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Firstly, AN and BN are not discreet disorders and approximately 50% of women with AN develop BN over the course of their illness (Treasure & Collier, 2001) The boundaries between other eating disorder categories also overlap For example, it is dif cult to distinguish between non-purging BN and BED Secondly, a substantial proportion of the individuals presenting with an eating disorder do not ful l the diagnostic criteria for the main categories and are classi ed as having an eating disorder not otherwise speci ed (EDNOS) This group includes individuals presenting with partial syndromes It has been proposed that it might be more appropriate to view all eating disorders as lying on a spectrum with a continuum of eating symptoms from under-eating to over-eating (Treasure & Collier, 2001).

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It is conceivable that many of the differences between eating disorder categories, such as between BN and obesity, may be quantitative rather than qualitative Several of the personality traits and Axis II disorders that are frequently associated with eating disorders have been shown to precede the development of eating disorders and to persist after long-term recovery, suggesting that they could contribute to the pathogenesis of eating disorders (Kaye et al, 2000a) It has been suggested that eating disorders should not only be classi ed in terms of eating symptoms but also by personality types Three personality domains have been identi ed: a high functioning, self-critical, perfectionistic group, which was mainly associated with BN; a constricted, overcontrolled group restricting pleasure, needs, emotions, relationships and self-knowledge, which was associated with RAN; and an impulsive, undercontrolled and emotionally dysregulated group (Goldner et al, 1999).

To learn a decision tree/decision graph representation from data, a simple top-down method may be used as for standard decision tree induction: first the split attribute at the root node is chosen based on an evaluation measure. Then the sample cases to learn from are split w.r.t. the value they have for this attribute and the algorithm is applied recursively to each subset. For learning decision graphs, we must also be able to merge nodes. Thus we arrive at the

Individual-based models have been available for some time (Barnthouse, 1992) Using these, toxic impacts on physiology and metabolism of individual organisms have been developed These have been developed further using sh bioenergetics and growth models (Nisbet et al, 1997; Gamito, 1998) Models have been developed to examine the uptake and accumulation of pollutants For example, the accumulation and body burden of PCBs in trout (Salvelinus namaycush) has been examined using a bioenergetics-based pollutant accumulation model which highlighted the importance of the food chain route of exposure in this sh (Luk & Brockway, 1997) Muller and Nisbet (1996) have taken this further by linking an energy budget model of individual growth and reproduction to a toxicant bioaccumulation model and a model of toxicant action on the individual One modelling approach that is proving instructive in risk assessment is the physiologically-based pharmacokinetic models (PBPK).

De ning the phenotype for eating disorder research is far from simple Many genetic studies to date have used eating disorder subgroups such as RAN, AN/BP, binge-purging BN, or non-purging BN because the genes underlying these types are likely to be more homogeneous than those underlying the main eating disorder categories The use of narrow diagnostic criteria increases the chance of detecting small genetic effects However, the number of individuals in each subgroup would be very small because AN and BN are rare and this would reduce the statistical power of the study Conversely, a broad eating disorder phenotype that includes partial symptoms would increase the sample size but the heterogeneity of the sample would reduce statistical power Future research may bene t from conceptualizing the eating disorders as a spectrum..

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