In this issue of Essays in Biochemistry , biochemistry meets systems biology—a blind date that may hold all the promises, pitfalls and failures of a relationship where a new discipline has been sprung upon a well-established one. As the articles in this issue show, the blind date in this case has great potential to develop into a long-term relationship, where both partners share common values but can benefit from different complementary approaches. Together this partnership is well poised to address and solve some of the major challenges in modern biology.

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available –omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of –omics data into CBMs focussing on the methods’ assumptions and limitations. We argue that key assumptions – often derived from single-enzyme kinetics – do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for –omics data integration in a common framework to provide more accurate predictions.