Automated abstraction methodology for genetic regulatory networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4220 LNBI, Issue , 2006, Pages 150-175

  Kuwahara, Hiroyuki ^a   Myers, Chris J ^a   Samoilov, Michael S ^b   Barker, Nathan A ^a   Arkin, Adam P ^b  

^a UNIVERSITY OF UTAH   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; GENETIC ENGINEERING; MARKOV PROCESSES; MATHEMATICAL MODELS;

CHEMICAL MASTER EQUATION; DECISION SWITCH;

ABSTRACTING;

EID: 38549150205     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11880646_7     Document Type: Conference Paper

References (33)

1. Jong, H.D.: Modeling and simulation of genetic regulatory systems: A literature review. J. Comp. Biol. 9(1) (2002) 67-103
2. Baldi, P., Hatfield, G.W.: DNA Microarrays and Gene Expression. Cambridge University Press (2002)
3. Arkin, A., Ross, J., McAdams, H.: Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected escherichia coli cells. Genetics 149 (1998) 1633-1648
4. Elowitz, M.B., Levine, A.J., Siggia, E.D., Swain, P.S.: Stochastic gene expression in a single cell. Science 297 (2002) 1183-1186
5. Rao, C.V., Wolf, D.M., Arkin, A.P.: Control, exploitation and tolerance of intracellular noise. Nature 420 (2002) 231-238
6. Samoilov, M., Plyasunov, S., Arkin, A.P.: Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proceedings of the National Academy of Sciences US 102(7) (2005) 2310-5
7. Raser, J.M., O'Shea, E.K.: Control of stochasticity in eukaryotic gene expression. Science 304 (2004) 1811-1814
8. Kierzek, A.M., Zaim, J., Zielenkiewicz, P.: The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression. J. Biol. Chem 276 (2001) 8165
9. Gillespie, D.T.: A rigorous derivation of the chemical master equation. Physica A 188 (1992) 404-425
10. Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics 22 (1976) 403-434
11. Turner, T.E., Schnell, S., Burrage, K.: Stochastic approaches for modelling in vivo reactions. Computational Biology 28 (2004)
12. Gibson, M., Bruck, J.: Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. A 104 (2000) 1876-1889
13. Gibson, M., Bruck, J.: An efficient algorithm for generating trajectories of stochastic gene regulation reactions. Technical report, California Institute of Technology (1998)
14. Gillespie, D.T.: Approximate accelerated stochastic simulation of chemically reacting systems. Journal of Chemical Physics 115(4) (2001) 1716-1733
15. Rathinam, M., Cao, Y., Petzold, L., Gillespie, D.: Stiffness in stochastic chemically reacting systems: The implicit tau-leaping method. Journal of Chemical Physics 119 (2003) p12784-94
16. Gillespie, D., Petzold, L.: Improved leap-size selection for accelerated stochastic simulation. Journal of Chemical Physics 119 (2003)
17. Cao, Y., Gillespie, D., Petzold, L.: Avoiding negative populations in explicit tau leaping. Journal of Chemical Physics 123 (2005)
18. Rao, C.V., Arkin, A.P.: Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the gillespie algorithm. J. Phys. Chem. 118(11) (2003)
19. max estimates. Comments on Theoretical Biology 8 (2003) 169-187
20. Myers, C.J., Belluomini, W., Killpack, K., Mercer, E., Peskin, E., Zheng, H.: Timed circuits: A new paradigm for high-speed design. (2001) 335-340
21. Berry, R.S., Rice, S.A., Ross, J.: Physical Chemistry (2nd Edition). Oxford University Press, New York (2000)
22. Systems Biology Workbench Development Group. (http://www.sbw-sbml.org/)
23. BioSPICE. (http://www.biospice.org/)
24. Schnell, S., Mendoza, C.: Enzyme kinetics of multiple alternative substrates. Journal of Mathematical Chemistry 27 (2000) 155-170
25. Ackers, G.K., Johnson, A.D., Shea, M.A.: Quantitative model for gene regulation by λ phage repressor. Proc. Natl. Acad. Sci. USA 79 (1982) 1129-1133
26. Santillán, M., Mackey, M.C.: Why the lysogenic state of phase λ is stable: A mathematical modeling approch. Biophysical Jounal 86 (2004)
27. Dacol, D., Rabitz, H.: Sensitivity analysis of stochastic kinetic models. J. Math. Phys. 25 (1984)
28. Gunawan, R., Cao, Y., Petzold, L., Doyle, F.J.: Sensitivity analysis of discrete stochastic systems. Biophysical Journal 88 (2005) 2530-2540
29. REB2SAC. (http://www.async.ece.utah.edu/tools/)
30. Kourilsky, P.: Lysogenization by bacteriophage lambda: I. multiple infection and the lysogenic response. Mol. Gen. Genet. 122 (1973) 183-195
31. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25) (1977) 2340-2361
32. Gillespie, D.T.: Markov Processes An Introduction for Physical Scientists. Academic Press, Inc. (1992)
33. Stewart, W.J.: Introduction to the Numerical Solution of Markov Chains. Princeton University Press (1994)