Basic and clinical research increasingly depends on sophisticated information technologies for storing, exchanging, and mining large volumes of data. The biomedical research community needs common bioinformatics infrastructures for locating and analyzing datasets of different types and formats and from disparate sources. The integration of data from human studies with data from model organisms, particularly the laboratory mouse, which is the most powerful and widely used animal model for studying human disease, is critical for translational research to ultimately improve diagnosis, determine individualized treatment, and predict clinical outcomes. The Mouse Phenome Project was conceived and launched at The Jackson Laboratory (JAX) to complement the mouse genome sequencing effort and to provide a research resource and integral tool for complex trait analysis. The Mouse Phenome Database (MPD; http://www.jax.org/phenome) is an established phenomics database containing phenotypic and/or genotypic data for over 600 strains of mice. MPD contains a wealth of data contributed by research teams worldwide, including phenotypes relevant to human health such as cancer susceptibility, aging, obesity, susceptibility to infectious diseases, atherosclerosis, blood disorders, neurosensory disorders, and drug toxicity. These studies represent ~130 research institutions in 12 countries, and are supported by ~65 funding agencies and research foundations worldwide. Building on our MPD experience to further advance the goal of accelerating biomedical research, we propose to acquire, annotate, and integrate new datasets for public access; ensure MPD data, information, and formats are consistent with community-adopted standards; and implement new tools and website features to best present and analyze those data. The intrinsic nature of phenotypes to reflect the underlying genome and its response to environmental influences is key to inferring the function of genetic determinates. Thus a high-throughput systems biology approach to identify phenotypic networks will provide insights that in turn help generate new hypotheses. We will exploit MPD's growing collection of quantitative data to build phenotypic networks, providing a framework for visualizing putative functional relationships between phenotypes and integrating this information with other biological information, such as gene networks, metabolic pathways, epigenetic information, SNPs, and copy number variants. Our work will enable cross-species identification and facilitate efforts to precisely define and classify human diseases and their risk factors, and to identify network components that are predictive of physiological and pathological responses.