Industrial wine making confronts viticulturalists, wine makers, process engineers and scientists alike with a bewilderingarray of independent and semi-independent parameters that can in many cases only be optimized by trial and error.Furthermore, as most parameters are outside of individual control, predictability and consistency of the end productremain difficult to achieve. The traditional wine sciences of viticulture and oenology have been accumulating data setsand generating knowledge and know-how that has resulted in a significant optimization of the vine growing and winemaking processes. However, much of these processes remain based on empirical and even anecdotal evidence, andonly a small part of all the interactions and cause-effect relationships between individual input and output parametersis scientifically well understood. Indeed, the complexity of the process has prevented a deeper understanding of suchinteractions and causal relationships. New technologies and methods in the biological and chemical sciences, combinedwith improved tools of multivariate data analysis, open new opportunities to assess the entire vine growing and winemaking process from a more holistic perspective. This review outlines the current efforts to use the tools of systemsbiology in particular to better understand complex industrial processes such as wine making
