Author

Pooja Agarwal

Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA 94720; Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA 94720;Primary ORCID profile (0009-0009-9984-4871 · 3 papers)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Pooja AgarwaliD3 papers · University of Warwick

Biology Disciplines

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Synthetic Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:protein sequence· 2 Approach:deep learning· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1 Research Focus:protein design· 1 Organism:yeast· 1

Frequent Co-Authors

Claire J. LeBlanc· 2 shared Marissa A. ZinteliD· 2 shared Aditya Udupa· 1 shared Angelica Lam· 1 shared Angelica W. Y. Lam· 1 shared Daniel Aguilera· 1 shared G.F. HuiD· 1 shared Gean Hu· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersSynthetic Biology

Claire J. LeBlanc

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2 papers

Recent·Most cited

Machine learning guided design of strong transcriptional activation domains

Marissa A. Zintel, Claire J. LeBlanc, Sanjana R. Kotha, Melvin Soriano, Rishaan J. Bansal, Daniel Aguilera, Touss Majidi, Izaiah Ornelas, Liam D. Kirkpatrick, Aditya Udupa, Gean Hu, Pooja Agarwal, Angelica W. Y. Lam, James K. Nunez, Patrick M. Shih, , Max V. Staller

The paper designs functional transcriptional activation domains using machine learning and experimental validation, finding that over 90% of predicted sequences work in yeast.

bioRxiv · Nov 2, 2026 · PreprintSynthetic Biology

Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding

Claire J. LeBlanc, Pooja Agarwal, Jack Demaray, G.F. Hu, Marissa A. Zintel, Angelica Lam, Joel Enrique Castro Hernandez, Max Valentín Staller

This paper develops interpretable biophysical neural networks to predict transcriptional activation domains from protein sequences, revealing how residue properties influence both activation strength and protein abundance.

Cell Systems · Aug 17, 2026Peer-reviewedSystems Biology