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The connectionist framework for gene regulation

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Abstract

I show that gene regulation networks are qualitatively consistent and therefore sufficiently similar to linearly seperable connectionist networks to warrant that the connectionist framework be applied to gene regulation. On this view, natural selection designs gene regulation networks to overcome the difficulty of development. I offer some general lessons about their evolvability that can be learned by examining the generic features of connectionist networks.

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Notes

  1. Here I distinguish designing and building a living form from scratch (as we do space shuttles) from such practices as genetic engineering, where we merely manipulate the systems designed by natural selection.

  2. See White et al. (1999); Iyer et al. (2001); Kayo et al. (2001); and Arbeitman et al. (2002) for examples of the variance of gene expression across life cycle, cell type, physiological condition, and environmental condition.

  3. This tradition ignores a number of complexities in regulation of protein production. DNA methylation prevents transcription of specific coding regions and can be seen as removing nodes from the network and meiotic mutation, which may result in a slightly different gene regulation network for some cells in an organism. Both meiotic mutations and methylation patterns are passed on to daughter cells during development. I think that this is a justifiable simplification because differently methylated networks in an organism may still have many nodes and connections in common (i.e., all gene methylated gene regulation networks are highly constrained because they share the same genome) and most regulation of gene expression occurs at the rate of transcription initiation (Latchman 1998; Carey and Smale 2000; Lemon and Tjian 2000; White 2001; Wray et al. 2002). Alternative splicing of RNA also complicates matters because it allows the same regulatory region to be responsible for the regulation of different proteins.

  4. The input node is active in 50% of all possible inputs, and 50% of those are half of a pair that is sensitive to that input node.

  5. For example, Fig. 7 represents a function that is also qualitatively consistent. In the context of the absence of Hunchback and knirps, the presence of Bicoid results in Krüppel expression (see Fig. 7, rows 1 and 3) and in no context does the absence of Bicoid result in Krüppel expression and its presence result in Krüppel inexpression (see rows 2 and 4, 5 and 7, and 6 and 8). Therefore Bicoid qualitatively consistently encourages Krüppel expression. In addition, Hunchback and knirps each discourage Krüppel expression (see rows 2 and 6, and rows 3 and 4, respectively), and do so in a qualitatively consistent way (see also rows 1 and 5, 3 and 7, 4 and 8, and rows 1 and 2, 5 and 6, and 7 and 8, respectively).

  6. I focused on mutations that add and remove nodes to better explain qualitative consistency.

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Acknowledgments

I acknowledge the contributions of the anonymous referees, Colin Allen, John Roberts, John Scroggs, and Kim Sterelny to this paper.

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  1. Department of Philosophy, Texas A&M University, College Station, TX, 77843-4237, USA

    Roger Sansom

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Sansom, R. The connectionist framework for gene regulation. Biol Philos 23, 475–491 (2008). https://doi.org/10.1007/s10539-007-9086-6

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