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The regulatory networks of bacteria play a key role in their information processing capabilities, coordinating and executing interactions with their environments. Quantitative, predictive models of these networks would be tremendously beneficial for facilitating the development of new antimicrobial therapies, enabling synthetic biology applications, and understanding bacterial evolution and ecology. Ultimately, the aim of the Freddolino laboratory is to build a multiscale framework enabling modeling of bacterial regulatory networks at any level of detail, from atomistic to cellular. To this end, we develop and apply high-throughput experimental methods for measuring biomolecular interactions and cellular regulatory states in vivo, and for profiling the phenotypic consequences of regulatory changes. In tandem with these experimental approaches, we use molecular simulation and mathematical modeling to obtain high-resolution insight into the biomolecular interactions driving regulatory networks, and the systems-level effects of altering them. We have also found that many of the approaches that we use to dissect bacterial regulatory networks translate well to identifying new regulatory logic in eukaryotic systems, and we are thus involved in a rich web of collaborations applying our tools for network analysis, motif inference, and identification of protein-nucleic acid interactions to systems from yeast to flies to humans.