A Genetic Classifier Account for the Regulation of Expression - Computational Neuroscience - page 123

Alternatively, if a gene has been artificially inserted but its products are not suf-
ficiently expressed, it may be possible to inhibit native genes. This can be achieved
by introducing protein products to match product patterns of the native genes.
Lastly, since promoters are distributed across genes, this system reveals how
copied genes can integrate into the genome while still being closely regulated.
In summary, this chapter outlines the concept and implications of regulatory feed-
back systems that maintain homeostasis and explores their systematic properties.
This model suggests speculative methods to control gene expression by manipulat-
ing shared molecular pathways.
Acknowledgments I would like to thank Eyal Amir, Satwik Rajaram, Frances R. Wang, Tal Raveh,
Cyrus Omar, and Robert Lee DeVille for helpful suggestions. This work was supported by the US
National Geospatial Agency Grant HM1582-06-BAA-0001.
References
1. Achler, T. Input shunt networks. Neurocomputing 44 , 249-255 (2002)
2. Achler, T. Object classification with recurrent feedback neural networks. Proceedings of
the SPIE Evolutionary and Bio-inspired Computation: Theory and Applications, vol. 6563,
Orlando (2007)
3. Achler, T., Amir, E. Input feedback networks: Classification and inference based on network
structure. Artif General Intell 1 , 15-26 (2008)
4. Achler, T., Omar, C., Amir, E. Shedding weights: More with less. Proceedings of the
2008 IEEE International Joint Conference on Neural Networks (IJCNN'08), Hong Kong,
pp. 3020-3027 (2008)
5. Albert, R., Jeong, H., Barabasi, A.L. Error and attack tolerance of complex networks. Nature
406 (6794), 378-382 (2000)
6. Kauffman, S., et al. Random Boolean network models and the yeast transcriptional network.
Proc Natl Acad Sci USA 100 (25), 14796-14799 (2003)
7. Mcfadden, F.E. Convergence of competitive activation models based on virtual lateral inhibi-
tion. Neural Networks 8 (6), 865-875 (1995)
8. Nochomovitz, Y.D., Li, H. Highly designable phenotypes and mutational buffers emerge from
a systematic mapping between network topology and dynamic output. Proc Natl Acad Sci
USA 103 (11), 4180-4185 (2006)
9. Nykter, M., et al. Critical networks exhibit maximal information diversity in structure-
dynamics relationships. Phys Rev Lett 100 (5), 058702 (2008)
10. Shmulevich, I., et al. The role of certain Post classes in Boolean network models of genetic
networks. Proc Natl Acad Sci USA 100 (19), 10734-10739 (2003)