Analysing scientific data may involve very complex and interlinked steps where several tools are combined together. Scientific workflows systems have reached a level of maturity that makes them able to support the design and execution of such in-silico experiments. They provide a systematic way of describing the scientific and data methods, and execute complex analysis on a variety of distributed resources. In this lecture, we will review main features of scientific workflows (representation, composition, model of computation, execution, mapping), present different workflows systems, and illustrate how algebraic scientific workflow and provenance can enhance reproductibility in the analysis and simulation of complex systems in biology.