The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic datasets in numerous species including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is now recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to the study of a particular disease type. Examples of such techniques include immunocapture, which minimizes the loss of post-translational modification, IBTP (4-iodobutyl-triphenylphosphonium) labeling, which quantifies protein redox states, and SELDI-TOF, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with both top-down and bottom-up approaches and have been steadily improved in both size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods, on the other hand, often require the incorporation of a large amount of existing data, but provide more rigorous and quantitative information which can be used as hypotheses for subsequent experimental studies. Successes and limitations of studies reviewed in this article provide both opportunities and challenges that need to be addressed to facilitate the application of systems biology to larger systems.