ENISI SDE: A novel web-based stochastic modeling tool for computational biology

Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013

Volumn , Issue , 2013, Pages 392-397

  Mei, Yongguo ^a   Carbo, Adria ^a   Hontecillas, Raquel ^a   Bassaganya Riera, Josep ^a  

^a VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84894570574     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2013.6732524     Document Type: Conference Paper

References (36)

1. S. Adra, T. Sun, S. MacNeil, M. Holcombe, and R. Smallwood. Development of a three dimensional multiscale computational model of the human epidermis. PLoS ONE, 5(1):e8511, 2010.
2. E. Bettelli, Y. Carrier, W. Gao, T. Korn, T. B. Strom, M. Oukka, H. L. Weiner, and V. K. Kuchroo. Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells. Nature, 441(7090):235-238, May 2006.
3. E. Borenstein. Computational systems biology and in silico modeling of the human microbiome. Brief Bioinform, May 2012.
4. X. Cai and J. Wen. Efficient exact and K-skip methods for stochastic simulation of coupled chemical reactions. J Chem Phys, 131(6):064108, Aug 2009.
5. Y. Cao, D. T. Gillespie, and L. R. Petzold. The slow-scale stochastic simulation algorithm. J Chem Phys, 122(1):14116, Jan 2005.
6. A. Carbo, R. Hontecillas, B. Kronsteiner, M. Viladomiu, M. Pedragosa, P. Lu, C. W. Philipson, S. Hoops, M. Marathe, S. Eubank, et al. Systems modeling of molecular mechanisms controlling cytokine-driven cd4+ t cell differentiation and phenotype plasticity. PLoS computational biology, 9(4):e1003027, 2013.
7. K. C. Chen, T. Y. Wang, H. H. Tseng, C. Y. Huang, and C. Y. Kao. A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics, 21(12):2883-2890, Jun 2005.
8. H. de Jong. Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol., 9(1):67-103, 2002.
9. C. DeLisi. Mathematical modeling in immunology. Annu. Rev. Biophys. Bioeng., 12:117-138, 1983.
10. S. Forrest and C. Beauchemin. Computer Immunology. Immunological Reviews, 216:176-197, 2007.
11. D. T. Gillespie. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics, 22(4):403-434, 1976.
12. D. T. Gillespie. Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, 81(25):2340-2361, 1977.
13. H. J. Gold. Mathematical modeling of biological systems : an introductory guidebook. Wiley, New York, 1977.
14. M. Holcombe, S. Adra, M. Bicak, S. Chin, S. Coakley, A. I. Graham, J. Green, C. Greenough, D. Jackson, M. Kiran, S. MacNeil, A. Maleki-Dizaji, P. McMinn, M. Pogson, R. Poole, E. Qwarnstrom, F. Ratnieks, M. D. Rolfe, R. Smallwood, T. Sun, and D. Worth. Modelling complex biological systems using an agentbased approach. Integr Biol (Camb), 4(1):53-64, Jan 2012.
15. S. Hoops, S. Sahle, R. Gauges, C. Lee, J. Pahle, N. Simus, M. Singhal, L. Xu, P. Mendes, and U. Kummer. COPASI-a COmplex PAthway SImulator. Bioinformatics, 22(24):3067-3074, Dec 2006.
16. M. Hucka, A. Finney, H. M. Sauro, H. Bolouri, J. C. Doyle, H. Kitano, A. P. Arkin, B. J. Bornstein, D. Bray, A. Cornish-Bowden, A. A. Cuellar, S. Dronov, E. D. Gilles, M. Ginkel, V. Gor, I. I. Goryanin, W. J. Hedley, T. C. Hodgman, J. H. Hofmeyr, P. J. Hunter, N. S. Juty, J. L. Kasberger, A. Kremling, U. Kummer, N. Le Novere, L. M. Loew, D. Lucio, P. Mendes, E. Minch, E. D. Mjolsness, Y. Nakayama, M. R. Nelson, P. F. Nielsen, T. Sakurada, J. C. Schaff, B. E. Shapiro, T. S. Shimizu, H. D. Spence, J. Stelling, K. Takahashi, M. Tomita, J. Wagner, and J. Wang. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4):524-531, Mar 2003.
17. S. M. Iacus. Simulation and Inference for Stochastic Differential Equations: With R Examples (Springer Series in Statistics). Springer Publishing Company, Incorporated, 1 edition, 2008.
18. F. H. Karlsson, I. Nookaew, D. Petranovic, and J. Nielsen. Prospects for systems biology and modeling of the gut microbiome. Trends Biotechnol., 29(6):251-258, Jun 2011.
19. H. Kitano. Computational systems biology. Nature, 420(6912):206-210, Nov 2002.
20. N. Le Novere and T. S. Shimizu. STOCHSIM: modelling of stochastic biomolecular processes. Bioinformatics, 17(6):575-576, Jun 2001.
21. Z. Liu and Y. Cao. Detailed comparison between Stoch Sim and SSA. IET Syst Biol, 2(5):334-341, Sep 2008.
22. B. D. Macarthur, A. Ma'ayan, and I. R. Lemischka. Systems biology of stem cell fate and cellular reprogramming. Nat. Rev. Mol. Cell Biol., 10(10):672-681, Oct 2009.
23. B. D. MacArthur, C. P. Please, and R. O. Oreffo. Stochasticity and the molecular mechanisms of induced pluripotency. PLoS ONE, 3(8):e3086, 2008.
24. T. Manninen, M. L. Linne, and K. Ruohonen. Developing It stochastic differential equation models for neuronal signal transduction pathways. Comput Biol Chem, 30(4):280-291, Aug 2006.
25. J. A. Morgan and D. Rhodes. Mathematical modeling of plant metabolic pathways. Metab. Eng., 4(1):80-89, Jan 2002.
26. B. Novak, Z. Pataki, A. Ciliberto, and J. J. Tyson. Mathematical model of the cell division cycle of fission yeast. Chaos, 11(1):277-286, Mar 2001.
27. B. Novak and J. J. Tyson. Modeling the control of DNA replication in fission yeast. Proc. Natl. Acad. Sci. U.S.A., 94(17):9147-9152, Aug 1997.
28. L. Petzold and A. Hindmarsh. LSODA (Livermore Solver of Ordinary Differential Equations). Computing and Mathematics Research Division, Lawrence Livermore National Laboratory, Livermore, CA, 1997.
29. R. Ramaswamy and I. F. Sbalzarini. A partial-propensity formulation of the stochastic simulation algorithm for chemical reaction networks with delays. J Chem Phys, 134(1):014106, Jan 2011.
30. A. Saarinen, M. L. Linne, and O. Yli-Harja. Stochastic differential equation model for cerebellar granule cell excitability. PLoS Comput. Biol., 4(2):e1000004, Feb 2008.
31. E. Schneidman, B. Freedman, and I. Segev. Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Comput, 10(7):1679-1703, Oct 1998.
32. SDE-Matlab-Toolbox. http://sdetoolbox.sourceforge.net/.
33. D. M. Tartar, A. M. Van Morlan, X. Wan, F. B. Guloglu, R. Jain, C. L. Haymaker, J. S. Ellis, C. M. Hoeman, J. A. Cascio, M. Dhakal, M. Oukka, and H. Zaghouani. FoxP3+RORgammat+ T helper intermediates display suppressive function against autoimmune diabetes. J. Immunol., 184(7):3377-3385, Apr 2010.
34. M. Terzer, N. D. Maynard, M. W. Covert, and J. Stelling. Genome-scale metabolic networks. Wiley Interdiscip Rev Syst Biol Med, 1(3):285-297, 2009.
35. X. O. Yang, R. Nurieva, G. J. Martinez, H. S. Kang, Y. Chung, B. P. Pappu, B. Shah, S. H. Chang, K. S. Schluns, S. S. Watowich, X. H. Feng, A. M. Jetten, and C. Dong. Molecular antagonism and plasticity of regulatory and inflammatory T cell programs. Immunity, 29(1):44-56, Jul 2008.
36. L. Zhou, J. E. Lopes, M. M. Chong, I. I. Ivanov, R. Min, G. D. Victora, Y. Shen, J. Du, Y. P. Rubtsov, A. Y. Rudensky, S. F. Ziegler, and D. R. Littman. TGF-beta-induced Foxp3 inhibits T(H)17 cell differentiation by antagonizing RORgammat function. Nature, 453(7192):236-240, May 2008.