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

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

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. PLoS Computational Biology. 2019; 15.

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. PLoS Computational Biology. 2016; 12.

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

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 (United Kingdom). 2013; 5:940-963.

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. Computer Applications in the Biosciences. 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.

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.

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.

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. 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.

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. PLoS Computational Biology. 2019; 15.

Raviram, R; Rocha, PP; Luo, VM; Swanzey, E; Miraldi, ER; Chuong, EB; Feschotte, C; Bonneau, R; Skok, JA. Analysis of 3D genomic interactions identifies candidate host genes that transposable elements potentially regulate. Genome Biology. 2018; 19.

Science Blog

What is the Inferelator? How New Software Can Track Complex Transcription Factor Patterns
Big Data and Analytics

What is the Inferelator? How New Software Can Track Complex Transcription Factor Patterns

Emily R. Miraldi, PhD9/7/2022

‘Massively Parallel’ Gene Screening Tool Can Accelerate Research for Nearly Any Disease
Autoimmune Disorders

‘Massively Parallel’ Gene Screening Tool Can Accelerate Research for Nearly Any Disease

Leah C. Kottyan, PhD, Matthew T. Weirauch, PhD, Emily R. Miraldi, PhD3/12/2021

A New Way to Map Cell Regulatory Networks
Genomics and Development

A New Way to Map Cell Regulatory Networks

Emily R. Miraldi, PhD7/3/2019

Contact Us

Emily Miraldi builds models on the whiteboard.

Emily R. Miraldi, PhD

Assistant Professor

The Divisions of Immunobiology and Biomedical Informatics

Phone: 513-517-2063
Email: emily.miraldi@cchmc.org

Google Scholar Page

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