Abstract
After the sequencing of whole genomes and the identification of the genes contained in them, one of the main challenges remaining is to understand the mechanisms that regulate the expression of genes within the genome in order to gain knowledge about structural, biochemical, physiological and behavioral characteristics of organisms. Some of these mechanisms are controlled by so-called Genetic Regulatory Networks (GRNs). Boolean networks can help model biological GRNs. In this paper, a genetic algorithm is used to make inferences in Boolean networks, in combination with the Quine-McCluskey algorithm, when not all the output states of the genes have been determined. This lack of information could be treated as “don’t care” states. Genetic algorithms are useful in multi-objective optimization problems, such as minimization of Gene Regulatory Functions, where it is important not only to have the smallest quantity of disjunctions, but also the smallest quantity of genes involved in the regulation.
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References
Chen, T., Filkov, V., Skiena, S.: Identifying gene regulatory networks from experimental data. In: Proceedings of the Third Annual International Conference on Computational Molecular Biology (RECOMB 1999), pp. 94–103. ACM, New York (1999)
De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 9(1), 67–103 (2002)
Cho, R., Campbell, M.A.: Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle. Molecular Cell 2, 65–73 (1998)
Kim, H., Lee, J.K., Park, T.: Boolean networks using the chi-square test for inferring large-scale gene regulatory networks. BMC Bioinformatics 8, 37 (2007)
Shmulevich, I.: Binary Analysis and Optimization-Based Normalization of Gene Expression Data. Bioinformatics 18(4), 555–565 (2002)
Kauffmann, S.A.: Metabolic Stability and Epigenesis in Randomly Constructed Genetic Nets. J. Theoret. Biol. 22, 437–467 (1969)
Xiao, Y.: A Tutorial on Analysis and Simulation of Boolean Gene Regulatory Network Models. Current Genomics 10, 511–525 (2009)
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., Guthke, R.: Gene regulatory network inference: data integration in dynamic models-a review. Biosystems 96(1), 86–103 (2009)
Xiao, Y., Dougherty, E.: Optimizing Consistency-Based Design of Context-Sensitive Gene Regulatory Networks. IEEE Transactions on Circuits and Systems 53(11), 2431–2437 (2006)
Quine, W.V.: The Problem of Simplifying Truth Functions. Am. Math. Monthly 59, 521 (1952)
Quine, W.V.: A Way to Simplify Truth Functions. Am. Math. Monthly 62, 627 (1955)
McCluskey, E.J.: Minimization of Boolean Functions. Bell Syst. Tech. J. 35, 1417 (1956)
Liang, S.: REVEAL, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures. In: Pacific Symposium on Biocomputing, vol. 3, pp. 18–29 (1998)
Shannon, C.E., Weaver, W.: The mathematical theory of communication. University of Illinois Press (1963)
Popov, A., Filipova, K.: Genetic Algorithms Synthesis of Finite State Machines. In: Proceedings of the 27th Spring Seminar on Electronics Technology, pp. 388–392 (2004)
Mihailov, S., Popov, A., Filipova, K., Kasev, N.: Comparative Analysis of Boolean Functions Minimization in Terms of Symplifying the Synthesis. In: First International Congress of Mechanical and Electrical Engineering and Technologies, pp. 273–276 (2002)
Bittner, M.L., Meltzer, P.: Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536–540 (2000)
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Chavez-Alvarez, R., Chavoya, A., Lopez-Martin, C. (2012). Finding the Minimal Gene Regulatory Function in the Presence of Undefined Transitional States Using a Genetic Algorithm. In: Lones, M.A., Smith, S.L., Teichmann, S., Naef, F., Walker, J.A., Trefzer, M.A. (eds) Information Processign in Cells and Tissues. IPCAT 2012. Lecture Notes in Computer Science, vol 7223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28792-3_29
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DOI: https://doi.org/10.1007/978-3-642-28792-3_29
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