Areas of Interest
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 my 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.
A Well-Mixed E. coli Genome: Widespread Contacts Revealed by Tracking Mu Transposition.
Walker DM, Freddolino PL, Harshey RM.
Cell. 2020; 180: 703-16.
Rapid metabolic shifts occur during the transition between hunger and satiety in Drosophila melanogaster.
Wilinski D, Winzeler J, Duren W, Persons JL, Holme KJ, Mosquera J, Khabiri M, Kinchen JM, Freddolino PL, Karnovsky A, Dus M.
Nat Commun. 2019; 10: 4052.
High-Resolution Mapping of the Escherichia coli Chromosome Reveals Positions of High and Low Transcription.
Scholz SA, Diao R, Wolfe MB, Fivenson EM, Lin XN, Freddolino PL.
Cell Syst. 2019; 8: 212-25.
Global analysis of RNA metabolism using bio-orthogonal labeling coupled with next-generation RNA sequencing.
Wolfe MB, Goldstrohm AC, Freddolino PL.
Methods. 2019; 155: 88-103.
Escherichia coli Lrp Regulates One-Third of the Genome via Direct, Cooperative, and Indirect Routes.
Kroner GM, Wolfe MB, Freddolino PL.
J Bacteriol. 2019; 201: e00411-18.
The structure of human Nocturnin reveals a conserved ribonuclease domain that represses target transcript translation and abundance in cells.
Abshire ET, Chasseur J, Bohn JA, Del Rizzo PA, Freddolino PL, Goldstrohm AC, Trievel RC.
Nucleic Acids Res. 2018; 46: 6257-70.
MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.
Zhang C, Zheng W, Freddolino PL, Zhang Y.
J Mol Biol. 2018; 430: 2256-65.
For a list of publications at Google Scholar, click HERE