Faculty Directory
Neda Bagheri

Adjunct Associate Professor of Chemical and Biological Engineering

Contact

2145 Sheridan Road
Tech E154
Evanston, IL 60208-3109

847-491-2716Email Neda Bagheri

Website

Bagheri Lab


Departments

Chemical and Biological Engineering

Affiliations

PhD Program in Interdisciplinary Biological Sciences

Education

Ph.D./M.S. Electrical & Computer Engineering, University of California, Santa Barbara

B.S. Electrical & Computer Engineering, University of California, Santa Barbara


Research Interests

Neda Bagheri directs the Modeling Dynamic Life Systems (MODYLS) Lab at Northwestern University. Her research lies at the cutting-edge intersection of engineering and biology to solve exciting challenges within medicine and basic science. Her group is particularly interested in challenges related to identifying engineering ‘design principles’ that underlie, explain, and rationalize complex biological function, as well as understanding how extrinsic factors can be used to optimize therapeutic interventions. To accomplish these goals, her interdisciplinary team of engineers, basic scientists and applied mathematicians combine experimental data with novel computational strategies derived from statistical analysis and control theory to attack problems from creative angles not possible with single discipline methods. As evidenced from her research accomplishments, solutions to these challenges have potential impact to addressing important problems in cancer and immune system diseases, and in uncovering new fundamental understanding of microbial and circadian biology.

In recognition for her research accomplishments, Bagheri was awarded a CAREER Award from the National Science Foundation (NSF) in 2017. Her research has been published in high-profile journals, including Proceedings of the National Academy of Sciences and PLoS Computational Biology. She serves on an NSF Science & Technology Center and on the Immuneering advisory boards, and she is recognized internationally for her leadership in the field of computational and systems biology.



Selected Publications

Finkle J. D.*, Wu J. J.*, Bagheri N. Windowed Granger causal inference strategy improves discovery of gene regulatory networks. Proc Natl Acad Sci U.S.A. 115(9):2252–2257, 2018. PMID: 29440433.

Hartfield R. M.*, Schwarz K. A.*, Muldoon J. J.*, Bagheri N., Leonard J. N. Multiplexing engineered receptors for multiparametric evaluation of environmental ligands. ACS Synth Biol. 6(11):2042–2055, 2017. PMID: 28771312.

Xue A. Y., Szymczak L. C., Mrksich M., Bagheri N. Machine learning on SAMDI mass spectrometry signal to noise ratio improves peptide array designs. Anal Chem. 89(17):9039–9047, 2017. PMID: 28719743.

Misharin A., ..., Yacoub T. J., ..., Bagheri N., Shilatifard A., Budinger G. R., Perlma H. Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the lifespan. J Exp Med. 214(8):2387–2404, 2017. PMID: 28694385.

Stainbrook S. C.*, Yu J. S.*, Reddick M. P., Bagheri N.^, Tyo K. E. J.^ Modulating and evaluating receptor promiscuity through directed evolution and modeling. Protein Eng Des Sel. 30(6):455–465, 2017. PMID: 28453776.

Yu J. S., Xue A. Y., Redei E. E., Bagheri N. A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder. Transl Psychiatry. 6(10):e931, 2016. PMID: 27779627.

Yu J. S., Bagheri N. Multi-class and multi-scale models of complex biological phenomena. Curr Opin Biotechnol. 6(10):e931, 2016. PMID: 27779627.

Hill S. M., et al. and HPN-DREAM Consortium. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods. 13(4):310–318, 2016. PMID: 26901648.

Ciaccio M. F., Chen V. C., Jones R. B., Bagheri N. The DIONESUS algorithm provides scalable and accurate reconstruction of dynamic phosphoproteomic networks to reveal new drug targets. Integr Biol. 7(7):776–791, 2015. PMID: 26057728.

Duncan M. T.*, Shin S.*, Wu J. J.*, Mays Z., Weng S., Bagheri N.^, Miller W. M.^, Shea L. D.^ Dynamic transcription factor activity profiles reveal key regulatory interactions during megakaryocytic and erythroid differentiation. Biotechnol Bioeng. 111(10):2082–2094, 2014. PMID: 24853077.

Ciaccio M. F., Finkle J. D., Xue A. Y., Bagheri N. A systems approach to integrative biology: an overview of statistical methods to elucidate association and architecture. Integr Comp Biol. 54(2):296–306, 2014. PMID: 24813462.