This paper discusses how fruit set, fruit growth and dry matter partitioning can be simulated by models where sink strength (assimilate demand) and source strength (assimilate supply) are the key variables. Although examples are derived from experiments on fruit vegetables such as tomato, sweet pepper and cucumber, the theoretical basis holds for a wide range of crops including fruit trees. Dry matter partitioning is the end result of the flow of assimilates from source organs via a transport path to the sink organs. It appears to be primarily regulated by the sink strength of the sinks, with fruits being the major sinks in fruit trees or fruit vegetables. Source strength has only an indirect effect on dry matter partitioning through effects on the number of fruits on a plant. The transport path is only of minor importance for the regulation of dry matter partitioning at the whole plant level. The growth rate of a fruit depends on the source strength and the fraction of the assimilates partitioned into it. Dry matter partitioning was modelled as a function of the sink strengths of the plant organs, where sink strength of an organ is defined by its potential growth rate (potential capacity to accumulate assimilates). The potential growth rate has been shown to quantitatively reflect the sink strength of an organ. The potential growth of a fruit is a function of both its age and temperature. In several experiments and for different treatments it was shown that dry matter partitioning into a fruit can be simulated as a function of its sink strength relative to that of the other plant organs. The number of fruits set per plant has a great impact on the dry matter partitioning and fruit growth. Several experiments have shown that fruit set increases with source strength and decreases with sink strength. Consequently fruit set could be reasonably successful modelled as a function of sink and source strength. Finally it is shown how a photosynthesis-based model combined with submodels for fruit set, fruit growth and dry matter partitioning can be used for predictions of yield and fruit size.