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Gene Coexpression as Hebbian Learning in Prokaryotic Genomes

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Abstract

Biological interaction networks represent a powerful tool for characterizing intracellular functional relationships, such as transcriptional regulation and protein interactions. Although artificial neural networks are routinely employed for a broad range of applications across computational biology, their underlying connectionist basis has not been extensively applied to modeling biological interaction networks. In particular, the Hopfield network offers nonlinear dynamics that represent the minimization of a system energy function through temporally distinct rewiring events. Here, a scaled energy minimization model is presented to test the feasibility of deriving a composite biological interaction network from multiple constituent data sets using the Hebbian learning principle. The performance of the scaled energy minimization model is compared against the standard Hopfield model using simulated data. Several networks are also derived from real data, compared to one another, and then combined to produce an aggregate network. The utility and limitations of the proposed model are discussed, along with possible implications for a genomic learning analogy where the fundamental Hebbian postulate is rendered into its genomic equivalent: Genes that function together junction together.

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References

  • Achtman, M., & Wagner, M. (2008). Microbial diversity and the genetic nature of microbial species. Nat. Rev. Microbiol., 6, 431–440.

    Google Scholar 

  • Anderson, J. A. (1995). An introduction to neural networks. Cambridge: MIT Press. 650 pp.

    MATH  Google Scholar 

  • Balázsi, G., Barabási, A. L., & Oltvai, Z. N. (2005). Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. Proc. Natl. Acad. Sci. USA, 102, 7841–7846.

    Article  Google Scholar 

  • Bansal, M., Belcastro, V., Ambesi-Impiombato, A., & di Bernardo, D. (2007). How to infer gene networks from expression profiles. Mol. Syst. Biol., 3, 78.

    Article  Google Scholar 

  • Barabási, A. L. (2007). Network medicine—from obesity to the “diseasome”. N. Engl. J. Med., 357, 404–407.

    Article  Google Scholar 

  • Barabási, A. L., & Oltvai, Z. N. (2004). Network biology: understanding the cell’s functional organization. Nat. Rev. Genet., 5, 101–113.

    Article  Google Scholar 

  • Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nat. Rev. Genet., 12, 56–68.

    Article  Google Scholar 

  • Castelo, R., & Roverato, A. (2009). Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J. Comput. Biol., 16, 213–227.

    Article  MathSciNet  Google Scholar 

  • Cho, K. H., Choo, S. M., Jung, S. H., Kim, J. R., Choi, H. S., & Kim, J. (2007). Reverse engineering of gene regulatory networks. IET Syst. Biol., 1, 149–163.

    Article  Google Scholar 

  • Chung, F., Lu, L., Dewey, T. G., & Galas, D. J. (2003). Duplication models for biological networks. J. Comput. Biol., 10, 677–687.

    Article  Google Scholar 

  • Dandekar, T., Snel, B., Huynen, M., & Bork, P. (1998). Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci., 23, 324–328.

    Article  Google Scholar 

  • DeRisi, J. L., Iyer, V. R., & Brown, P. O. (1997). Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278, 680–686.

    Article  Google Scholar 

  • Dobrin, R., Beg, Q. K., Barabási, A. L., & Oltvai, Z. N. (2004). Aggregation of topological motifs in the Escherichia coli transcriptional regulatory network. BMC Bioinform., 5, 10.

    Article  Google Scholar 

  • Doidge, N. (2007). The brain that changes itself: stories of personal triumph from the frontiers of brain science. New York: Penguin Books. 427 pp.

    Google Scholar 

  • Enright, A., Iliopoulos, I., Kyrpides, N., & Ouzounis, C. (1999). Protein interaction maps for complete genomes based on gene fusion events. Nature, 402, 86–90.

    Article  Google Scholar 

  • Farkas, I., Jeong, H., Vicsek, T., Barabási, A. L., & Oltvai, Z. N. (2003). The topology of the transcription regulatory network in the yeast Saccharomyces cerevisiae. Physica A, 318, 601–612.

    Article  Google Scholar 

  • Fraser, C., Alm, E. J., Polz, M. F., Spratt, B. G., & Hanage, W. P. (2009). The bacterial species challenge: making sense of genetic and ecological diversity. Science, 323, 741–746.

    Article  Google Scholar 

  • Gerdes, S. Y., Scholle, M. D., Campbell, J. W., Balázsi, G., Ravasz, E., Daugherty, M. D., Somera, A. L., Kyrpides, N. C., Anderson, I., Gelfand, M. S., Bhattacharya, A., Kapatral, V., D’Souza, M., Baev, M. V., Grechkin, Y., Mseeh, F., Fonstein, M. Y., Overbeek, R., Barabási, A. L., Oltvai, Z. N., & Osterman, A. L. (2003). Experimental determination and system level analysis of essential genes in Escherichia coli MG1655. J. Bacteriol., 185, 5673–5684.

    Article  Google Scholar 

  • Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A. L. (2007). The human disease network. Proc. Natl. Acad. Sci. USA, 104, 8685–8690.

    Article  Google Scholar 

  • Hamming, R. W. (1950). Error detecting and error correcting codes. Bell Syst. Tech. J., 29, 147–160.

    Article  MathSciNet  Google Scholar 

  • Hebb, D. O. (1949). The organization of behavior. New York: Wiley. 335 pp.

    Google Scholar 

  • Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA, 79, 2554–2558.

    Article  MathSciNet  Google Scholar 

  • Hopfield, J. J. (1984). Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. USA, 81, 3088–3092.

    Article  Google Scholar 

  • Hubble, J., Demeter, J., Jin, H., Mao, M., Nitzberg, M., Reddy, T. B., Wymore, F., Zachariah, Z. K., Sherlock, G., & Ball, C. A. (2009). Implementation of GenePattern within the Stanford Microarray Database. Nucleic Acids Res., 37, D898–D901 (Database issue).

    Article  Google Scholar 

  • Jennings, N. R. (2000). On agent-based software engineering. Artif. Intell., 117, 277–296.

    Article  MATH  Google Scholar 

  • Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A. L. (2000). The large-scale organization of metabolic networks. Nature, 407, 651–654.

    Article  Google Scholar 

  • Kaye, R., & Wilson, R. (1998). Linear algebra. New York: Oxford University Press. 230 pp.

    MATH  Google Scholar 

  • Medini, D., Donati, C., Tettelin, H., Masignani, V., & Rappuoli, R. (2005). The microbial pan-genome. Curr. Opin. Genet. Dev., 15, 589–594.

    Article  Google Scholar 

  • Mira, A., Martín-Cuadrado, A. B., D’Auria, G., & Rodríguez-Valera, F. (2010). The bacterial pan-genome: a new paradigm in microbiology. Int. Microbiol., 13, 45–57.

    Google Scholar 

  • Park, J., Lee, D. S., Christakis, N. A., & Barabási, A. L. (2009). The impact of cellular networks on disease comorbidity. Mol. Syst. Biol., 5, 262.

    Article  Google Scholar 

  • Podani, J., Oltvai, Z. N., Jeong, H., Tombor, B., Barabási, A. L., & Szathmáry, E. (2001). Comparable system-level organization of Archaea and Eukaryotes. Nat. Genet., 29, 54–56.

    Article  Google Scholar 

  • Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A. L. (2002). Hierarchical organization of modularity in metabolic networks. Science, 297, 1551–1555.

    Article  Google Scholar 

  • Sasai, M., & Wolynes, P. G. (2003). Stochastic gene expression as a many-body problem. Proc. Natl. Acad. Sci. USA, 100, 2374–2379.

    Article  Google Scholar 

  • Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L., & Ideker, T. (2011). Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics, 27, 431–432.

    Article  Google Scholar 

  • Sun, M. G., & Kim, P. M. (2011). Evolution of biological interaction networks: from models to real data. Genome Biol., 12, 235.

    Article  Google Scholar 

  • Tettelin, H., Riley, D., Cattuto, C., & Medini, D. (2008). Comparative genomics: the bacterial pan-genome. Curr. Opin. Microbiol., 12, 472–477.

    Article  Google Scholar 

  • Vey, G., & Moreno-Hagelsieb, G. (2009). Alice in Wonderland or biology among the computational sciences. Int. J. Integr. Biol., 6, 99–104.

    Google Scholar 

  • Vidal, M., Cusick, M. E., & Barabási, A. L. (2011). Interactome networks and human disease. Cell, 144, 986–998.

    Article  Google Scholar 

  • Yildirim, M. A., Goh, K. I., Cusick, M. E., Barabási, A. L., & Vidal, M. (2007). Drug-target network. Nat. Biotechnol., 25, 1119–1126.

    Article  Google Scholar 

  • Yook, S. H., Oltvai, Z. N., & Barabási, A. L. (2004). Functional and topological characterization of protein interaction networks. Proteomics, 4, 928–942.

    Article  Google Scholar 

  • Zaknich, A. (2003). Neural networks for intelligent signal processing. Singapore: World Scientific. 508 pp.

    Book  MATH  Google Scholar 

  • Zhu, X., Gerstein, M., & Snyder, M. (2007). Getting connected: analysis and principles of biological networks. Genes Dev., 21, 1010–1024.

    Article  Google Scholar 

Download references

Acknowledgements

The author acknowledges support from an NSERC Strategic Grant awarded to Trevor Charles at the University of Waterloo. The author thanks Trevor Charles and Mélanie Lafrance for reviewing the manuscript.

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  1. Department of Biology, University of Waterloo, 200 University Ave. W., Waterloo, ON, Canada, N2L 3G1

    Gregory Vey

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  1. Gregory Vey
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Correspondence to Gregory Vey.

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Vey, G. Gene Coexpression as Hebbian Learning in Prokaryotic Genomes. Bull Math Biol 75, 2431–2449 (2013). https://doi.org/10.1007/s11538-013-9900-z

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  • DOI: https://doi.org/10.1007/s11538-013-9900-z

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