Modelling of nutrient partitioning in growing pigs to predict their anatomical body composition. 1. Model description

Abstract

A dynamic mechanistic model was developed for growing and fattening pigs. The aim of the model was to predict growth rate and the chemical and anatomical body compositions from the digestible nutrient intake of gilts (20 -105 kg live weight). The model represents the partitioning of digestible nutrients from intake through intermediary metabolism to body protein and body fat. State variables of the model were lysine, acetyl-CoA equivalents, glucose, volatile fatty acids and fatty acids as metabolite pools, and protein in muscle, hide-backfat, bone and viscera and body fat as body constituent pools. It was assumed that fluxes of metabolites follow saturation kinetics depending on metabolite concentrations. In the model, protein deposition rate depended on the availability of lysine and of acetyl-CoA. The anatomical body composition in terms of muscle, organs, hide-backfat and bone was predicted from the chemical body composition and accretion using allometric relationships. Partitioning of protein, fat, water and ash in muscle, organs, hide-backfat and bone fractions were driven by the rates of muscle protein and body fat deposition. Model parameters were adjusted to obtain a good fit of the experimental data from literature. Differential equations were solved numerically for a given set of initial conditions and parameter values. In the present paper, the model is presented, including its parameterisation. The evaluation of the model is described in a companion paper. Modelling: Anatomical body composition: Chemical body composition: PigSince the introduction of pig growth models, applicable in a scientific and a practical environment in the 1970s and 1980s (for example, see Whittemore & Fawcett, 1976;Moughan et al. 1987;Black et al. 1988), interest in prediction of pig growth has increased over the years. New models have been introduced, each serving their own objective: some models have focused on nutrient digestion processes (Bastianelli et al. 1996), on protein digestion in the small intestine (Rivest et al. 2000) or on estimating amino acid requirements (Moughan, 1989). Others have aimed to model growth rate and its composition in terms of protein and lipid (Burlacu et al. 1989;Pomar et al. 1991;Danfaer, 2000;Birkett & de Lange, 2001b), or especially fatty acid composition of the body fat (Lizardo et al. 2002), or improving understanding of different processes, such as protein turnover and ion pumping (Gill et al. 1989b), or the process of growth (Lovatto & Sauvant, 2003). In addition, pig growth modelling efforts have been reviewed and various approaches have been discussed extensively (Black, 1995;Gerrits & Dijkstra, 2000;Halas & Babinszky, 2000;Birkett & de Lange, 2001a). Most pig growth simulation models until the 1990s considered protein and energy as separate entities (de Lange, 1995).As acknowledged in more recently developed models, this approach ignored the effects of differences in the composition of the dietary energy (Danfaer, 2000;Birkett & de Lange, 2001a). In addition to models predicting che...

Modelling growth and body composition in fish nutrition: where have we been and where are we going?

“…minBL:BP). Certain biochemical transactions can exert control over others and kinetic constants such as maximum velocity ( V max ) can serve to achieve a realistic body composition (Halas, Dijkstra, Babinszky, Verstegen & Gerrits 2004). Nevertheless, this approach can fail to represent accurately observed empirical relationships.…”

“…Nevertheless, this approach can fail to represent accurately observed empirical relationships. For instance, the predicted relationships between energy intake and PD displayed a curvilinear shape, whereas observed data followed a linear pattern in evaluating a model designed to partition dietary nutrients in growing pigs (Halas et al 2004). Assumptions are sometimes made in models based on saturation kinetics that basically mask a lack of precise knowledge and may lead to discrepancies.…”

A multivariate nonlinear mixed effects method for analyzing energy partitioning in growing pigs1

“…where k 1 and k 2 are affinity parameters for dietary supply of energy and standardized ileal digestible lysine above maintenance (Lys M ). This idea had already been used in a pig simulation model, although in a slightly different version (Halas et al, 2004). Sandberg et al (2005) criticized the partitioning rule put forward by van Milgen and Noblet (1999) for not being able to cope with a varying protein or AA supply.…”

Precision pork production: Predicting the impact of nutritional strategies on carcass quality

“…This means that "errors will cumulate in lipid" and we have difficulties seeing these errors. Few models that have been developed accounted for the distribution of lipids between carcass and non-carcass tissues or among the different fat depots (e.g., Halas et al, 2004;Hoch & Agabriel, 2004;McPhee, Oltjen, Fadel, Mayer, & Sainz, 2009). The model of Lizardo, van Milgen, Mourot, Noblet, and Bonneau (2002) is to our knowledge the only one in pigs that considers the distribution of body lipids and fatty acids among tissues.…”