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Abstract. Nitrogen (N) was added over two years to a spruce-dominated (Picea abies) montane forest at Alptal, central Switzerland. A solution of ammonium nitrate (NH4NO3) was frequently sprinkled on the forest floor (1500 m 2 ) to simulate an additional input of 30 kg N ha -1 yr -1 over the ambient 12 kg bulk inorganic N deposition. The added nitrogen was labelled with 15 NH4 15 NO3 during the first year. Results are compared to a control plot. Neither the trees nor the ground vegetation showed any increase in their N content. Only 4.1% of N in the ground vegetation came from the N addition. Current-year needles contained 11 mg N g -1 dry weight, of which only 2% was from labelled N; older needles had approximately half as much 15 N. The uptake from the treatment was therefore very small. Redistribution of N also took place in the trunks: 1 to 2-year-old wood contained 0.7% labelled N, tree rings dating back 3 to 14 years contained 0.4%.Altogether, the above-ground vegetation took up 12% of the labelled N. Most 15 N was recovered in the soil: 13% in litter and roots, 63% in the sieved soil. Nitrate leaching accounted for 10%. Factors thought to be influencing N uptake are discussed in relation to plant use of N and soil conditions.

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Mathematical modelling is increasingly becoming an indispensable tool for the study of cellular processes, allowing their analysis in a systematic and comprehensive manner. In the vast majority of the cases, models focus on specific subsystems, and in particular describe either metabolism, gene expression or signal transduction. Integrated models that are able to span and interconnect these layers are, by contrast, rare as their construction and analysis face multiple challenges. Such methods, however, would represent extremely useful tools to understand cell behaviour, with application in distinct fields of biological and medical research. In particular, they could be useful tools to study genotype-phenotype mappings, and the way they are affected by specific conditions or perturbations. Here, we review existing computational approaches that integrate signalling, gene regulation and/or metabolism. We describe existing challenges, available methods and point at potentially useful strategies.