16 April 2010
Department of Plant Sciences
University of Oxford
Networks are common within biological systems and have been characterised in a range of different contexts that include metabolism, protein-protein interaction, neuronal circuits and ecological food webs. However, one area that has received relatively little attention is analysis of organisms that actually develop as a growing self-organised network that exploits patchy and ephemeral resources.
We are beginning to develop techniques to analyse how these networks resolve the conflicting demands of exploration, exploitation, transport and resilience to predation or random damage using a combination of live-cell imaging techniques, network analysis and mathematical modelling .
Even at this stage, some common features of biological network formation seem to emerge. Fungal networks are constructed by local iterative developmental processes rather than centralized control, with growth involving over-production of links and nodes, followed by selective pruning of some links and reinforcement of others [2,3]. Such a process mimics the process of Darwinian evolution in which natural selection removes less fit offspring. This "Darwinian network model" may represent a generalized model for growth of physical biological networks. The generic ingredients include a non-linear positive reinforcement term related to the local flux and a linear decay term . This framework is reminiscent of reaction-diffusion systems, but constrained to operate within a network.
Networks involving physical flows also obey continuity equations and are therefore intrinsically coupled such that each part of the network is influenced by and can influence the whole network, but without any global assessment of behaviour. Useful properties of the network may emerge from the interaction between the local update rules governing topology and flows without the need for long-distance communication or calculation of aggregate properties of the network .
The third general observation on these biological networks is the prevalence of some form of synchronised oscillatory processes . In other contexts, such as supply chains or traffic flow, the existing strategy is to minimize oscillations to achieve maximum throughput. This suggests that either the biological systems lack the additional sensory and feedback systems to suppress oscillations, or that maintaining an oscillatory system is an alternative means to achieve a stable long-term quasi-optimal solution, potentially with less control infrastructure.
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