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. 2019 Jun 10;374(1774):20180370.
doi: 10.1098/rstb.2018.0370.

Plant behaviour in response to the environment: information processing in the solid state

Affiliations

Plant behaviour in response to the environment: information processing in the solid state

Salva Duran-Nebreda et al. Philos Trans R Soc Lond B Biol Sci. .

Abstract

Information processing and storage underpins many biological processes of vital importance to organism survival. Like animals, plants also acquire, store and process environmental information relevant to their fitness, and this is particularly evident in their decision-making. The control of plant organ growth and timing of their developmental transitions are carefully orchestrated by the collective action of many connected computing agents, the cells, in what could be addressed as distributed computation. Here, we discuss some examples of biological information processing in plants, with special interest in the connection to formal computational models drawn from theoretical frameworks. Research into biological processes with a computational perspective may yield new insights and provide a general framework for information processing across different substrates. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Keywords: biological computation; computational networks; connectome; information processing; plant.

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Conflict of interest statement

We declare we have no competing interests.

Figures

Figure 1.
Figure 1.
Information processing loop in multicellular plants. Environmental cues are integrated and prompt changes in plant behaviour in the form of developmental transitions and organogenesis. The creation of new organs, the substrates of computation in plants, in turn feed back onto system-level information processing. An indeterminate loop forms by information processing, leading to the creation of new organs, which in turn process information themselves.
Figure 2.
Figure 2.
(a) Single cells are able to perform computations using their intracellular dynamics. These are integrated at the higher scale in the context of a tissue (b) by means of aggregation. The aggregation process makes use of the network structural template in which transport takes place. (c) Network structural template depiction of the tissue in (b). (Online version in colour.)
Figure 3.
Figure 3.
Multicellular information processing in plants. (a) Stomata (dark cells) are dynamically open and closed in order to capture CO2 and avoid excessive loss of water. (b) Thermal images of leaf surfaces showing the current state of stomata aperture within sectors. Patches of coordinated stomata activity are seen, indicating that collective dynamics of stomata are present in the form of excitable media-like waves that propagate through the leaf surface. (c) The hormone metabolic network underpinning the regulation of abscisic acid (ABA) and gibberellic acid (GA) levels in dormant Arabidopsis seeds. (d) Distribution of ABA and GA synthesis and response within distinct cell types of the dormant embryo radicle. (e) Spatial sites of ABA and GA responses within the dormant Arabidopsis embryo. (f) Attractor dynamics of the hormone metabolic interaction network in dormant Arabidopsis seeds when the distinct spatial embedding of hormone responses is taken into account. (a,b) taken from [45] and (f) from [46].
Figure 4.
Figure 4.
Multicellular information processing in plants. (a) In birch, SAMs can be formed of a collection of isolated cells, or a fully connected and communicating tissue, depending on environmental cues. (b) A fragmented system does not promote the growth activation of the meristem, thus following a different decision-making scheme from a fully connected SAM. (c) FPGA design with multiple PUs that can be dynamically rearranged in order to manipulate the computation. (d) Glutamate mediates calcium waves that rapidly propagate in Arabidopsis thaliana. These waves make use of topological shortcuts in organ design. (e) Degree distributions in Caenorhabditis elegans neurons and A. thaliana hypocotyl cells. Although showing similar average values, C. elegans neurons show a long-tailed distribution compared with the lattice-like Arabidopsis hypocotyl. Greater cell degree is possible owing to the intricate shapes that neuronal animal cells can attain. (a) taken from [63], and (d) taken from [67]. (Online version in colour.)

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