Immunobiology
Miraldi Lab

Miraldi Research Lab

The Miraldi lab’s focus is mathematical modeling of the immune system from high-dimensional genomics measurements. In close collaboration with experimental immunologists, we seek to learn how diverse immune cells sense and respond to their environment in both health and disease. Our studies leverage new biotechnologies (e.g., chromatin accessibility, single-cell genomics measurements) and often require development of new computational methods. The resulting genome-scale models provide unbiased, experimentally testable hypotheses. Long-term, we would like to use these models to re-engineer immune-cell behavior in the context of autoimmunity, cancer and other diseases.

In the News

Deep neural networks to predict transcription factor binding, Ohio Supercomputer Center, Aug 24, 2023. 

New tool provides low-cost, high-quality epigenome maps, Research Horizons, April 5, 2023.

Smart Software Untangles Gene Regulation in Cells, Nature, September 5, 2022.

Patient-Specific Prediction of Epigenomes Through Deep Learning, January 2021.

A New Way to Map Cell Regulatory Networks. EurekAlert!, March 5, 2019. 

How Big Data and Genomics are Improving Infectious Disease Research. Healthcare Analytics, Nov. 30, 2017.

Immuno-Engineering from the Numbers. Research Horizons, Spring 2017.

Computational Methods and Biological Systems

Learn more about Miraldi Lab research projects.

Meet the Lab

Learn more about our current lab members and lab alumni.

Open Lab Positions

The Miraldi Lab seeks talented, motivated individuals at all levels (masters, PhD, postdoc, programmers). Interested parties are invited to apply today.

Publications

Wayman, JA; Thomas, A; Bejjani, A; Katko, A; Almanan, M; Godarova, A; Korinfskaya, S; Cazares, TA; Yukawa, M; Kottyan, LC; et al. An atlas of gene regulatory networks for memory CD4 + T cells in youth and old age. 2023; 4:2023.03.07.531590.

Cazares, TA; Rizvi, FW; Iyer, B; Chen, X; Kotliar, M; Bejjani, AT; Wayman, JA; Donmez, O; Wronowski, B; Parameswaran, S; et al. maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks. Editor, Przytycka TM. PLoS Computational Biology. 2023; 19:e1010863.

Pokrovskii, M; Hall, JA; Ochayon, DE; Yi, R; Chaimowitz, NS; Seelamneni, H; Carriero, N; Watters, A; Waggoner, SN; Littman, DR; et al. Characterization of Transcriptional Regulatory Networks that Promote and Restrict Identities and Functions of Intestinal Innate Lymphoid Cells. Immunity. 2019; 51:185-197.e6.

Miraldi, ER; Pokrovskii, M; Watters, A; Castro, DM; De Veaux, N; Hall, JA; Lee, J; Ciofani, M; Madar, A; Carriero, N; et al. Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells. Genome research. 2019; 29:449-463.

Castro, DM; de Veaux, NR; Miraldi, ER; Bonneau, R. Multi-study inference of regulatory networks for more accurate models of gene regulation. Editor, van Nimwegen E. PLoS Computational Biology. 2019; 15:e1006591.

Karwacz, K; Miraldi, ER; Pokrovskii, M; Madi, A; Yosef, N; Wortman, I; Chen, X; Watters, A; Carriero, N; Awasthi, A; et al. Critical role of IRF1 and BATF in forming chromatin landscape during type 1 regulatory cell differentiation. Nature Immunology. 2017; 18:412-421.

Raviram, R; Rocha, PP; Mueller, CL; Miraldi, ER; Badri, S; Fu, Y; Swanzey, E; Proudhon, C; Snetkova, V; Bonneau, R; et al. 4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments. Editor, Morozov AV. PLoS Computational Biology. 2016; 12:e1004780.

Kurtz, ZD; Mueller, CL; Miraldi, ER; Littman, DR; Blaser, MJ; Bonneau, RA. Sparse and compositionally robust inference of microbial ecological networks. Editor, von Mering C. PLoS Computational Biology. 2015; 11:e1004226.

Miraldi, ER; Sharfi, H; Friedline, RH; Johnson, H; Zhang, T; Lau, KS; Ko, HJ; Curran, TG; Haigis, KM; Yaffe, MB; et al. Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice. Integrative Biology: interdisciplinary approaches for molecular and cellular life sciences. 2013; 5:940-963.

Toth, A; Kannan, P; Snowball, J; Kofron, M; Wayman, JA; Bridges, JP; Miraldi, ER; Swarr, D; Zacharias, WJ. Alveolar epithelial progenitor cells require Nkx2-1 to maintain progenitor-specific epigenomic state during lung homeostasis and regeneration. Nature Communications. 2023; 14:8452.

Thomas, AL; Godarova, A; Wayman, JA; Miraldi, ER; Hildeman, DA; Chougnet, CA. Accumulation of immune-suppressive CD4 + T cells in aging - tempering inflammaging at the expense of immunity. Seminars in Immunology. 2023; 70:101836.

Thomas, AL; Wayman, JA; Almanan, M; Bejjani, AT; Miraldi, ER; Chougnet, CA; Hildeman, DA. Elevated CD153 Expression on Aged T Follicular Helper Cells is Vital for B cell Responses. 2023; 4:2023.03.17.533214.

Wayman, JA; Thomas, A; Bejjani, A; Katko, A; Almanan, M; Godarova, A; Korinfskaya, S; Cazares, TA; Yukawa, M; Kottyan, LC; et al. An atlas of gene regulatory networks for memory CD4 + T cells in youth and old age. 2023; 4:2023.03.07.531590.

Cazares, TA; Rizvi, FW; Iyer, B; Chen, X; Kotliar, M; Bejjani, AT; Wayman, JA; Donmez, O; Wronowski, B; Parameswaran, S; et al. maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks. Editor, Przytycka TM. PLoS Computational Biology. 2023; 19:e1010863.

Skok Gibbs, C; Jackson, CA; Saldi, GA; Tjärnberg, A; Shah, A; Watters, A; De Veaux, N; Tchourine, K; Yi, R; Hamamsy, T; et al. High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0. Editor, Mathelier A. Bioinformatics. 2022; 38:2519-2528.

Toth, A; Steinmeyer, S; Kannan, P; Gray, J; Jackson, CM; Mukherjee, S; Demmert, M; Sheak, JR; Benson, D; Kitzmiller, J; et al. Inflammatory blockade prevents injury to the developing pulmonary gas exchange surface in preterm primates. Science Translational Medicine. 2022; 14:eabl8574.

Lu, X; Chen, X; Forney, C; Donmez, O; Miller, D; Parameswaran, S; Hong, T; Huang, Y; Pujato, M; Cazares, T; et al. Global discovery of lupus genetic risk variant allelic enhancer activity. Nature Communications. 2021; 12:1611.

Ho, JS Y; Mok, BW Y; Campisi, L; Jordan, T; Yildiz, S; Parameswaran, S; Wayman, JA; Gaudreault, NN; Meekins, DA; Indran, SV; et al. TOP1 inhibition therapy protects against SARS-CoV-2-induced lethal inflammation. Cell. 2021; 184:2618-2632.e17.

Lu, X; Chen, X; Forney, C; Donmez, O; Miller, D; Parameswaran, S; Hong, T; Huang, Y; Pujato, M; Cazares, T; et al. Global discovery of lupus genetic risk variant allelic enhancer activity. Nature Communications. 2021; 12.

Miraldi, ER; Chen, X; Weirauch, MT. Deciphering cis-regulatory grammar with deep learning. Nature Genetics. 2021; 53:266-268.

Moreno-Fernandez, ME; Miraldi, ER; Divanovic, S. Not Chopped Liver-A Careful, Fate-Mapping Study of Macrophages in NASH. Cell Metabolism. 2020; 32:328-330.

Tang, MS; Miraldi, ER; Girgis, NM; Bonneau, RA; Loke, P. Alternative Activation of Macrophages Is Accompanied by Chromatin Remodeling Associated with Lineage-Dependent DNA Shape Features Flanking PU.1 Motifs. Journal of immunology (Baltimore, Md. : 1950). 2020; 205:1070-1083.

Almanan, M; Raynor, J; Ogunsulire, I; Malyshkina, A; Mukherjee, S; Hummel, SA; Ingram, JT; Saini, A; Xie, MM; Alenghat, T; et al. IL-10-producing Tfh cells accumulate with age and link inflammation with age-related immune suppression. Science Advances. 2020; 6:eabb0806.

Ho, JS Y; Angel, M; Ma, Y; Sloan, E; Wang, G; Martinez-Romero, C; Alenquer, M; Roudko, V; Chung, L; Zheng, S; et al. Hybrid Gene Origination Creates Human-Virus Chimeric Proteins during Infection. Cell. 2020; 181:1502-1517.e23.

Pokrovskii, M; Hall, JA; Ochayon, DE; Yi, R; Chaimowitz, NS; Seelamneni, H; Carriero, N; Watters, A; Waggoner, SN; Littman, DR; et al. Characterization of Transcriptional Regulatory Networks that Promote and Restrict Identities and Functions of Intestinal Innate Lymphoid Cells. Immunity. 2019; 51:185-197.e6.

Miraldi, ER; Pokrovskii, M; Watters, A; Castro, DM; De Veaux, N; Hall, JA; Lee, J; Ciofani, M; Madar, A; Carriero, N; et al. Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells. Genome research. 2019; 29:449-463.

Castro, DM; de Veaux, NR; Miraldi, ER; Bonneau, R. Multi-study inference of regulatory networks for more accurate models of gene regulation. Editor, van Nimwegen E. PLoS Computational Biology. 2019; 15:e1006591.