| Year |
Citation |
Score |
| 2022 |
Gupta A, Khammash M. Universal structural requirements for maximal robust perfect adaptation in biomolecular networks. Proceedings of the National Academy of Sciences of the United States of America. 119: e2207802119. PMID 36256812 DOI: 10.1073/pnas.2207802119 |
0.332 |
|
| 2020 |
Frei T, Cella F, Tedeschi F, Gutiérrez J, Stan GB, Khammash M, Siciliano V. Characterization and mitigation of gene expression burden in mammalian cells. Nature Communications. 11: 4641. PMID 32934213 DOI: 10.1038/S41467-020-18392-X |
0.332 |
|
| 2020 |
Baumschlager A, Rullan M, Khammash M. Exploiting natural chemical photosensitivity of anhydrotetracycline and tetracycline for dynamic and setpoint chemo-optogenetic control. Nature Communications. 11: 3834. PMID 32737309 DOI: 10.1038/S41467-020-17677-5 |
0.379 |
|
| 2020 |
Briat C, Khammash M. Ergodicity Analysis and Antithetic Integral Control of a Class of Stochastic Reaction Networks with Delays Siam Journal On Applied Dynamical Systems. 19: 1575-1608. DOI: 10.1137/19M1286219 |
0.381 |
|
| 2020 |
Siami M, Motee N, Buzi G, Bamieh B, Khammash M, Doyle JC. Fundamental Limits and Tradeoffs in Autocatalytic Pathways Ieee Transactions On Automatic Control. 65: 733-740. DOI: 10.1109/Tac.2019.2921671 |
0.389 |
|
| 2019 |
Blum Y, Mikelson J, Dobrzyński M, Ryu H, Jacques MA, Jeon NL, Khammash M, Pertz O. Temporal perturbation of ERK dynamics reveals network architecture of FGF2/MAPK signaling. Molecular Systems Biology. 15: e8947. PMID 31777174 DOI: 10.15252/Msb.20198947 |
0.326 |
|
| 2019 |
Aoki SK, Lillacci G, Gupta A, Baumschlager A, Schweingruber D, Khammash M. A universal biomolecular integral feedback controller for robust perfect adaptation. Nature. PMID 31217585 DOI: 10.1038/S41586-019-1321-1 |
0.767 |
|
| 2018 |
Gupta A, Khammash M. Sensitivity Analysis for Multiscale Stochastic Reaction Networks Using Hybrid Approximations. Bulletin of Mathematical Biology. PMID 30302636 DOI: 10.1007/S11538-018-0521-4 |
0.431 |
|
| 2018 |
Lillacci G, Benenson Y, Khammash M. Synthetic control systems for high performance gene expression in mammalian cells. Nucleic Acids Research. PMID 30203050 DOI: 10.1093/Nar/Gky795 |
0.753 |
|
| 2018 |
Benzinger D, Khammash M. Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation. Nature Communications. 9: 3521. PMID 30166548 DOI: 10.1038/S41467-018-05882-2 |
0.367 |
|
| 2018 |
Briat C, Gupta A, Khammash M. Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks. Journal of the Royal Society, Interface. 15. PMID 29899158 DOI: 10.1098/Rsif.2018.0079 |
0.459 |
|
| 2018 |
Rullan M, Benzinger D, Schmidt GW, Milias-Argeitis A, Khammash M. An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation. Molecular Cell. 70: 745-756.e6. PMID 29775585 DOI: 10.1016/J.Molcel.2018.04.012 |
0.322 |
|
| 2018 |
Gupta A, Khammash M. Computational Identification of Irreducible State-Spaces for Stochastic Reaction Networks Siam Journal On Applied Dynamical Systems. 17: 1213-1266. DOI: 10.1137/17M1134299 |
0.397 |
|
| 2018 |
Gupta A, Rathinam M, Khammash M. Estimation of Parameter Sensitivities for Stochastic Reaction Networks Using Tau-Leap Simulations Siam Journal On Numerical Analysis. 56: 1134-1167. DOI: 10.1137/17M1119445 |
0.417 |
|
| 2017 |
Gupta A, Mikelson J, Khammash M. A finite state projection algorithm for the stationary solution of the chemical master equation. The Journal of Chemical Physics. 147: 154101. PMID 29055349 DOI: 10.1063/1.5006484 |
0.311 |
|
| 2017 |
Baumschlager A, Aoki SK, Khammash M. Dynamic Blue Light-Inducible T7 RNA Polymerases (Opto-T7RNAPs) for Precise Spatiotemporal Gene Expression Control. Acs Synthetic Biology. PMID 29045151 DOI: 10.1021/Acssynbio.7B00169 |
0.314 |
|
| 2017 |
Kuzmanovska I, Milias-Argeitis A, Mikelson J, Zechner C, Khammash M. Parameter inference for stochastic single-cell dynamics from lineage tree data. Bmc Systems Biology. 11: 52. PMID 28446158 DOI: 10.1186/S12918-017-0425-1 |
0.307 |
|
| 2017 |
Briat C, Khammash M. Simple interval observers for linear impulsive systems with applications to sampled-data and switched systems Ifac-Papersonline. 50: 5079-5084. DOI: 10.1016/J.Ifacol.2017.08.773 |
0.319 |
|
| 2017 |
Briat C, Khammash M. Robust and structural ergodicity analysis of stochastic biomolecular networks involving synthetic antithetic integral controllers Ifac-Papersonline. 50: 10918-10923. DOI: 10.1016/J.Ifacol.2017.08.2457 |
0.42 |
|
| 2017 |
Briat C, Khammash M. Stability analysis of LPV systems with piecewise differentiable parameters Ifac-Papersonline. 50: 7554-7559. DOI: 10.1016/J.Ifacol.2017.08.1191 |
0.334 |
|
| 2016 |
Gupta A, Hepp B, Khammash M. Noise Induces the Population-Level Entrainment of Incoherent, Uncoupled Intracellular Oscillators. Cell Systems. PMID 27818082 DOI: 10.1016/J.Cels.2016.10.006 |
0.303 |
|
| 2016 |
Milias-Argeitis A, Rullan M, Aoki SK, Buchmann P, Khammash M. Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nature Communications. 7: 12546. PMID 27562138 DOI: 10.1038/Ncomms12546 |
0.344 |
|
| 2016 |
Briat C, Zechner C, Khammash MH. Design of a synthetic integral feedback circuit: dynamic analysis and DNA implementation. Acs Synthetic Biology. PMID 27345033 DOI: 10.1021/Acssynbio.6B00014 |
0.382 |
|
| 2016 |
Buzi G, Khammash M. Implementation Considerations, Not Topological Differences, Are the Main Determinants of Noise Suppression Properties in Feedback and Incoherent Feedforward Circuits. Plos Computational Biology. 12: e1004958. PMID 27257684 DOI: 10.1371/Journal.Pcbi.1004958 |
0.343 |
|
| 2016 |
Briat C, Gupta A, Khammash M. Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks. Cell Systems. 2: 15-26. PMID 27136686 DOI: 10.1016/J.Cels.2016.01.004 |
0.422 |
|
| 2016 |
Khammash M. An engineering viewpoint on biological robustness. Bmc Biology. 14: 22. PMID 27007299 DOI: 10.1186/S12915-016-0241-X |
0.309 |
|
| 2016 |
Albayrak C, Jordi CA, Zechner C, Lin J, Bichsel CA, Khammash M, Tay S. Digital Quantification of Proteins and mRNA in Single Mammalian Cells. Molecular Cell. 61: 914-924. PMID 26990994 DOI: 10.1016/J.Molcel.2016.02.030 |
0.314 |
|
| 2016 |
Briat C, Khammash M. Interval peak-to-peak observers for continuous- and discrete-time systems with persistent inputs and delays Automatica. 74: 206-213. DOI: 10.1016/J.Automatica.2016.07.043 |
0.334 |
|
| 2015 |
Milias-Argeitis A, Engblom S, Bauer P, Khammash M. Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks. Journal of the Royal Society, Interface / the Royal Society. 12. PMID 26609065 DOI: 10.1098/Rsif.2015.0831 |
0.375 |
|
| 2015 |
Ruess J, Parise F, Milias-Argeitis A, Khammash M, Lygeros J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. Proceedings of the National Academy of Sciences of the United States of America. 112: 8148-53. PMID 26085136 DOI: 10.1073/Pnas.1423947112 |
0.389 |
|
| 2015 |
Buzi G, Lander AD, Khammash M. Cell lineage branching as a strategy for proliferative control. Bmc Biology. 13: 13. PMID 25857410 DOI: 10.1186/S12915-015-0122-8 |
0.332 |
|
| 2015 |
Hepp B, Gupta A, Khammash M. Adaptive hybrid simulations for multiscale stochastic reaction networks. The Journal of Chemical Physics. 142: 034118. PMID 25612700 DOI: 10.1063/1.4905196 |
0.406 |
|
| 2015 |
Khammash M, Munsky B. System theory applications in biology: From stochastic chemical kinetics to deterministic model invalidation 2007 European Control Conference, Ecc 2007. 824-831. |
0.318 |
|
| 2014 |
Gupta A, Khammash M. An efficient and unbiased method for sensitivity analysis of stochastic reaction networks. Journal of the Royal Society, Interface / the Royal Society. 11: 20140979. PMID 25354975 DOI: 10.1098/Rsif.2014.0979 |
0.41 |
|
| 2014 |
Milias-Argeitis A, Lygeros J, Khammash M. Fast variance reduction for steady-state simulation and sensitivity analysis of stochastic chemical systems using shadow function estimators. The Journal of Chemical Physics. 141: 024104. PMID 25027996 DOI: 10.1063/1.4886935 |
0.363 |
|
| 2014 |
Gupta A, Briat C, Khammash M. A scalable computational framework for establishing long-term behavior of stochastic reaction networks. Plos Computational Biology. 10: e1003669. PMID 24968191 DOI: 10.1371/Journal.Pcbi.1003669 |
0.432 |
|
| 2014 |
Mélykúti B, Hespanha JP, Khammash M. Equilibrium distributions of simple biochemical reaction systems for time-scale separation in stochastic reaction networks. Journal of the Royal Society, Interface / the Royal Society. 11: 20140054. PMID 24920118 DOI: 10.1098/Rsif.2014.0054 |
0.378 |
|
| 2014 |
Kazeev V, Khammash M, Nip M, Schwab C. Direct solution of the Chemical Master Equation using quantized tensor trains. Plos Computational Biology. 10: e1003359. PMID 24626049 DOI: 10.1371/Journal.Pcbi.1003359 |
0.371 |
|
| 2014 |
Sheppard PW, Sun X, Khammash M, Giffard RG. Overexpression of heat shock protein 72 attenuates NF-κB activation using a combination of regulatory mechanisms in microglia. Plos Computational Biology. 10: e1003471. PMID 24516376 DOI: 10.1371/Journal.Pcbi.1003471 |
0.693 |
|
| 2014 |
Sheppard PW, Sun X, Khammash M, Giffard RG. Updated mechanistic diagram for Hsp72 regulation of NF-κB signaling in microglia. Plos Computational Biology. DOI: 10.1371/Journal.Pcbi.1003471.G007 |
0.666 |
|
| 2014 |
Gupta A, Khammash M. Sensitivity analysis for stochastic chemical reaction networks with multiple time-scales Electronic Journal of Probability. 19. DOI: 10.1214/Ejp.V19-3246 |
0.439 |
|
| 2014 |
Gupta A, Khammash M. Determining the long-term behavior of cell populations: A new procedure for detecting ergodicity in large stochastic reaction networks Ifac Proceedings Volumes (Ifac-Papersonline). 19: 1711-1716. |
0.313 |
|
| 2013 |
Lawson MJ, Drawert B, Khammash M, Petzold L, Yi TM. Spatial stochastic dynamics enable robust cell polarization. Plos Computational Biology. 9: e1003139. PMID 23935469 DOI: 10.1371/Journal.Pcbi.1003139 |
0.329 |
|
| 2013 |
Lillacci G, Khammash M. The signal within the noise: efficient inference of stochastic gene regulation models using fluorescence histograms and stochastic simulations. Bioinformatics (Oxford, England). 29: 2311-9. PMID 23821649 DOI: 10.1093/Bioinformatics/Btt380 |
0.796 |
|
| 2013 |
Neuert G, Munsky B, Tan RZ, Teytelman L, Khammash M, van Oudenaarden A. Systematic identification of signal-activated stochastic gene regulation. Science (New York, N.Y.). 339: 584-7. PMID 23372015 DOI: 10.1126/Science.1231456 |
0.374 |
|
| 2013 |
Sheppard PW, Rathinam M, Khammash M. SPSens: a software package for stochastic parameter sensitivity analysis of biochemical reaction networks. Bioinformatics (Oxford, England). 29: 140-2. PMID 23104889 DOI: 10.1093/Bioinformatics/Bts642 |
0.731 |
|
| 2013 |
Gupta A, Khammash M. Unbiased estimation of parameter sensitivities for stochastic chemical reaction networks Siam Journal On Scientific Computing. 35. DOI: 10.1137/120898747 |
0.394 |
|
| 2012 |
Fagiano L, Khammash M. Simulation of stochastic systems via polynomial chaos expansions and convex optimization. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 86: 036702. PMID 23031048 DOI: 10.1103/Physreve.86.036702 |
0.38 |
|
| 2012 |
Sheppard PW, Rathinam M, Khammash M. A pathwise derivative approach to the computation of parameter sensitivities in discrete stochastic chemical systems. The Journal of Chemical Physics. 136: 034115. PMID 22280752 DOI: 10.1063/1.3677230 |
0.736 |
|
| 2012 |
Hasenauer J, Löhning M, Khammash M, Allgöwer F. Dynamical optimization using reduced order models: A method to guarantee performance Journal of Process Control. 22: 1490-1501. DOI: 10.1016/J.Jprocont.2012.01.017 |
0.398 |
|
| 2012 |
Motee N, Bamieh B, Khammash M. Stability analysis of quasi-polynomial dynamical systems with applications to biological network models Automatica. 48: 2945-2950. DOI: 10.1016/J.Automatica.2012.06.094 |
0.387 |
|
| 2012 |
Lillacci G, Khammash M. A distribution-matching method for parameter estimation and model selection in computational biology International Journal of Robust and Nonlinear Control. 22: 1065-1081. DOI: 10.1002/Rnc.2794 |
0.779 |
|
| 2011 |
Milias-Argeitis A, Summers S, Stewart-Ornstein J, Zuleta I, Pincus D, El-Samad H, Khammash M, Lygeros J. In silico feedback for in vivo regulation of a gene expression circuit. Nature Biotechnology. 29: 1114-6. PMID 22057053 DOI: 10.1038/Nbt.2018 |
0.703 |
|
| 2011 |
Sheppard PW, Sun X, Emery JF, Giffard RG, Khammash M. Quantitative characterization and analysis of the dynamic NF-κB response in microglia. Bmc Bioinformatics. 12: 276. PMID 21729324 DOI: 10.1186/1471-2105-12-276 |
0.718 |
|
| 2011 |
Pahlajani CD, Atzberger PJ, Khammash M. Stochastic reduction method for biological chemical kinetics using time-scale separation. Journal of Theoretical Biology. 272: 96-112. PMID 21126524 DOI: 10.1016/J.Jtbi.2010.11.023 |
0.385 |
|
| 2011 |
Lillacci G, Khammash M. Model selection in stochastic chemical reaction networks using flow cytometry data Proceedings of the Ieee Conference On Decision and Control. 1680-1685. DOI: 10.1109/CDC.2011.6161417 |
0.766 |
|
| 2011 |
Khammash M. Pili Expression in Uropathgenic E. coli: Stochastic Switching and Epigenetic Control Biophysical Journal. 100. DOI: 10.1016/J.Bpj.2010.12.1152 |
0.32 |
|
| 2010 |
Dandach SH, Khammash M. Analysis of stochastic strategies in bacterial competence: a master equation approach. Plos Computational Biology. 6: e1000985. PMID 21085679 DOI: 10.1371/Journal.Pcbi.1000985 |
0.377 |
|
| 2010 |
Munsky B, Khammash M. Identification from stochastic cell-to-cell variation: a genetic switch case study. Iet Systems Biology. 4: 356-66. PMID 21073235 DOI: 10.1049/Iet-Syb.2010.0013 |
0.355 |
|
| 2010 |
Lillacci G, Khammash M. Parameter estimation and model selection in computational biology. Plos Computational Biology. 6: e1000696. PMID 20221262 DOI: 10.1371/Journal.Pcbi.1000696 |
0.778 |
|
| 2010 |
Rathinam M, Sheppard PW, Khammash M. Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks. The Journal of Chemical Physics. 132: 034103. PMID 20095724 DOI: 10.1063/1.3280166 |
0.743 |
|
| 2010 |
Lillacci G, Khammash M. Parameter identification of biological networks using extended kalman filtering and χ2 criteria Proceedings of the Ieee Conference On Decision and Control. 3367-3372. DOI: 10.1109/CDC.2010.5717460 |
0.761 |
|
| 2010 |
El-Samad H, Khammash M. Modelling and analysis of gene regulatory network using feedback control theory International Journal of Systems Science. 41: 17-33. DOI: 10.1080/00207720903144545 |
0.663 |
|
| 2009 |
Munsky B, Trinh B, Khammash M. Listening to the noise: random fluctuations reveal gene network parameters. Molecular Systems Biology. 5: 318. PMID 19888213 DOI: 10.1038/Msb.2009.75 |
0.409 |
|
| 2009 |
Khammash M. Stochastic gene expression: Modeling, analysis, and identification Ifac Proceedings Volumes (Ifac-Papersonline). 15: 1022-1028. DOI: 10.3182/20090706-3-FR-2004.0445 |
0.314 |
|
| 2008 |
Munsky B, Khammash M. Transient analysis of stochastic switches and trajectories with applications to gene regulatory networks. Iet Systems Biology. 2: 323-33. PMID 19045827 DOI: 10.1049/Iet-Syb:20070082 |
0.387 |
|
| 2008 |
Khammash M. Reverse engineering: the architecture of biological networks. Biotechniques. 44: 323-9. PMID 18361784 DOI: 10.2144/000112772 |
0.398 |
|
| 2008 |
Purvis KB, Åström KJ, Khammash M. Estimation and optimal configurations for localization using cooperative UAVs Ieee Transactions On Control Systems Technology. 16: 947-958. DOI: 10.1109/Tcst.2007.916348 |
0.728 |
|
| 2008 |
Khammash M, Tomlin CJ, Vidyasagar M. Guest editorial - Special issue on systems biology Ieee Transactions On Automatic Control. 53: 4-7. DOI: 10.1109/Tac.2007.913343 |
0.356 |
|
| 2008 |
Munsky B, Khammash M. The finite state projection approach for the analysis of stochastic noise in gene networks Ieee Transactions On Automatic Control. 53: 201-214. DOI: 10.1109/Tac.2007.911361 |
0.427 |
|
| 2007 |
Purvis KB, Åström KJ, Khammash M. Online control strategies for highly coupled cooperative UAVs Proceedings of the American Control Conference. 3961-3966. DOI: 10.1109/ACC.2007.4283125 |
0.727 |
|
| 2007 |
Munsky B, Peleš S, Khammash M. Stochastic analysis of gene regulatory networks using finite state projections and singular perturbation Proceedings of the American Control Conference. 1323-1328. DOI: 10.1109/ACC.2007.4283077 |
0.323 |
|
| 2007 |
Munsky B, Khammash M. A multiple time interval finite state projection algorithm for the solution to the chemical master equation Journal of Computational Physics. 226: 818-835. DOI: 10.1016/J.Jcp.2007.05.016 |
0.331 |
|
| 2006 |
Peles S, Munsky B, Khammash M. Reduction and solution of the chemical master equation using time scale separation and finite state projection. The Journal of Chemical Physics. 125: 204104. PMID 17144687 DOI: 10.1063/1.2397685 |
0.4 |
|
| 2006 |
Kurata H, El-Samad H, Iwasaki R, Ohtake H, Doyle JC, Grigorova I, Gross CA, Khammash M. Module-based analysis of robustness tradeoffs in the heat shock response system. Plos Computational Biology. 2: e59. PMID 16863396 DOI: 10.1371/Journal.Pcbi.0020059 |
0.647 |
|
| 2006 |
El-Samad H, Khammash M. Regulated degradation is a mechanism for suppressing stochastic fluctuations in gene regulatory networks. Biophysical Journal. 90: 3749-61. PMID 16500958 DOI: 10.1529/Biophysj.105.060491 |
0.682 |
|
| 2006 |
Munsky B, Khammash M. The finite state projection algorithm for the solution of the chemical master equation. The Journal of Chemical Physics. 124: 044104. PMID 16460146 DOI: 10.1063/1.2145882 |
0.317 |
|
| 2006 |
El-Samad H, Prajna S, Papachristodoulou A, Doyle J, Khammash M. Advanced methods and algorithms for biological networks analysis Proceedings of the Ieee. 94: 832-852. DOI: 10.1109/JPROC.2006.871776 |
0.605 |
|
| 2006 |
El-Samad H, Khammash M. Coherence resonance: A mechanism for noise induced stable oscillations in gene regulatory networks Proceedings of the Ieee Conference On Decision and Control. 2382-2387. |
0.613 |
|
| 2005 |
El-Samad H, Kurata H, Doyle JC, Gross CA, Khammash M. Surviving heat shock: control strategies for robustness and performance. Proceedings of the National Academy of Sciences of the United States of America. 102: 2736-41. PMID 15668395 DOI: 10.1073/Pnas.0403510102 |
0.669 |
|
| 2005 |
El Samad H, Khammash M, Homescu C, Petzold L. Optimal performance of the heat-shock gene regulatory network Ifac Proceedings Volumes (Ifac-Papersonline). 16: 19-24. DOI: 10.3182/20050703-6-Cz-1902.02207 |
0.754 |
|
| 2005 |
Khammash M, El Samad H. Stochastic modeling and analysis of genetic networks Proceedings of the 44th Ieee Conference On Decision and Control, and the European Control Conference, Cdc-Ecc '05. 2005: 2320-2325. DOI: 10.1109/CDC.2005.1582508 |
0.788 |
|
| 2005 |
El Samad H, Khammash M, Petzold L, Gillespie D. Stochastic modelling of gene regulatory networks International Journal of Robust and Nonlinear Control. 15: 691-711. DOI: 10.1002/Rnc.1018 |
0.79 |
|
| 2005 |
Purvis KB, Åström KJ, Khammash M. Estimating radar positions using cooperative unmanned air vehicle teams Proceedings of the American Control Conference. 5: 3512-3517. |
0.68 |
|
| 2005 |
El Samad H, Del Vecchio D, Khammash M. Repressilators and promotilators: Loop dynamics in synthetic gene networks Proceedings of the American Control Conference. 6: 4405-4410. |
0.772 |
|
| 2004 |
Qiu W, Vittal V, Khammash M. Decentralized power system stabilizer design using linear parameter varying approach Ieee Transactions On Power Systems. 19: 1951-1960. DOI: 10.1109/Tpwrs.2004.836269 |
0.329 |
|
| 2004 |
Qi X, Salapaka MV, Voulgaris PG, Khammash M. Structured optimal and robust control with multiple criteria: A convex solution Ieee Transactions On Automatic Control. 49: 1623-1640. DOI: 10.1109/Tac.2004.835357 |
0.386 |
|
| 2004 |
Khammash M, El-Samad H. Systems biology: From physiology to gene regulation Ieee Control Systems Magazine. 24: 62-76. DOI: 10.1109/Mcs.2004.1316654 |
0.669 |
|
| 2004 |
El-Samad H, Khammash M. Intrinsic noise rejection in gene networks by regulation of stability International Symposium On Control, Communications and Signal Processing, Isccsp. 187-190. |
0.643 |
|
| 2003 |
El-Samad H, Prajna S, Papachristodoulou A, Khammash M, Doyle JC. Model Validation and Robust Stability Analysis of the Bacterial Heat Shock Response Using SOSTOOLS Proceedings of the Ieee Conference On Decision and Control. 4: 3766-3771. |
0.586 |
|
| 2002 |
El-Samad H, Goff JP, Khammash M. Calcium homeostasis and parturient hypocalcemia: an integral feedback perspective. Journal of Theoretical Biology. 214: 17-29. PMID 11786029 DOI: 10.1006/Jtbi.2001.2422 |
0.614 |
|
| 2002 |
El-Samad H, Khammash M, Kurata H, Doyle J. Robustness analysis of the heat shock response in E. coli Proceedings of the American Control Conference. 3: 1742-1747. |
0.592 |
|
| 2001 |
Yu X, Khammash M, Vittal V. Robust design of a damping controller for static var compensators in power systems Proceedings of the Ieee Power Engineering Society Transmission and Distribution Conference. 2: 790. DOI: 10.1109/Mper.2001.4311549 |
0.31 |
|
| 2001 |
Khammash M, Salapaka MV, Van Voorhis T. Robust synthesis in ℓ1: A globally optimal solution Ieee Transactions On Automatic Control. 46: 1744-1754. DOI: 10.1109/9.964684 |
0.32 |
|
| 2001 |
Kurata i, El-Samad H, Yi TM, Khammash M, Doyle J. Feedback regulation of the heat shock response in E. coli Proceedings of the Ieee Conference On Decision and Control. 1: 837-842. |
0.622 |
|
| 2000 |
Malaterre PO, Khammash M. l1 controller design for a high-order 5-pool irrigation canal system Proceedings of the Ieee Conference On Decision and Control. 4: 3188-3193. DOI: 10.1115/1.1636776 |
0.334 |
|
| 2000 |
El-Samad H, Khammash M. Feedback analysis of calcium homeostasis and parturient paresis Proceedings - Ieee International Symposium On Bio-Informatics and Biomedical Engineering, Bibe 2000. 365-369. DOI: 10.1109/BIBE.2000.889629 |
0.545 |
|
| 2000 |
Khammash M. A new approach to the solution of the ℓ1 control problem: The scaled-Q method Ieee Transactions On Automatic Control. 45: 180-187. DOI: 10.1109/9.839942 |
0.301 |
|
| 2000 |
El-Samad H, Khammash M, Goff J. Calcium homeostasis: a feedback control point of view Proceedings of the American Control Conference. 5: 2962-2966. |
0.58 |
|
| 1999 |
Djukanovic M, Khammash M, Vittal V. Sequential synthesis of structured singular value based decentralized controllers in power systems Ieee Transactions On Power Systems. 14: 635-641. DOI: 10.1109/59.761891 |
0.309 |
|
| 1998 |
Djiikanovic MOR, Khammash M, Vittal V. Application of structured singular value theory for robust stability and control analysis in multimachine power systems par TII-numerical simula tions and results Ieee Transactions On Power Systems. 13: 1317-1322. DOI: 10.1109/59.736271 |
0.317 |
|
| 1998 |
Djukanovic M, Khammash M, Vittal V. Application of the structured singular value theory for robust stability and control analysis in multimachine power systems part-I: framework development Ieee Transactions On Power Systems. 13: 1311-1316. DOI: 10.1109/59.736270 |
0.316 |
|
| 1993 |
Khammash M, Pearson JB. Analysis and design for robust performance with structured uncertainty Systems and Control Letters. 20: 179-187. DOI: 10.1016/0167-6911(93)90059-F |
0.343 |
|
| 1992 |
Khammash M, Dahleh M. Time-varying control and the robust performance of systems with structured norm-bounded perturbations Automatica. 28: 819-821. DOI: 10.1016/0005-1098(92)90043-F |
0.306 |
|
| 1991 |
Khammash M, Pearson JB. Performance Robustness of Discrete-Time Systems with Structured Uncertainty Ieee Transactions On Automatic Control. 36: 398-412. DOI: 10.1109/9.75099 |
0.328 |
|
| 1990 |
Khammash M, Pearson JB. Robust disturbance rejection in l1 optimal control systems Systems and Control Letters. 14: 93-101. DOI: 10.1016/0167-6911(90)90024-O |
0.306 |
|
| Show low-probability matches. |