| Signal integration in bacterial two-component regulatory systems AY Mitrophanov, EA Groisman Genes & development 22 (19), 2601-2611, 2008 | 467 | 2008 |
| Positive feedback in cellular control systems AY Mitrophanov, EA Groisman Bioessays 30 (6), 542-555, 2008 | 324 | 2008 |
| Sensitivity and convergence of uniformly ergodic Markov chains AY Mitrophanov Journal of Applied Probability 42 (4), 1003-1014, 2005 | 182 | 2005 |
| Evolution and dynamics of regulatory architectures controlling polymyxin B resistance in enteric bacteria AY Mitrophanov, MW Jewett, TJ Hadley, EA Groisman PLoS genetics 4 (10), e1000233, 2008 | 117 | 2008 |
| Statistical significance in biological sequence analysis AY Mitrophanov, M Borodovsky Briefings in Bioinformatics 7 (1), 2-24, 2006 | 103 | 2006 |
| Stability and exponential convergence of continuous-time Markov chains AY Mitrophanov Journal of applied probability 40 (4), 970-979, 2003 | 96 | 2003 |
| Computational approach to characterize causative factors and molecular indicators of chronic wound inflammation S Nagaraja, A Wallqvist, J Reifman, AY Mitrophanov The Journal of Immunology 192 (4), 1824-1834, 2014 | 88 | 2014 |
| A connector of two-component regulatory systems promotes signal amplification and persistence of expression A Kato, AY Mitrophanov, EA Groisman Proceedings of the National Academy of Sciences 104 (29), 12063-12068, 2007 | 76 | 2007 |
| LPS‐stimulated NF‐κB p65 dynamic response marks the initiation of TNF expression and transition to IL‐10 expression in RAW 264.7 macrophages S Hobbs, M Reynoso, AV Geddis, AY Mitrophanov, RW Matheny Jr Physiological reports 6 (21), e13914, 2018 | 71 | 2018 |
| Computational study of thrombus formation and clotting factor effects under venous flow conditions V Govindarajan, V Rakesh, J Reifman, AY Mitrophanov Biophysical journal 110 (8), 1869-1885, 2016 | 64 | 2016 |
| Computational analysis of the effects of reduced temperature on thrombin generation: the contributions of hypothermia to coagulopathy AY Mitrophanov, FR Rosendaal, J Reifman Anesthesia & Analgesia 117 (3), 565-574, 2013 | 61 | 2013 |
| Kinetic modeling sheds light on the mode of action of recombinant factor VIIa on thrombin generation AY Mitrophanov, J Reifman Thrombosis research 128 (4), 381-390, 2011 | 42 | 2011 |
| The spectral gap and perturbation bounds for reversible continuous-time Markov chains AY Mitrophanov Journal of applied probability 41 (4), 1219-1222, 2004 | 39 | 2004 |
| Kinetic model facilitates analysis of fibrin generation and its modulation by clotting factors: implications for hemostasis-enhancing therapies AY Mitrophanov, AS Wolberg, J Reifman Molecular BioSystems 10 (9), 2347-2357, 2014 | 38 | 2014 |
| Positive autoregulation shapes response timing and intensity in two-component signal transduction systems AY Mitrophanov, TJ Hadley, EA Groisman Journal of molecular biology 401 (4), 671-680, 2010 | 37 | 2010 |
| Control of Streptococcus pyogenes virulence: modeling of the CovR/S signal transduction system AY Mitrophanov, G Churchward, M Borodovsky Journal of theoretical biology 246 (1), 113-128, 2007 | 37 | 2007 |
| Impact of tissue factor localization on blood clot structure and resistance under venous shear V Govindarajan, S Zhu, R Li, Y Lu, SL Diamond, J Reifman, ... Biophysical journal 114 (4), 978-991, 2018 | 32 | 2018 |
| Sensitivity of hidden Markov models AY Mitrophanov, A Lomsadze, M Borodovsky Journal of Applied Probability 42 (3), 632-642, 2005 | 31 | 2005 |
| A step toward balance: thrombin generation improvement via procoagulant factor and antithrombin supplementation AY Mitrophanov, F Szlam, RM Sniecinski, JH Levy, J Reifman Anesthesia & Analgesia 123 (3), 535-546, 2016 | 28 | 2016 |
| Stability estimates for finite homogeneous continuous-time Markov chains AY Mitrophanov Theory of Probability & Its Applications 50 (2), 319-326, 2006 | 27 | 2006 |
Mark BorodovskyRegents' Professor, Georgia TechVerified email at gatech.edu
