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Regulatory Networks

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Bioinformatics

Part of the book series: Computational Biology ((COBO))

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Abstract

This chapter elaborates on the overview presented in Chapter 3. Gene regulatory networks are complex systems that control the expression of genes in response to environmental cues. These networks can be quantified by looking at how the network elements interact with each other, and how the network elements can be tuned to optimize a desired outcome. Trade-offs between different forms of regulation can be quantified by looking at the effects on gene expression, or the number of regulatory proteins needed to achieve a desired outcome; trade-offs between different types of regulatory strategies can be quantified by looking at the efficiency of the network; that is, how well the system controlled by the network is able to respond to environmental changes, and how much energy and other resources are needed to maintain the network. This chapter necessarily deals with the interactome—the network of protein–protein and protein–nucleic acid interactions—that underpin the regulatory networks. Biophysicochemical aspects of the interactions, including specificity and cooperative binding, are discussed, followed by an account of the various in vivo and in vitro (including chromatography and biosensing) experimental methods that can be used to gather the primary data on interactions, from which interactomic inferences may follow. Network modelling is covered, including analysis of an operon. Data collection and analysis of metabolism (the metabolome) is covered, including metabolic control analysis.

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Notes

  1. 1.

    Norris et al. (1996).

  2. 2.

    de Jong (2002).

  3. 3.

    Yahyanejad et al. (2019).

  4. 4.

    Baker et al. (1997). This work, incidentally, also demonstrates how a rational understanding of a regulatory network can lead to practical guidance for designing crop protection strategies.

  5. 5.

    Eukaryotic cells in particular are in a great deal more structured than the simple picture suggests: Filaments of various kinds (e.g., microtubules) appear to function inter alia as tracks along which certain molecules are transported to specific destinations. However, even in this case, the information-bearing (“signalling”) molecule has first to encounter, and bind to, the carrier molecule that will convey it along the track.

  6. 6.

    McConkey (1982) has coined the term “quinary structure” (of proteins) for this web of interactions.

  7. 7.

    Wilson et al. (2018).

  8. 8.

    This statement, the obvious corollary of the central dogma, is actually quite problematical—in the sense of having a rather ambiguous meaning—when scrutinized in detail. Many functionally relevant proteins are significantly modified (e.g., glycosylated) by enzymes after translation. Of course, the enzymes themselves are gene products.

  9. 9.

    Schlitt and Brazma (2005).

  10. 10.

    Many transcription factors, for example, are multiprotein complexes.

  11. 11.

    After Vohradský (2001).

  12. 12.

    After tackling the problem, look at Wang et al. (2011) and Stanton et al. (2014) for actual experimental work on this problem.

  13. 13.

    Groups of operons controlled by a single transcription factor are called regulons; groups of regulons are called modulons.

  14. 14.

    Lugagne et al. (2017).

  15. 15.

    Mackey and Glass (1977).

  16. 16.

    The paper by Mackey and Glass (loc. cit.) can be consulted for physiological context. For a more general exposition of how feedback delay can lead to chaos, see Pippard (1985).

  17. 17.

    Vu and Vohradský (2007).

  18. 18.

    Alter et al. (2000).

  19. 19.

    Golub and Van Loan (2013), Sect. 2.4.

  20. 20.

    Hore et al. (2016).

  21. 21.

    Hu et al. (2019).

  22. 22.

    In the literature, K is often loosely defined using Eq. (23.7) with concentrations rather than mole fractions, whereupon it loses its dimensionless quality.

  23. 23.

    Remarkable specificity is achievable (see, e.g., Popescu and Misevic 1997).

  24. 24.

    The dehydron (Sect. 15.5.2) is an underwrapped (i.e., underdesolvated) hydrogen bond and is a key determinant of protein affinity. See also Fernández (2015).

  25. 25.

    See, e.g., Ramsden (1984; 1986).

  26. 26.

    Hydrogen bonding is a special example of Lewis acid–base (AB) or electron donor–acceptor (da) interactions.

  27. 27.

    See Ramsden (2000).

  28. 28.

    See, e.g., Kornyshev and Leikin (2001).

  29. 29.

    E.g. Ramsden and Dreier (1996); see Ramsden and Grätzel (1986) for a nonbiological example of the effect of dimensional reduction from 3 to 2.

  30. 30.

    See Ramsden (1994) for a comprehensive survey of all these techniques and others.

  31. 31.

    Kozma et al. (2009).

  32. 32.

    A popular way to avoid the bioincompatibility of the gold or silver surface of the transducer required with SPR has been to coat it with a thick (\(\sim \negmedspace 200\) nm) layer of a biocompatible polysaccharide such as dextran, which forms a hydrogel, to which the target protein is bound. Unfortunately, this drastically changes the transport properties of the solution in the vicinity of the target (bound) protein (see Schuck 1996), which can lead to errors of up to several orders of magnitude in apparent binding constants (via a differential effect on \(k_\textrm{a}\) and \(k_\textrm{d}\)). Furthermore, such materials interact very strongly (via hydrogen bonds) with water, altering its hydrophilicity, with concomitant drastic changes to protein affinity, leading to further, possibly equally large, distortions in binding constant via its link to the free energy of interaction (\(\Delta G = -RT\ln K\)).

  33. 33.

    See Sect. 18.5; the immobilization of proteins without altering their conformation, and hence association characteristics, is however more difficult than for nucleic acid oligomers.

  34. 34.

    See also Sect. 18.1 for limitations of the technique.

  35. 35.

    Jeong et al. (2001).

  36. 36.

    Cyclic adenosine monophosphate.

  37. 37.

    Guanosine monophosphate.

  38. 38.

    The official classification of enzyme function is that of the Enzyme Commission (EC), which recognizes six main classes: 1, oxidoreductases; 2, transferases; 3, hydrolases; 4, lyases; 5, isomerases; and 6, ligases. The main class number is followed by three further numbers (separated by points), whose significance depends on the main class. For class 1, the second number denotes the substrate and the third number denotes the acceptor; whereas for class 3, the second number denotes the type of bond cleaved and the third number denotes the molecule in which that bond is embedded. For all classes, the fourth number signifies some specific feature such as a particular cofactor.

  39. 39.

    These correlations are crucial for understanding the links between genome and epigenetics.

  40. 40.

    See Voigt and Matt (2004) for some insight into this question.

  41. 41.

    See, e.g., Tkemaladze (2002).

  42. 42.

    See also Schuster et al. (2000).

  43. 43.

    But see Wolkenhauer et al. (2005).

  44. 44.

    See Wagner and Fell (2001) or Raine and Norris (2002).

  45. 45.

    Theorell and Stelling (2022).

  46. 46.

    Shlomi et al. (2008).

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  1. Department of Biomedical Research, The University of Buckingham, Buckingham, UK

    Jeremy Ramsden

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Ramsden, J. (2023). Regulatory Networks. In: Bioinformatics. Computational Biology. Springer, Cham. https://doi.org/10.1007/978-3-030-45607-8_23

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  • DOI: https://doi.org/10.1007/978-3-030-45607-8_23

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