Author

Alyssa D. Arnheim

Boston University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 2 Systems Biology· 2

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:mouse· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

Dino Di CarloiD· 2 shared Ian Morales· 2 shared Qianhui WuiD· 2 shared Stacey D. FinleyiD· 2 shared Andrew Tran· 1 shared Chung Hee Soo· 1 shared Lily Shang· 1 shared Michael MellodyiD· 1 shared

Activity by Year

2017-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersBioengineering

Dino Di CarloORCID

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Author profile →

4 papers

Recent·Most cited

Hydrogel formulation controls size-dependent accumulation of proteins and nanoparticles in PEG microparticles

Alyssa D. Arnheim, Ian Morales, Andrew Tran, Dino Di Carlo

This study shows how PEG hydrogel formulation affects the accumulation of proteins and nanoparticles, with larger molecules requiring more permissive formulations.

bioRxiv · Aug 10, 2026 · PreprintBioengineering

Sealable capped nanovials for high-throughput screening of cell growth and function

Michael Mellody, Yuta Nakagawa, Alyssa D. Arnheim, Lily Shang, Chung Hee Soo, Natalie Tsubamoto, Sarah Taylor, Sumedha Shastry, Wesley Luk, Ian Morales, Richard G. James, Dino Di Carlo

This paper introduces capped nanovials as a scalable platform for single-cell and small colony culture, enabling high-throughput screening of growth and function with improved signal-to-noise ratios and co-culture capabilities.

bioRxiv · Jun 29, 2025 · PreprintBioengineering

In silico mouse study identifies tumor growth kinetics as biomarkers for the outcome of anti-angiogenic treatment

Qianhui Wu, Alyssa D. Arnheim, Stacey D. Finley

The paper uses a computational model to identify tumor growth kinetic parameters as prognostic biomarkers for anti-angiogenic treatment outcomes.

Journal of The Royal Society Interface · Aug 1, 2018Peer-reviewedSystems Biology

Mechanistic Modeling Quantifies The Influence Of Tumor Growth Kinetics On The Response To Anti-Angiogenic Treatment

Thomas D. Gaddy, Qianhui Wu, Alyssa D. Arnheim, Stacey D. Finley

This study uses computational modeling to show that tumor growth kinetics influence the response to anti-VEGF treatment, with nonlinear dependence on VEGF receptor expression.

PLoS Computational Biology · Dec 21, 2017Peer-reviewedSystems Biology