A dynamic model for simulation of growth in pigs was extended by a module to describe protein turn-over in six body protein pools (muscle, connective tissue, liver, blood plasma, gastro-intestinal, and 'other' proteins). The model describes protein deposition in these pools following different growth curves and differential rates of turnover. Growth curve parameters and turn-over rates were obtained from the literature.

In growing animals, experimentally measured turn-over rates represent a combination of turn-over of already-present body protein and fractional (repeated) synthesis of newly deposited protein. An attempt was made to distinguish between these processes by varying the values of the fractional rate of synthesis of newly deposited protein (FRSdrp) and of the proportionof maintenance energy requirements not related to protein turn-over (FrcMEmaint), and comparing the simulated outputto the output from the original model without the protein turnover module.

The turn-over rate (TRpres)of already present connective tissue protein reached unrealistic values for FRSdep > 2·5 per day, which puts an upper limit on FRSdep.

The output from the extended and the original models showed similar patterns for certain combinations of FRSdefl and FrcMEmaint, dependent on the levels of model input variables. For FRSdcp 2·5 perday, these similar patterns have their optimum at FrcME^^ = 0·65, coinciding with FRSdep =2·0 per day. The corresponding TRpres values were 0·060, 0·019, 0·585, 1·492, 0·582, and 0·016 per day for the above mentioned pools.

A dynamic model for simulation of growth in pigs, that was extended by a module to describe protein turn-over, was made stochastic in order to simulate groups of pigs with among-animal variation in the maximum daily protein deposition (Pdep, maxK in the minimum lipid to protein deposition rate (Ri/pimin), and in the distributionof body protein over protein pools (muscle, connective tissue, and other proteins). As a result, these simulated pigs show among-animal variation in body protein content and composition. This in turn leads to among-animal variation in energy requirements for protein turn-over and this causes among-animal variation in maintenance metabolizable energy requirements (MEmaint)as a result of variation in body composition.

Simulated population means for PieVimax were varied in seven steps from 100 to 250 g/day, with an among-animal variation coefficient of 0·10; the feeding level was also varied in seven steps. Dependent on the levels of these input variables, 100-kg pigs showed within-population standard deviations in body protein and lipid content of 0·31 to 0·54 kg and 1·22 to 2·17 kg, respectively. ME showed a protein-turn-over-related, within-population coefficient of variation of 0·014 to 0·02. Comparisons over populations suggests that a 1·50 proportional increase in Pdep, max (from 100 to 250 g/day) would increase protein-turn-over-related MEmaint by 11 to 15%, from between 470 and 486 to 541 k] ME per kg body weight0'75 per day. The inferences that can be made from this with regard to experimental design are discussed.

A procedure for maximizing genetic gain (after a number of generations of selection) for a given rate of inbreeding or for a given coefficient of variation of response is presented. An infinitesimal genetic model is assumed. Mass selection is practised for a number of discrete generations. With constraints on inbreeding, expected rates of genetic progress (ΔG) are combined with expeced rates of inbreeding (ΔF) in a linear objective function (Ω = ΔG - μΔF). In addition, an expression to approximate the rate of gain at any generation accounting for changes in genetic parameters due to linkage disequilibrium and due to inbreeding is derived. Predicted gain is in general within 5% of that obtained from simulation. Thus, both ΔG and ΔF are obtained from simple analytical formulae. An equivalent function is used when the coefficient of variation of response (CV) is the parameter restricted (Ω = ΔG -μCV). Maximization of the objective function Ω for appropriate values of μ gives the optimum number of sires and dams selected when specific constraints on the level of inbreeding or the coefficient of variation of response are imposed. The method is applied to a practical situation in fish breeding. Optimum mating ratios and optimum numbers of sires selected are obtained for different scored population sizes and heritabilities. Results obtained with this procedure agree very well with results from simulation studies. The optimum number of sires increases with the size of the scheme and with more severe restrictions on risk. In the schemes considered, the optimum mating ratio is equal to 2 unless the constraint on the rate of inbreeding is severe, the size of the scheme is small and the heritability is low. In these situations the optimum mating ratio is equal to 1. The procedure is general in terms of generations of selection considered and in terms of parameters to be constrained. A large amount of computer processor unit time is saved with this method in comparison with simulation procedures.