The growth potential of a tumour can significantly depend on host features such as age, cell proliferation rates and caloric intake. Although this is widely known, existing mathematical models for tumour growth do not account for it. We therefore developed a new model for tumour growth, starting from a mathematical framework that describes the host's physiology. The resulting tumour-inhost model allowed us to study the implications of various specific interactions between the energetics of tumour and host. The model accounts for the influence of both age and feeding regimen of the host organism on the behaviour of a tumour. Concerning the effects of a tumour on its host, it explains why tumour-mediated body-weight loss is often more dramatic than expected from the energy demands of the tumour. We also show how the model can be applied to study enhanced body-weight loss in presence of cachectic factors. Our tumour-in-host model thus appears a proper tool to unite a wide range of phenomena in tumour -host interactions. British Journal of Cancer (2003) Mathematical models for tumour growth have been widely used in different subdisciplines, such as cancer risk assessment (Dewanji et al, 1991;Sherman and Portier, 2000), cancer biology (Laird, 1964;Ward and King, 1999), cancer treatment (Thomlison and Gray, 1955;Adam and Bellomo, 1997), and oncological decision making (Friberg and Mattson, 1997). Since the first models for tumour growth were published (Mayneord, 1932;Winsor, 1932;Von Bertalanffy, 1957), they have become more detailed and, consequently, more complex (Groebe and Mueller-Klieser, 1991;Ward and King, 1997). Most classic and modern approaches share at least one feature, though: both describe the increase in size of an independent 'entity.' The models are therefore adequate to analyse, for instance, data on tumour spheroids growing in vitro. Their use to describe data on tumours growing in vivo may be less warranted because of interactions between tumour and host. The aim of this article is to develop a mathematical model to explore such interactions between the growth of a tumour and the physiology of the host organism. We based our model on well-recognised interactions between tumour growth, energy homeostasis, utilisation of stored energy by tumour and host and cancer cachexia. The formulation in terms of a mathematical model has several benefits. First, it forces us to specify quantitative formulations about the interactions, which improves testability of the hypotheses involved. Second, because the model asks for an overall view of a number of processes and their inter-relationships, it can offer insights that complement those arising from individual experimental studies. Finally, model simulations allow to switch on or off particular hypothetical mechanisms easily, so that we can evaluate their impact on and relevance for the expected outcome.The article is organised as follows. First, we introduce the dynamic energy budget (DEB) theory (Kooijman, 2000(Kooijman, , 2001, which provides quantitative e...