Systems biology approaches in traditional comparativebiology Major advances in high-throughput technologies for the detection and quantification of nucleic acids, proteins and metabolites have led to a paradigm shift in biological research. The new field of systems biology attempts to integrate the complex data sets generated by high-throughput approaches to develop a holistic understanding of complex biological structures, their dynamics and their responsiveness to external stimuli such as salinity stress. The level of complexity of structures modeled by systems biology ranges from molecular networks via cells and organs to whole organisms (Kitano, 2002). Systems biology organizes data obtained by genomic, transcriptomic, proteomic and metabolomic approaches to attempt to build descriptive and mechanistic models of integrative biological phenomena such as development or interactions of organisms with their environment.The ultimate goal of this approach is to generate a mathematical model that describes the biological system and has predictive power (Aggarwal and Lee, 2003). Achieving this tremendously ambitious goal depends on in-depth knowledge about each element constituting the system of interest. For instance, experimental data about the expression, regulation, function, compartmentation, interaction, modification and stability of individual RNAs and proteins have to be collected and integrated for each state of the system that is described by the model. Systems biology approaches have largely been applied to organisms whose genomes have been sequenced (socalled 'model' organisms) because many of the available bioinformatics tools are based on prior genome sequencing and annotation of the encoded transcriptome and proteome. However, most high-throughput technologies for analyzing the transcriptome, proteome and metabolome do not strictly All organisms are adapted to well-defined extracellular salinity ranges. Osmoregulatory mechanisms spanning all levels of biological organization, from molecules to behavior, are central to salinity adaptation. Functional genomics and proteomics approaches represent powerful tools for gaining insight into the molecular basis of salinity adaptation and euryhalinity in animals. In this review, we discuss our experience in applying such tools to so-called 'non-model' species, including euryhaline animals that are well-suited for studies of salinity adaptation. Suppression subtractive hybridization, RACE-PCR and mass spectrometry-driven proteomics can be used to identify genes and proteins involved in salinity adaptation or other environmental stress responses in tilapia, sharks and sponges. For protein identification in non-model species, algorithms based on sequence homology searches such as MSBLASTP2 are most powerful. Subsequent gene ontology and pathway analysis can then utilize sets of identified genes and proteins for modeling molecular mechanisms of environmental adaptation. Current limitations for proteomics in non-model species can be overcome by improving sequence co...