Abstract
We discuss the implementation and results of an evolutionary algorithm designed to generate oscillating biological networks. In our algorithm we have used a type of fitness function which defines oscillations independent of amplitude and period, which improves results significantly when compared to a simple fitness function which only measures the distance to a predefined target function. We show that with our fitness function, we are able to conduct an analysis of minimal oscillating motifs. We find that there are several different examples of mechanisms that generate oscillations, which make use in various ways of transcriptional regulations, complex formation and catalytic degradation.
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van Dorp, M., Lannoo, B., Carlon, E. (2013). Evolutionary Generation of Small Oscillating Genetic Networks. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_13
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DOI: https://doi.org/10.1007/978-3-642-37213-1_13
Publisher Name: Springer, Berlin, Heidelberg
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