Author

M. Strobl

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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Cancer Biology· 5

Signature Assays, Organisms & Methods

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Organism:human· 5 Research Focus:drug response / drug discovery· 4 Approach:statistical model· 3 Assay:imaging· 2 Research Focus:cell signaling & trafficking· 1 Approach:deep learning· 1 Approach:mechanistic / dynamical model· 1

Frequent Co-Authors

A. R. A. Anderson· 2 shared J. West· 2 shared M. Damaghi· 2 shared M. Robertson-Tessi· 2 shared P. Maini· 2 shared R. Gatenby· 2 shared R. Wenham· 2 shared A. Anderson· 1 shared

Activity by Year

2018-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersCancer Biology

A. R. A. Anderson

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5 papers

Recent·Most cited

Learning to Adapt - Deep Reinforcement Learning in Treatment-Resistant Prostate Cancer

K. Gallagher, M. Strobl, R. Gatenby, P. Maini, A. Anderson

This paper applies deep reinforcement learning to adaptive drug scheduling in prostate cancer, showing it can extend time to progression by up to a quarter and outperform current protocols.

bioRxiv · Apr 28, 2023Cancer Biology

Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling

M. Strobl, A. Martin, J. West, J. Gallaher, M. Robertson-Tessi, R. Gatenby, R. Wenham, P. Maini, M. Damaghi, A. R. A. Anderson

This study develops an adaptive therapy protocol for PARP inhibitors by combining mathematical modeling and experiments to optimize treatment scheduling and mitigate resistance.

bioRxiv · Mar 22, 2023 · PreprintCancer Biology

Antifragile therapy

J. West, B. Desai, M. Strobl, L. Pierik, R. Vander Velde, C. Armagost, R. Miles, M. Robertson-Tessi, A. Marusyk, A. R. A. Anderson

This paper introduces a mathematical framework to quantify cancer cell line fragility or antifragility to treatment variability and applies it to treatment resistance and collateral sensitivity.

bioRxiv · Oct 8, 2020 · PreprintCancer Biology

Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma - Learning to adapt

I. C. Sorribes, A. Basu, R. Brady, P. M. Enriquez-Navas, X. Feng, J. N. Kather, N. Nerlakanti, R. Stephens, M. Strobl, I. Tavassoly, N. Vitos, D. Lemanne, B. Manley, C. O'Farrelly, H. Enderling

This paper develops a dynamic tumor-immune model to predict patient response to treatment using NLR as a biomarker and simulates optimal therapies for virtual RCC patients.

bioRxiv · Feb 28, 2019 · PreprintCancer Biology

Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer

M. Strobl, M. Wicker, V. Adhikarla, A. Shockey, E. Lakatos, P. Pooladvand, R. Schenck, L. Saputro, C. Gatenby, M. Koppens, S. Cruz Garcia, R. Wenham, M. Damaghi, J. A. Gallaher

This study develops a mathematical model to analyze spatial interactions in ovarian cancer microenvironments and shows how niche composition affects treatment response.

bioRxiv · Oct 24, 2018 · PreprintCancer Biology