Faculty Directory
Neda Bagheri

Assistant Professor of Chemical and Biological Engineering

Contact

2145 Sheridan Road
Tech E154
Evanston, IL 60208-3109

847-491-2716Email Neda Bagheri

Website

Bagheri Lab


Centers

Center for Synthetic Biology


Departments

Chemical and Biological Engineering

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

The Bagheri Lab integrates experimental data with novel computational strategies to elucidate fundamental properties governing intracellular dynamics and intercellular regulation. When the regulation of biological function fails, people manifest a variety of illnesses including cancer and autoimmune disease. My lab combines computational strategies with signaling data and complex biological observations to develop predictive models that highlight sensitivity tradeoffs and identify control strategies to restore healthy function.

Professor Bagheri earned a CAREER Award from the National Science Foundation in 2017, and a Cornew Innovation Award (joint with Prof. Josh Leonard) from the Chemistry of Life Processes Institute in 2014.


Selected Publications

    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.