research-article

A model for drosophila melanogaster development from a single cell to stripe pattern formation

Authors:

Danilo Pianini,

Mirko ViroliAuthors Info & Claims

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

Pages 1406 - 1412

https://doi.org/10.1145/2245276.2231999

Published: 26 March 2012 Publication History

Abstract

The development of multicellular organisms, from the early forms of zygote, involves a range of phenomena that control cell growth and differentiation, making the overall process of morphogenesis highly complex. A well studied example of such a huge phenomenon is given by Drosophila Melanogaster morphogenesis that has been object of several models, whose main goal was to investigate the mechanisms involved in the spatial and temporal evolution of the patterning process, namely gene regulatory network, mor-phogen diffusion, synthesis and degradation.

In this paper we present a model of Drosophila development that considers also nuclear division and movements as basic morphogenetic mechanisms. The model is run on top of a prototype simulator which is based on a variation of an existing SSA (Stochastic Simulation Algorithm), tailored to the specific features of embryo development, including dynamicity in the topology of compartment network.

References

[1]

B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, and P. Walter. Molecular Biology of the Cell. Garland Science Textbooks. Garland Science, Fourth edition, June 2002.

[2]

J. M. Bull, L. A. Smith, C. Ball, L. Pottage, and R. Freeman. Benchmarking java against c and fortran for scientific applications. Concurrency and Computation: Practice and Experience, 15(3--5): 417--430, 2003.

[3]

M. A. Gibson and J. Bruck. Efficient exact stochastic simulation of chemical systems with many species and many channels. The Journal of Physical Chemistry A, 104(9): 1876--1889, March 2000.

[4]

M. A. Gibson and J. Bruck. Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. A, 104: 1876--1889, 2000.

[5]

S. F. Gilbert. Developmental Biology, Eighth Edition. Sinauer Associates Inc., Eighth edition, Mar. 2006.

[6]

D. T. Gillespie. Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, 81(25): 2340--2361, December 1977.

[7]

V. V. Gursky, J. Jaeger, K. N. Kozlov, J. Reinitz, and A. M. Samsonov. Pattern formation and nuclear divisions are uncoupled in drosophila segmentation: comparison of spatially discrete and continuous models. Physica D: Nonlinear Phenomena, 197(3--4): 286--302, October 2004.

[8]

W. Hoschek. The Colt Distribution: Open Source Libraries for High Performance Scientific and Technical Computing in Java. CERN, Geneva, 2004. Available at http://acs.lbl.gov/software/colt/.

[9]

P. Lecca, A. E. C. Ihekwaba, L. Dematté, and C. Priami. Stochastic simulation of the spatio-temporal dynamics of reaction-diffusion systems: the case for the bicoid gradient. J. Integrative Bioinformatics, 7(1), 2010.

[10]

W. J. Longabaugh, E. H. Davidson, and H. Bolouri. Visualization, documentation, analysis, and communication of large-scale gene regulatory networks. Biochimica et Biophysica Acta (BBA) -Gene Regulatory Mechanisms, 1789(4): 363--374, 2009.

[11]

M. Matsumoto and T. Nishimura. Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul., 8(1): 3--30, 1998.

Digital Library

[12]

S. Montagna and M. Viroli. A framework for modelling and simulating networks of cells. Electr. Notes Theor. Comput. Sci., 268: 115--129, Dec. 2010. Proceedings of the 1st International Workshop on Interactions between Computer Science and Biology (CS2Bio'10).

Digital Library

[13]

B. Oancea, I. G. Rosca, T. Andrei, and A. I. Iacob. Evaluating java performance for linear algebra numerical computations. Procedia CS, 3: 474--478, 2011.

[14]

T. J. Perkins, J. Jaeger, J. Reinitz, and L. Glass. Reverse engineering the gap gene network of Drosophila Melanogaster. PLoS Computational Biology, 2(5): e51, 05 2006.

[15]

A. Pisarev, E. Poustelnikova, M. Samsonova, and J. Reinitz. Flyex, the quantitative atlas on segmentation gene expression at cellular resolution. Nucleic Acids Research, 37(Database-Issue): 560--566, 2009.

[16]

J. Reinitz and D. H. Sharp. Mechanism of eve stripe formation. Mechanisms of Development, 49(1--2): 133--158, Jan. 1995.

[17]

R. Rivera-Pomar and H. Jackle. From gradients to stripes in drosophila embryogenesis: filling in the gaps. Trends in Genetics, 12(11): 478--483, 1996.

[18]

A. Slepoy, A. P. Thompson, and S. J. Plimpton. A constant-time kinetic monte carlo algorithm for simulation of large biochemical reaction networks. The Journal of Chemical Physics, 128(20): 205101, 2008.

[19]

S. Surkova, D. Kosman, K. Kozlov, Manu, E. Myasnikova, A. A. Samsonova, A. Spirov, C. E. Vanario-Alonso, M. Samsonova, and J. Reinitz. Characterization of the Drosophila segment determination morphome. Developmental Biology, 313(2): 844--862, 2008.

[20]

C. Versari and N. Busi. Efficient stochastic simulation of biological systems with multiple variable volumes. Electr. Notes Theor. Comput. Sci., 194(3): 165--180, 2008.

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Cited By

Pianini D(2021)Simulation of Large Scale Computational Ecosystems with Alchemist: A TutorialDistributed Applications and Interoperable Systems10.1007/978-3-030-78198-9_10(145-161)Online publication date: 9-Jun-2021
https://doi.org/10.1007/978-3-030-78198-9_10
Montagna SOmicini A(2020)Agent-Based Modelling in Multicellular Systems BiologyData Analytics in Medicine10.4018/978-1-7998-1204-3.ch020(369-389)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-1204-3.ch020
Montagna SOmicini A(2017)Agent-Based Modelling in Multicellular Systems BiologyMulti-Agent-Based Simulations Applied to Biological and Environmental Systems10.4018/978-1-5225-1756-6.ch007(159-178)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-1756-6.ch007
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cover image ACM Conferences

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

March 2012

2179 pages

ISBN:9781450308571

DOI:10.1145/2245276

Conference Chairs:
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Paola Lecca
The Microsoft Research - University of Trento COSBI, Italy

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 March 2012

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SAC 2012

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SAC 2012: ACM Symposium on Applied Computing

March 26 - 30, 2012

Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Pianini D(2021)Simulation of Large Scale Computational Ecosystems with Alchemist: A TutorialDistributed Applications and Interoperable Systems10.1007/978-3-030-78198-9_10(145-161)Online publication date: 9-Jun-2021
https://doi.org/10.1007/978-3-030-78198-9_10
Montagna SOmicini A(2020)Agent-Based Modelling in Multicellular Systems BiologyData Analytics in Medicine10.4018/978-1-7998-1204-3.ch020(369-389)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-1204-3.ch020
Montagna SOmicini A(2017)Agent-Based Modelling in Multicellular Systems BiologyMulti-Agent-Based Simulations Applied to Biological and Environmental Systems10.4018/978-1-5225-1756-6.ch007(159-178)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-1756-6.ch007
Montagna SOmicini APianini D(2015)Extending the Gillespie's Stochastic Simulation Algorithm for Integrating Discrete-Event and Multi-Agent Based SimulationRevised Selected Papers of the International Workshop on Multi-Agent Based Simulation XVI - Volume 956810.1007/978-3-319-31447-1_1(3-18)Online publication date: 5-May-2015
https://dl.acm.org/doi/10.1007/978-3-319-31447-1_1
Pianini DSebastio SVandin A(2014)Distributed statistical analysis of complex systems modeled through a chemical metaphor2014 International Conference on High Performance Computing & Simulation (HPCS)10.1109/HPCSim.2014.6903715(416-423)Online publication date: Jul-2014
https://doi.org/10.1109/HPCSim.2014.6903715

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