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Nature Communications|September 2, 2021
The basis of easy controllability in Boolean networksEnrico Borriello, Bryan C DanielsNature Communications|August 22, 2015
Automated adaptive inference of phenomenological dynamical modelsBryan C Daniels, Ilya NemenmanTheory in Biosciences = Theorie in Den Biowissenschaften|February 26, 2021
Quantifying the impact of network structure on speed and accuracy in collective decision-makingBryan C Daniels, Pawel RomanczukPlos One|March 26, 2015
Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regressionBryan C Daniels, Ilya NemenmanPlos One|May 13, 2021
Evolution of default genetic control mechanismsWilliam Bains, Enrico Borriello, Dirk Schulze-MakuchPhysical Review. E, Statistical, Nonlinear, and Soft Matter Physics|May 24, 2011
Nucleation at the DNA supercoiling transitionBryan C Daniels, James P SethnaComputational Brain & Behavior|July 16, 2026
Continuous Attractor Networks for Laplace Neural ManifoldsBryan C Daniels, Marc W HowardJournal of the Royal Society, Interface|February 10, 2026
Tuning regimes in ant foraging dynamics depend on the existence of bistabilityColin M Lynch, Bryan C DanielsJournal of Experimental Zoology. Part B, Molecular and Developmental Evolution|March 12, 2020
Cell phenotypes as macrostates of the GRN dynamicsEnrico Borriello, Sara I Walker, Manfred D LaubichlerProceedings of the National Academy of Sciences of the United States of America|March 24, 2019
Automated, predictive, and interpretable inference of <i>Caenorhabditis elegans</i> escape dynamicsBryan C Daniels, William S Ryu, Ilya NemenmanPageof 3