Abstract
Many different models of genetic regulatory networks (GRN) exist, but most of them are focused on off-line processing, so that important features of real networks, like adaptive and non-stationary characterare missed. Interdisciplinary insight into the area of self-organization within the living organisms has caused some interesting new thoughts, and the suggested model is among them. Based on reinforcement learning of the Boolean network with random initial structure, the model is searching for a specialized network, that agrees with experimentally obtained data from the real GRN. With some experiments of real biological networks we investigate its behaviour.
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Šter, B., Dobnikar, A. (2013). Reinforcement Learning and Genetic Regulatory Network Reconstruction. 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_25
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DOI: https://doi.org/10.1007/978-3-642-37213-1_25
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