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Evolutionary Approach to Gene Regulatory Networks

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Evolutionary Approach to Machine Learning and Deep Neural Networks

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Abstract

Gene regulatory networks (GRNs) described in this chapter are recently attracting attention as a model that can learn in a way similar to neural networks. Gene regulatory networks express the interactions between genes in an organism. We first give several inference methods to GRN. Then, we explain the real-world application of GRN to robot motion learning. We show how GRNs have generated effective motions to specific humanoid tasks. Thereafter, we explain ERNe (Evolving Reaction Network), which produces a type of genetic network suitable for biochemical systems. ERNe’s effectiveness is shown by several in silico and in vitro experiments, such as oscillator syntheses, XOR problem solving, and inverted pendulum task.

All life is problem solving. I have often said that from the amoeba to Einstein there is only one step.

(Karl Popper)

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Notes

  1. 1.

    In this field, the process in which organisms occur is researched from an evolutionary perspective. Its purpose is a comprehensive and empirical understanding of how systems and individuals emerge.

  2. 2.

    This is to utilize a structure that already exists for a new function. For example, it is thought that the feathers of a bird from a dinosaur that evolved with the objective of retaining warmth are used for flying later with their evolution to wings.

  3. 3.

    This is referred to as the edge of chaos, and is the hypothesis that life has evolved in those regions.

  4. 4.

    All of the transcripts (mRNA) within a cell.

  5. 5.

    http://dreamchallenges.org/challenges/.

  6. 6.

    http://dreamchallenges.org/challenges/.

  7. 7.

    Red Queen effect is an evolutionary type of arms race. The name is derived from Alice’s Adventures in Wonderland. See also xx p.

  8. 8.

    Erne is also another name for a sea eagle.

  9. 9.

    A robust DNA-enzyme oscillator.

  10. 10.

    nM is a unit of concentration in chemistry, where 1 [nM] \(=\) 1 \(\times \) 10\(^{-9}\) [M].

  11. 11.

    The thermal cycler in the figure is a device that duplicates DNA fragments.

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Authors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Hitoshi Iba

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  1. Hitoshi Iba
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Correspondence to Hitoshi Iba .

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Iba, H. (2018). Evolutionary Approach to Gene Regulatory Networks. In: Evolutionary Approach to Machine Learning and Deep Neural Networks. Springer, Singapore. https://doi.org/10.1007/978-981-13-0200-8_5

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  • DOI: https://doi.org/10.1007/978-981-13-0200-8_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0199-5

  • Online ISBN: 978-981-13-0200-8

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