Biomedical Engineering Reference
In-Depth Information
determine gene function [ 29 ] to define a family of BNs. Chap. 4 presents a method
for inferring and ranking gene predictors from continuous gene expression data using
modified Zhegalkin functions. Lastly, in Chap. 5, we use ATPG techniques on the
BN [ 30 ], [ 31 ] to identify genes leading to cancer and to determine drug selection for
cancer therapy.
References
1. Alberts, B., Bray, D., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: Essential Cell
Biology: An Introduction to the Molecular Biology of the Cell. Garland Publishing Inc. (1997)
2. Datta, A., Dougherty, E.R.: Introduction to Genomic Signal Processing with Control. CRC
Press (2007)
3. Saenger, W.: Principles of Nucleic Acid Structure. Springer-Verlag (1983)
4. Gehring, W.J.: The master control gene for morphogenesis and evolution of the eye. Genes.
Cells. 1 , 11-15 (1996).
5. Ashley-Koch, A., Yang, Q., Olney, R.R.: Sickle hemoglobin (hb s) allele and sickle cell disease:
A huGe review. American. J. Epidemiol. 151 (9), 839-845 (2000)
6. Mitchell, P., Tjian, R.: Transcriptional regulation in mammalian cells by sequence-specific
DNA binding proteins. Sci. 245 , 371-378, (1989)
7. Ripley, B.D.: The R project in statistical computing MSOR Connections. The newsletter of
the. LTSN. Maths Stats Netw. 1 (1), 23-25 (2001)
8. Kim, S., Li, H., Dougherty, E.R., Cao, N., Chen, Y., Bittner, M., Suh, E.B.: Can Markov chain
models mimic biological regulation? J. Biol. Syst. 10 (4), 337-357 (2002)
9. Vahedi, G., Faryabi, B., Chamberland, J.-F., Datta, A., Dougherty, E.R.: Intervention in gene
regulatory networks via a stationary mean-first-passage-time control policy. Biomed. Engg.
IEEE. Transact. 55 (10), 2319-2331, (2008)
10. Chen, T., He, H.L., Church, G.M., et al.: Modeling gene expression with differential equations.
Pac. Symp. Biocomput. 4 (4) (1999)
11. Wu,F.X., Zhang, W.J., Kusalik, A.J.: Modeling gene expression from microarray expression
data with state-space equations. Pacific. Symposium. Biocomput. 9 , 581-592 (2004)
12. Kauffman, S. A., Metabolic stability and epigenesis in randomly constructed genetic nets. J.
Theoreti. Biolo. 22 (3), 437-467 (1969)
13. Shmulevich, I., Dougherty, E.R.: Probabilistic Boolean Networks: The Modeling and Con-
trol of Gene Regulatory Networks, SIAM—Society for Industrial and Applied Mathematics.
Philadelphia, PA (2009)
14. Geard,N., Wiles, J.: A gene network model for developing cell lineages. Artif. Life 11 (3)
249-268 (2005)
15. Arkin, A., Ross, J., McAdams,H.H.: Stochastic kinetic analysis of developmental pathway
bifurcation in phage lambda-infected escherichia coli cells. Genetics. 149 , 1633-1648 (1998)
16. Shalgi, R., Lieber, D., Oren, M., Pilpel, Y.. Global and local architecture of the mammalian
microRNA-transcription factor regulatory network. PLoS. Computati. Biolo. 3 (7) e131 (2007)
17. Voinnet, O.: Origin, biogenesis, and activity of plant microRNAs. Cell. 136 (4), 669-687 (2009)
18. Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Sci. Signall.
296 (5569), 910 (2002)
19. Jacob, F., Monod, J.: Genetic regulatory mechanisms in the synthesis of proteins. J. Molecul.
Biolo. 3 (3), 318-356 (1961)
20. Burke, W., Psaty, B.M.:, Personalized Medicine in the Era of Genomics. JAMA 298 (14),
1682-1684 (2007)
21. Teutsch, M., et al.: The Evaluation of Genomic Applications in Practice and Prevention
(EGAPP) Initiative: Methods of the EGAPP working group. Genet. Medic. 11 (1), 3-14 (2009)
22. Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving. Commun.
ACM. 5 (7), 394-397 (1962)
23. Niklas, E., Niklas, S.: The minisat page. http://minisat.se/. Accessed 6 Apr 2010
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