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P
Pannabecker, T. Lloyd, Dantzler, W. H., Layton, A. T., & Layton, H. E.. (2008). Three-dimensional reconstructions of rat renal inner medulla suggest two anatomically separated countercurrent mechanisms for urine concentration. Federation of American Societies for Experimental Biology.
Pannabecker, T. L., Dantzler, W. H., Layton, H. E., & Layton, A. T.. (2008). Role of three-dimensional architecture in the urine concentrating mechanism of the rat renal inner medulla. American Journal of Physiology-Renal Physiology, 295, F1271–F1285. American Physiological Society.
Patterson, S. E., & Layton, A. T.. (2021). Computing Viscous Flow Along a 3D Open Tube Using the Immerse Interface Method. arXiv preprint arXiv:2112.12892.
Patterson, S. E., & Layton, A. T.. (2021). Computing viscous flow along a 2D open channel using the immersed interface method. Engineering Reports, 3, e12334.
Prieto-García, L., Vicente-Vicente, L., Blanco-Gozalo, V., Hidalgo-Thomas, O., García-Macías, M. C., Kurtz, A., Layton, A. T., et al. (2020). Pathophysiological mechanisms underlying a rat model of triple whammy acute kidney injury. Laboratory Investigation, 100, 1455–1464. Nature Publishing Group US New York.
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Robel, A., & Layton, A.. (2009). The Lorenz Model.
Ryu, H., & Layton, A. T.. (2014). Feedback-mediated dynamics in a model of coupled nephrons with compliant short loop of Henle. Biological Fluid Dynamics: Modeling, Computations, and Applications, 628, 209. American Mathematical Soc.
Ryu, H., & Layton, A. T.. (2014). Tubular fluid flow and distal NaCl delivery mediated by tubuloglomerular feedback in the rat kidney. Journal of mathematical biology, 68, 1023–1049. Springer Berlin Heidelberg.
Ryu, H., & Layton, A. T.. (2013). Effect of tubular inhomogeneities on feedback-mediated dynamics of a model of a thick ascending limb. Mathematical Medicine and Biology: a Journal of the IMA, 30, 191–212. Oxford University Press.
Ryu, H., Layton, A. T., Tubuloglomerular, D. NaCl Deliv, & others,. (2012). HWAYEON RYU. Biomed Eng, 28, 369–380.
Ryu, H., & Layton, A. T.. (2012). Tubular Fluid Oscillations Mediated by Tubuloglomerular Feedback in a Short Loop of Henle. Federation of American Societies for Experimental Biology.
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Sadria, M., Layton, A., & Bader, G.. (2023). Adversarial training improves model interpretability in single-cell RNA-seq analysis. bioRxiv, 2023–05. Cold Spring Harbor Laboratory.
Sadria, M., Bjornstad, P., Pottumarthi, P., Lu-Ping, L., Pyle, L., Vigers, T., & Layton, A.. (2023). A multimodal neural network that distinguishes between type 1 and type 2 diabetes in young persons using MRI and clinical data. Physiology, 38, 5695980. American Physiological Society Rockville, MD.
Sadria, M., & Layton, A.. (2023). The Power of Two: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis. bioRxiv, 2023–04. Cold Spring Harbor Laboratory.
Sadria, M., Layton, A., Goyal, S., & Bader, G.. (2022). Fatecode: Cell fate regulator prediction using classification autoencoder perturbation. bioRxiv, 2022–12. Cold Spring Harbor Laboratory.
Sadria, M., & Layton, A.. (2022). A predictive model for estimating protection against CKD and CVD with SGLT2 inhibition in patients with diabetes. The FASEB Journal, 36. The Federation of American Societies for Experimental Biology.
Sadria, M., Seo, D., & Layton, A. T.. (2022). The mixed blessing of AMPK signaling in Cancer treatments. BMC cancer, 22, 1–16. BioMed Central.
Sadria, M., & Layton, A. T.. (2021). A computational model for cell energy balance and metabolism.
Sadria, M., & Layton, A. T.. (2021). Modeling within-host SARS-CoV-2 infection dynamics and potential treatments. Viruses, 13, 1141. MDPI.
Sadria, M., & Layton, A. T.. (2021). Aging affects circadian clock and metabolism and modulates timing of medication. Iscience, 24. Elsevier.

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