. ElGindy, R. Nicolescu, H. Wu Pse
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H. ElGindy, R. Nicolescu, H. Wu to
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H. ElGindy, R. Nicolescu, H. Wu ter
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H. ElGindy, R. Nicolescu, H. Wu 4.
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H. ElGindy, R. Nicolescu, H. Wu Tab
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Membranes with local environments A
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Membranes with local environments T
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Membranes with local environments -
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Membranes with local environments -
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Membranes with local environments I
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On efficient algorithms for SAT dis
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On efficient algorithms for SAT 2 S
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On efficient algorithms for SAT 2.4
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On efficient algorithms for SAT Not
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On efficient algorithms for SAT the
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On efficient algorithms for SAT 32.
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A. Obtu̷lowicz 2−ramification
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Modelling ecological systems with t
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P. Sosík [9, 10], several research
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P. Sosík The rules of a system lik
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P. Sosík 1. The family Π is polyn
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P. Sosík (a) subsequently and recu
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P. Sosík contentFinal = content(l,
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P. Sosík Hence, with the aid of th
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P. Sosík 8. Lakshmanan K. and Rama
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S. Verlan, J. Quiros more relevance
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S. Verlan, J. Quiros For a regular
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S. Verlan, J. Quiros digital FPGA c
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S. Verlan, J. Quiros where k j = N
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S. Verlan, J. Quiros Now let us con
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S. Verlan, J. Quiros We consider a
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S. Verlan, J. Quiros the present co
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S. Verlan, J. Quiros Using similar
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S. Verlan, J. Quiros 5. M. Maliţa,
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Simplifying Event-B models of P sys
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Author Index Adorna H., 143 Agrigor
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