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Physical Review. E|October 16, 2021
Non-Fickian single-file pore transportSpencer Farrell, Andrew D RutenbergPlos Computational Biology|January 10, 2022
Interpretable machine learning for high-dimensional trajectories of aging healthSpencer Farrell, Arnold Mitnitski, Kenneth Rockwood, et al.Elife|November 21, 2022
Measurements of damage and repair of binary health attributes in aging mice and humans reveal that robustness and resilience decrease with age, operate over broad timescales, and are affected differently by interventionsSpencer Farrell, Alice E Kane, Elise Bisset, et al.The Journal of Chemical Physics|September 17, 2017
Photobleaching of randomly rotating fluorescently decorated particlesSwadhin Taneja, Andrew D RutenbergPhysical Review Letters|April 12, 2003
Pattern formation inside bacteria: fluctuations due to the low copy number of proteinsMartin Howard, Andrew D RutenbergPhysical Review. E, Statistical, Nonlinear, and Soft Matter Physics|August 27, 2011
Monodisperse domains by proteolytic control of the coarsening instabilityJulien Derr, Andrew D RutenbergArxiv|November 14, 2023
Network dynamical stability analysis reveals key "mallostatic" natural variables that erode homeostasis and drive age-related decline of healthGlen Pridham, Andrew D RutenbergThe Journals of Gerontology. Series A, Biological Sciences and Medical Sciences|January 11, 2024
Dynamical Network Stability Analysis of Multiple Biological Ages Provides a Framework for Understanding the Aging ProcessGlen Pridham, Andrew D RutenbergPhysical Review. E, Statistical, Nonlinear, and Soft Matter Physics|December 31, 2005
Maximally fast coarsening algorithmsMowei Cheng, Andrew D RutenbergScientific Reports|December 13, 2023
Network dynamical stability analysis reveals key "mallostatic" natural variables that erode homeostasis and drive age-related decline of healthGlen Pridham, Andrew D RutenbergPageof 6