Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks

Jin Y, Sendhoff B (2009)
In: Fuzzy Systems in Bioinformatics and Computational Biology. Jin Y, Wang L (Eds); Studies in Fuzziness and Soft Computing. Berlin, Heidelberg: Springer Berlin Heidelberg: 315-327.

Sammelwerksbeitrag | Veröffentlicht | Englisch
 
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Autor*in
Jin, YaochuUniBi ; Sendhoff, Bernhard
Herausgeber*in
Jin, Yaochu; Wang, Lipo
Abstract / Bemerkung
This chapter investigates empirically the influence of control logic on regulatory dynamics in computational models of genetic regulatory networks. The gene regulatory network motif considered in this work consists of three genes with both positive and negative feedback loops. Two fuzzy logic formulations are studied in this work, one is known as the Zadeh operator, and other is the probabilistic operator. The evolved dynamics of the network motifs is then verified with perturbed initial system states. Our empirical results show that with the probabilistic ‘AND’ operator and the probabilistic ‘OR’ operator, the system is able to evolve sustained oscillation with a low probability. However, sustained oscillation is not evolvable when the Zadeh operator is employed. In addition, we also show that regulatory motifs with the probabilistic operators possess much richer dynamics than that with the Zadeh operators.
Erscheinungsjahr
2009
Buchtitel
Fuzzy Systems in Bioinformatics and Computational Biology
Serientitel
Studies in Fuzziness and Soft Computing
Seite(n)
315-327
ISBN
978-3-540-89967-9
eISBN
978-3-540-89968-6
ISSN
1434-9922
eISSN
1860-0808
Page URI
https://pub.uni-bielefeld.de/record/2978626

Zitieren

Jin Y, Sendhoff B. Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks. In: Jin Y, Wang L, eds. Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing. Berlin, Heidelberg: Springer Berlin Heidelberg; 2009: 315-327.
Jin, Y., & Sendhoff, B. (2009). Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks. In Y. Jin & L. Wang (Eds.), Studies in Fuzziness and Soft Computing. Fuzzy Systems in Bioinformatics and Computational Biology (pp. 315-327). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_16
Jin, Yaochu, and Sendhoff, Bernhard. 2009. “Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks”. In Fuzzy Systems in Bioinformatics and Computational Biology, ed. Yaochu Jin and Lipo Wang, 315-327. Studies in Fuzziness and Soft Computing. Berlin, Heidelberg: Springer Berlin Heidelberg.
Jin, Y., and Sendhoff, B. (2009). “Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks” in Fuzzy Systems in Bioinformatics and Computational Biology, Jin, Y., and Wang, L. eds. Studies in Fuzziness and Soft Computing (Berlin, Heidelberg: Springer Berlin Heidelberg), 315-327.
Jin, Y., & Sendhoff, B., 2009. Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks. In Y. Jin & L. Wang, eds. Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 315-327.
Y. Jin and B. Sendhoff, “Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks”, Fuzzy Systems in Bioinformatics and Computational Biology, Y. Jin and L. Wang, eds., Studies in Fuzziness and Soft Computing, Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp.315-327.
Jin, Y., Sendhoff, B.: Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks. In: Jin, Y. and Wang, L. (eds.) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing. p. 315-327. Springer Berlin Heidelberg, Berlin, Heidelberg (2009).
Jin, Yaochu, and Sendhoff, Bernhard. “Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks”. Fuzzy Systems in Bioinformatics and Computational Biology. Ed. Yaochu Jin and Lipo Wang. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. Studies in Fuzziness and Soft Computing. 315-327.

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