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• W2055013595 abstract "So, naturalists observe, a flea Has smaller fleas that on him prey; And these have smaller still to bite ‘em; And so proceed ad infinitum. (Jonathan Swift: Poetry, a Rhapsody) Swift wrote these words to lampoon the role of the critic in the creative process but he unwittingly epigramised a theme in the emerging field of systems biology. The closer you look at a system the more levels of complexity you see and yet, apart from the scale of the components under observation, similar patterns are encountered again and again. Faced with such recursive intricacy it is less important to completely describe how a system works than to identify what is the least complicated description that explains its behaviour. This idea of the level of detail necessary to understand a systems behaviour was a theme running throughout the New Phytologist Symposium on Networks in Plant Biology held at the Linnean Society at the end of January 2006. The networks under discussion ranged widely in scale from the entire globe to the internal workings of a single cell, but in each case the question arose again and again: how much detail do we need to know about a systems components and how they interact to understand its behaviour? Another unifying feature of the symposium was that the word ‘understanding’ had a subtly different meaning than would have been used by the pedestrians outside strolling along Piccadilly or visiting the Royal Academy of Arts just across the courtyard. For this meeting, no system could be said to be understood without at least the possibility of a quantitative mathematical model predicting its behaviour, even under conditions that are not accessible to experimental observation. The first step in that process is to find out the composition of the system. Biologists have an unrivalled depth of experience in identifying and classifying individual components of a system, and for most plant biologists that means genes and their products. John Turner (University of East Anglia, UK) described how the traditional approach of screening for mutants, in his case those defective in their responses to stress signals, remains potent in identifying where within a network individual components lie. For those with a taste for the broader view the application of high through-put techniques to elucidate network topologies was discussed by both Hamid Boulari (Institute of Systems Biology, Seattle, USA) and Juliette Colinas (Duke University, North Carolina, USA) in the varied systems of the developing sea urchin embryo and the developing root. Juliette emphasised the appeal of the root as a system in which to study development: the result of its development is that it stays the same. Viewed from the outside, roots are constantly elongating, thrusting forward in a quest for water and other resources, but from the point of view of the root tip the initial cells around the quiescent centre are a constant factory for the production of new cells which flow out and back down the root, differentiating and maturing as they go. Systems at steady state are far simpler to describe mathematically than those that are never the same from one moment to the next. Another advantage of roots is that their behaviour has analogues in other systems. Roots grow from their tips, as do the hyphae of fungi, developing nerve cells in mammals, pollen tubes and the root hairs studied by Claire Grierson (University of Bristol, UK). Not only can general principles be carried over from other systems but sometimes even quite detailed models as well. For example, the Bartnicki-Garcia model for fungal hyphal growth (Bartnicki-Garcia et al., 1989; Gierz & Bartnicki-Garcia, 2001), with its central tenet that new membrane material is produced at a single localised source and incorporated in the outer cell wall following vesicle transport, could perhaps be adapted to explain growth morphology of root hairs. It is clear that the detailed modelling of complex transcriptional and signalling networks remains out of reach, but that is not true of all systems. The modelling of metabolism for example has been particularly successful, at least in bacteria. David Fell (Oxford Brookes University, UK) brought those experiences to the meeting explaining what can be achieved even before a complete and explicit description has been achieved. Again the advantages of a system at steady state were emphasised. This is not the same as a system at equilibrium, metabolic products are made and resources are consumed, but within the network itself the multitude of fluxes balance so that the levels of all metabolites remain constant, a prerequisite for the continued health of any organism. Such techniques open up the possibility of rational metabolic engineering. While there have been some high profile examples of the successful re-engineering of plants to make specific products, Lee Sweetlove (Oxford University, UK) was keen to point out that ‘there are more examples of this going wrong than going right’, such as when the addition of more transgenes for a synthetic pathway to produce plastics in planta decreased the ultimate yield (e.g. Bohmert et al., 2000), or where doping plants with extra Krebs cycle enzymes failed to alter respiration, instead increasing starch production (Jenner et al., 2001). Sweetlove is trying to use modelling based on our understanding of bacterial metabolism to predict and so prevent such unwanted outcomes, but there are pitfalls. Plant cells are not homogeneous bags of enzymes, compartmentalisation and the transient assembly of enzyme complexes on organelle surfaces are just two of the complications encountered when dealing with eukaryotes. Perhaps a simpler model system, such as Arabidopsis cells in suspension culture, can be used as a stepping stone towards whole-plant modelling and the near-mythical ‘in silico plant’. ‘In systems biology the model aims to be an alembic, distilling away information that is irrelevant to leave a kernel of understanding’ This nicely highlights the conflict inherent in the systems biology approach, and from which it draws much of its strength. The experimentalist wishes to know, or at least measure, everything. High throughput approaches are becoming ever faster at identifying the players in a network and how they fit together, leading to ever more complicated and detailed descriptions. The modeller, on the other hand, wishes to pare down descriptions to the simplest possible representation, the simpler the network the easier to model. In systems biology the model aims to be an alembic, distilling away information that is irrelevant to leave a kernel of understanding. More often than not, the failure of our models highlight the shortcomings in our experimentally derived information, guiding experimenters to expend their efforts where they can be most profitable. Nick Monk (University of Sheffield, UK) and Andrew Millar (University of Edinburgh, UK) gave examples of how relatively simple modelling of networks can be employed in this way. Both involved oscillatory systems: Millar's, the plant circadian clock, and Monk's, the auto-regulation of the transcription factor Hes1. The simplicity of the Hes1 network, consisting only of the Hes1 gene, the cDNA transcribed from it and the proteinaceous transcription factor that it encodes, belies its complexity. The expression of Hes1 oscillates with a 2-h period (Hirata et al., 2002), the periods and amplitudes of these oscillations only becoming explicable once a model containing several dozen parameters has been constructed. Without modelling it could have easily been concluded that a more complicated feedback system involving other, unidentified components, was responsible for the oscillations. With modelling, scarce experimental resources can be directed towards studying those parameters predicted to control the system as a whole, while ignoring those with little or no effect. A perfect example of such iterative discovery was the development of our understanding of the central circadian clock in plants. Only a few years ago analogies with the clocks of animals, such as fruit flies and mice, had led to the assumption that the plant central clock consisted of a single negative feedback loop involving essentially only two components. Millar's explicit modelling showed that while such a set up can maintain daily cycles, further elaboration was needed to explain the cycles seen in various mutant plants, suggesting a circuit involving two interlocking feedback loops. While closer to synthesising reality such an arrangement is still unable to cope with a number of double-mutant phenotypes leading to the suggestion that further loops, with as yet unidentified components, will need to be added (Locke et al., 2005). It would have been easy to have been overawed by the diversity of networks under consideration at this symposium, from Ian Woodward's (University of Sheffield, UK) exposition of how the global climate affects stomata and is in turn shaped by them, through Jane Memmott's (University of Bristol, UK) descriptions of the interleaved ecological networks for distribution of pollen and seeds, to the exquisite balance of forces studied by Andrew Bangham (University of East-Anglia, UK) that fold petals into their final shapes. This diversity was reiterated in the meeting's participants; one speaker who suggested that greater links needed to be forged with engineers, was surprised to discover that many were already in the room, and yet it was the sense of shared challenges and opportunities that is the afterimage of this symposium. Arthur Tansley was a systems biologist a century before the term was invented. He was the first to use the term ‘ecosystem’ in print (Tansley, 1935) and established the New Phytologist to promote ‘discussion between British botanists on all subjects connected with their branch of science’ (Lewis & Ingram, 2002). As Alistair Hetherington, one of the organisers said, ‘Tansley would have enjoyed this meeting’." @default.
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