Ann Arbor
MI, 48109-0600 United States
Available to mentor
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.
Freddolino Lab
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Postdoctoral fellowColumbia University, Systems Biology, 2014
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Postdoctoral researcherPrinceton University, Molecular Biology, 2011
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PhDUniversity of Illinois at Urbana-Champaign, Urbana, 2009
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BSCalifornia Institute of Technology, Pasadena, 2004
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.
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Haugen RJ, Barnier C, Elrod ND, Luo H, Jensen MK, Ji P, Smibert CA, Lipshitz HD, Wagner EJ, Freddolino PL, Goldstrohm AC. RNA, 2024 Apr 16;Journal ArticleRegulation of the Drosophila transcriptome by Pumilio and the CCR4-NOT deadenylase complex.
DOI:10.1261/rna.079813.123 PMID: 38627019 -
Schroeder JW, Lydia Freddolino P. J Mol Biol, 2024 Apr 5; 168567Journal ArticleEnricherator: A Bayesian method for inferring regularized genome-wide enrichments from sequencing count data.
DOI:10.1016/j.jmb.2024.168567 PMID: 38583516 -
Rakibova Y, Dunham DT, Seed KD, Freddolino PL. 2024 Mar 25;PreprintNucleoid-associated proteins shape the global protein occupancy and transcriptional landscape of a clinical isolate of Vibrio cholerae.
DOI:10.1101/2023.12.30.573743 PMID: 38260642 -
Zheng W, Wuyun Q, Li Y, Zhang C, Freddolino PL, Zhang Y. Nat Methods, 2024 Feb; 21 (2): 279 - 289.Journal ArticleImproving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data.
DOI:10.1038/s41592-023-02130-4 PMID: 38167654 -
Zhang C, Zhang X, Freddolino PL, Zhang Y. Nucleic Acids Res, 2024 Jan 5; 52 (D1): D404 - D412.Journal ArticleBioLiP2: an updated structure database for biologically relevant ligand-protein interactions.
DOI:10.1093/nar/gkad630 PMID: 37522378 -
Zhang C, Freddolino PL. 2023 Dec 19;PreprintFURNA: a database for function annotations of RNA structures.
DOI:10.1101/2023.12.19.572314 PMID: 38187637 -
Zheng W, Wuyun Q, Freddolino PL, Zhang Y. Proteins, 2023 Dec; 91 (12): 1684 - 1703.Journal ArticleIntegrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15.
DOI:10.1002/prot.26585 PMID: 37650367 -
Li Y, Zhang C, Feng C, Pearce R, Lydia Freddolino P, Zhang Y. Nat Commun, 2023 Sep 16; 14 (1): 5745Journal ArticleIntegrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction.
DOI:10.1038/s41467-023-41303-9 PMID: 37717036
