Publications

AS Moffett and AW Eckford. (2025) Kelly Bets and Single-Letter Codes: Optimal Information Processing in Natural Systems. IEEE Transactions on Molecular, Biological, and Multi-Scale Communications. DOI: https://doi.org/10.1109/TMBMC.2025.3581468

AS Moffett, K Ganzinger, and AW Eckford. (2025) Comparing kinetic proofreading and kinetic segregation for T cell receptor activation. Physical Review Research. 7: 023003. DOI: https://doi.org/10.1103/PhysRevResearch.7.023003

TS Barker, PJ Thomas, AS Moffett, AW Eckford, and M Pierobon. (2024) Fitness value of subjective information for living organisms. NANOCOM '24: Proceedings of the 11th ACM International Conference on Nanoscale Computing and Communication. 54-59. DOI: https://doi.org/10.1145/3686015.3689352 

AS Moffett, Y Deng, and H Levine. (2023) Modeling the role of immune cell conversion in the tumor-immune microenvironment. Bulletin of Mathematical Biology. 85 (93). DOI: https://doi.org/10.1007/s11538-023-01201-z

AS Moffett, G Cui, PJ Thomas, WD Hunt, NA McCarty, RS Westafer, and AW Eckford.  (2022) Permissive and nonpermissive channel closings in CFTR revealed by a factor graph inference algorithm. Biophysical Reports. 100083. DOI: https://doi.org/10.1016/j.bpr.2022.100083

AS Moffett,  PJ Thomas,  M Hinczewski, and AW Eckford. (2022) Cheater suppression and stochastic clearance through quorum sensing. PLOS Computational Biology. 18 (7): e1010292. DOI: https://doi.org/10.1371/journal.pcbi.1010292 

AS Moffett and AW Eckford (2022) Minimal informational requirements for fitness. Physical Review E. 105 (1): 014403. DOI: https://doi.org/10.1103/PhysRevE.105.014403. Preprint: https://arxiv.org/abs/2112.02193

AS Moffett, N Wallbridge, C Plummer, and AW Eckford.  (2020) The Fitness Value of Information with Delayed Phenotype Switching: Optimal Performance with Imperfect Sensing. Physical Review E. 102 (5): 052403. DOI: https://doi.org/10.1103/PhysRevE.102.052403. Preprint: https://arxiv.org/abs/2009.00058

AS Moffett and D Shukla. (2020) Structural consequences of multisite phosphorylation in the BAK1 kinase domain. Biophysical Journal. 118 (3): 698-707. DOI: https://doi.org/10.1016/j.bpj.2019.12.026

F Aldukhi, A Deb, C Zhao, AS Moffett, and D Shukla. (2019) Molecular mechanism of brassinosteroid perception by the plant growth receptor BRI1. Journal of Physical Chemistry B. 124 (2): 355-365. DOI: https://doi.org/10.1021/acs.jpcb.9b09377

AS Moffett and D Shukla. (2018) Using molecular simulation to explore the nanoscale dynamics of the plant kinome. Biochemical Journal. 475 (5): 905-921. DOI: https://doi.org/10.1042/BCJ20170299

AS Moffett, KW Bender, SC Huber, and D Shukla. (2017) Allosteric control of a plant receptor kinase through S-glutathionylation. Biophysical Journal. 113 (11): 2354-2363. DOI: https://doi.org/10.1016/j.bpj.2017.08.059

AS Moffett, KW Bender, SC Huber, and D Shukla. (2017) Molecular dynamics simulations reveal the conformational dynamics of Arabidopsis thaliana BRI1 and BAK1 receptor-like kinases. Journal of Biological Chemistry. 292: 12643-12652. DOI: https://doi.org/10.1016/j.bpj.2017.08.059

Z Shamsi†, AS Moffett†, and D Shukla. (2017) Enhanced unbiased sampling of protein dynamics using evolutionary coupling information. Scientific Reports. 7: 12700. DOI: https://doi.org/10.1038/s41598-017-12874-7

S Shukla, Z Shamsi, AS Moffett, B Selvam, and D Shukla. (2015) Application of hidden Markov models in biomolecular simulations. Pages 29-41 in DR Westhead and MS Vijayabaskar, editors. Hidden Markov Models. Humana Press, New York, NY. DOI: https://doi.org/10.1007/978-1-4939-6753-7

S Kalim, CB Clish, JJ Deferio, G Ortiz, AS Moffett, RE Gerszten, R Thadhani, and EP Rhee. (2015) Cross-sectional examination of metabolites and metabolic phenotypes in uremia. BMC Nephrology. 16: 98. DOI: https://doi.org/10.1186/s12882-015-0100-y