Systems biology is a rapidly growing field which seeks a refined quantitative understanding of organisms, particularly studying how molecular species such as metabolites, proteins and genes interact in cells to form the complex emerging behaviour exhibited by living systems. Synthetic biology is a related and emerging field which seeks to engineer new organisms for practical purposes. Both fields can benefit from formal languages for modelling, simulation and analysis. In systems biology there is however a trade-off in the landscape of existing formal languages: some are modular but may be difficult for some biologists to understand (e.g. process calculi) while others are more intuitive but monolithic (e.g. rule-based languages). The first major contribution of this thesis is to bridge this gap with a Language for Biochemical Systems (LBS). LBS is based on the modular Calculus of Biochemical Systems and adds e.g. parameterised modules with subtyping and a notion of nondeterminism for handling combinatorial explosion. LBS can also incorporate other rule-based languages such as Kappa, hence adding modularity to these. Modularity is important for a rational structuring of models but can also be exploited in analysis as is shown for the specific case of Petri net flows. On the synthetic biology side, none of the few existing dedicated languages allow for a high-level description of designs that can be automatically translated into DNA sequences for implementation in living cells. The second major contribution of this thesis is exactly such a language for Genetic Engineering of Cells (GEC). GEC exploits the recent advent of standard genetic parts (“biobricks”) and allows for the composition of such parts into genes in a modular and abstract manner using logical constraints. GEC programs can then be translated to DNA sequences using a constraint satisfaction engine based on a given database of genetic parts.