Science.

I employ biological, computational, and mathematical approaches to life science problem solving.

I conduct research in my role as a Biophysics PhD candidate at UCSF.

Curriculum Vitae

Presentations

Check out my recent presentation at the ARCS Foundation National Speaker Series [YouTube]

Scientific Papers


2022 | Science

Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning.

Daniels KG, Wang S, Simic MS, Bhargava HK, Capponi S, Tonai Y, Yu W, Bianco S, Lim WA.

To explore if non-natural combinations of signaling motifs could drive novel cell fates of interest, we constructed a library of CARs containing ∼2,300 synthetic costimulatory domains. The library produced CARs driving diverse fate outputs, which were sensitive to motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules.

Media & Press

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2021 | Nature Chemical Biology

The HaloTag as a general scaffold for far-red tunable chemigenetic indicators.

Deo C*, Abdelfattah AS*, Bhargava HK, Berro A, Falco N, Moeyaert B, Chupanova M, Lavis LD, Schreiter ER

We introduce a new platform for ‘chemigenetic’ fluorescent indicators, utilizing the self-labeling HaloTag protein conjugated to environmentally sensitive synthetic fluorophores. This approach affords bright, far-red calcium and voltage sensors with highly tunable photophysical and chemical properties, which can reliably detect single action potentials in neurons.

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2020 | Proceedings of the National Academy of Sciences

Structural basis for autophagy inhibition by the human Rubicon-Rab7 complex.

Bhargava HK, Tabata K, Byck JM, Hamasaki M, Farrell DP, Anishchenko I, DiMaio F, Im YJ, Yoshimori T, Hurley JH

We report the novel atomic structure of autophagy inhibitor Rubicon bound to GTPase Rab7. Our work provides a roadmap to block Rubicon localization and activity in order to upregulate autophagy, a major drug development goal.

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2020 | Clinical Cancer Research

Computationally derived image signature of stromal morphology is prognostic of prostate cancer recurrence following prostatectomy in African American patients.

Bhargava HK, Leo P, Elliott R, Janowcyzk A, Whitney J, Gupta S, Fu P, Yamoah K, Rebbeck T, Feldman D, Lal P, Madabhushi A

We developed computer vision and machine learning to predict prostate cancer recurrence risk based on quantitative measurements of the intratumoral stroma computed from digitized H&E images.

Media & Press

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2019 | American Journal of Clinical Pathology

PD-L1 Expression Reveals Significant Association with Squamous Differentiation in Upper Tract Urothelial Carcinoma.

Arriola A, Farahani S, Bhargava HK, Guzzo T, Brooks J, Lal P.

This study comprehensively reviews programmed death 1 receptor (PD-1)–positive and CD8+ tumor-infiltrating lymphocytes (TILs) and programmed death ligand 1 (PD-L1) expression on tumor epithelium (TE).

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2019 | G3 Genes|Genomes|Genetics

An Interscholastic Network To Generate LexA Enhancer Trap Lines in Drosophila.

Kockel L, Griffin C, Ahmed Y, Fidelak L, Rajan A, Gould EP, Haigney M, Ralston B, Tercek RJ, Galligani L, Rao S, Huq L, Bhargava HK, Dooner AC, Lemmerman EG, Malusa RF, Nguyen TH, Chung JS, Gregory SM, Kuwana KM, Regenold JT, Wei A, Ashton J, Dickinson P, Martel K, Cai C, Chen C, Price S, Qiao J, Shepley D, Zhang J, Chalasani M, Nguyen K, Aalto A, Kim B, Tazawa-Goodchild E, Sherwood A, Rahman A, Wu SYC, Lotzkar J, Michaels S, Aristotle H, Clark A, Gasper G, Xiang E, Schlör FL, Lu M, Haering K, Friberg J, Kuwana A, Lee J, Liu A, Norton E, Hamad L, Lee C, Okeremi D, diTullio H, Dumoulin K, Chi SYG, Derossi GS, Horowitch RE, Issa EC, Le DT, Morales BC, Noori A, Shao J, Cho S, Hoang MN, Johnson IM, Lee KC, Lee M, Madamidola EA, Schmitt KE, Byan G, Park T, Chen J, Monovoukas A, Kang MJ, McGowan T, Walewski JJ, Simon B, Zu SJ, Miller GP, Fitzpatrick KB, Lantz N, Fox E, Collette J, Kurtz R, Duncan C, Palmer R, Rotondo C, Janicki E, Chisholm T, Rankin A, Park S, Kim SK.

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