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Items: 1 to 50 of 69

1.

Sensitivity Analysis for Multiscale Stochastic Reaction Networks Using Hybrid Approximations.

Gupta A, Khammash M.

Bull Math Biol. 2018 Oct 9. doi: 10.1007/s11538-018-0521-4. [Epub ahead of print]

PMID:
30302636
2.

Synthetic control systems for high performance gene expression in mammalian cells.

Lillacci G, Benenson Y, Khammash M.

Nucleic Acids Res. 2018 Oct 12;46(18):9855-9863. doi: 10.1093/nar/gky795.

3.

Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation.

Benzinger D, Khammash M.

Nat Commun. 2018 Aug 30;9(1):3521. doi: 10.1038/s41467-018-05882-2.

4.

Principles of Systems Biology, No. 30.

Garcia HG, Benzinger D, Rullan M, Milias-Argeitis A, Khammash M, Deutschbauer AM, Langdon EM, Gladfelter AS.

Cell Syst. 2018 Jul 25;7(1):1-2. doi: 10.1016/j.cels.2018.07.002.

PMID:
30048618
5.

Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks.

Briat C, Gupta A, Khammash M.

J R Soc Interface. 2018 Jun;15(143). pii: 20180079. doi: 10.1098/rsif.2018.0079.

PMID:
29899158
6.

An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation.

Rullan M, Benzinger D, Schmidt GW, Milias-Argeitis A, Khammash M.

Mol Cell. 2018 May 17;70(4):745-756.e6. doi: 10.1016/j.molcel.2018.04.012.

7.

Pattern of burn injury at north of Jordan.

Bataineh ZA, Al Quran TM, Al Balas H, Khammash MR.

Int J Burns Trauma. 2018 Feb 5;8(1):1-5. eCollection 2018.

8.

Perfect Adaptation and Optimal Equilibrium Productivity in a Simple Microbial Biofuel Metabolic Pathway Using Dynamic Integral Control.

Briat C, Khammash M.

ACS Synth Biol. 2018 Feb 16;7(2):419-431. doi: 10.1021/acssynbio.7b00188. Epub 2018 Jan 30.

PMID:
29343065
9.

A finite state projection algorithm for the stationary solution of the chemical master equation.

Gupta A, Mikelson J, Khammash M.

J Chem Phys. 2017 Oct 21;147(15):154101. doi: 10.1063/1.5006484.

PMID:
29055349
10.

Dynamic Blue Light-Inducible T7 RNA Polymerases (Opto-T7RNAPs) for Precise Spatiotemporal Gene Expression Control.

Baumschlager A, Aoki SK, Khammash M.

ACS Synth Biol. 2017 Nov 17;6(11):2157-2167. doi: 10.1021/acssynbio.7b00169. Epub 2017 Oct 18.

PMID:
29045151
11.

Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate.

Gupta A, Milias-Argeitis A, Khammash M.

J R Soc Interface. 2017 Jul;14(132). pii: 20170311. doi: 10.1098/rsif.2017.0311.

12.

Parameter inference for stochastic single-cell dynamics from lineage tree data.

Kuzmanovska I, Milias-Argeitis A, Mikelson J, Zechner C, Khammash M.

BMC Syst Biol. 2017 Apr 26;11(1):52. doi: 10.1186/s12918-017-0425-1.

13.

Noise Induces the Population-Level Entrainment of Incoherent, Uncoupled Intracellular Oscillators.

Gupta A, Hepp B, Khammash M.

Cell Syst. 2016 Dec 21;3(6):521-531.e13. doi: 10.1016/j.cels.2016.10.006. Epub 2016 Nov 3.

14.

Design of a Synthetic Integral Feedback Circuit: Dynamic Analysis and DNA Implementation.

Briat C, Zechner C, Khammash M.

ACS Synth Biol. 2016 Oct 21;5(10):1108-1116. Epub 2016 Jul 8.

PMID:
27345033
15.

Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth.

Milias-Argeitis A, Rullan M, Aoki SK, Buchmann P, Khammash M.

Nat Commun. 2016 Aug 26;7:12546. doi: 10.1038/ncomms12546.

16.

Implementation Considerations, Not Topological Differences, Are the Main Determinants of Noise Suppression Properties in Feedback and Incoherent Feedforward Circuits.

Buzi G, Khammash M.

PLoS Comput Biol. 2016 Jun 3;12(6):e1004958. doi: 10.1371/journal.pcbi.1004958. eCollection 2016 Jun.

17.

Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks.

Briat C, Gupta A, Khammash M.

Cell Syst. 2016 Jan 27;2(1):15-26. doi: 10.1016/j.cels.2016.01.004. Epub 2016 Jan 27.

18.

Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks.

Briat C, Gupta A, Khammash M.

Cell Syst. 2016 Feb 24;2(2):133. doi: 10.1016/j.cels.2016.02.010. Epub 2016 Feb 24. No abstract available.

19.

Molecular circuits for dynamic noise filtering.

Zechner C, Seelig G, Rullan M, Khammash M.

Proc Natl Acad Sci U S A. 2016 Apr 26;113(17):4729-34. doi: 10.1073/pnas.1517109113. Epub 2016 Apr 12.

20.

An engineering viewpoint on biological robustness.

Khammash M.

BMC Biol. 2016 Mar 23;14:22. doi: 10.1186/s12915-016-0241-x. Review.

21.

Digital Quantification of Proteins and mRNA in Single Mammalian Cells.

Albayrak C, Jordi CA, Zechner C, Lin J, Bichsel CA, Khammash M, Tay S.

Mol Cell. 2016 Mar 17;61(6):914-24. doi: 10.1016/j.molcel.2016.02.030.

22.

Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks.

Milias-Argeitis A, Engblom S, Bauer P, Khammash M.

J R Soc Interface. 2015 Dec 6;12(113):20150831. doi: 10.1098/rsif.2015.0831.

23.

Iterative experiment design guides the characterization of a light-inducible gene expression circuit.

Ruess J, Parise F, Milias-Argeitis A, Khammash M, Lygeros J.

Proc Natl Acad Sci U S A. 2015 Jun 30;112(26):8148-53. doi: 10.1073/pnas.1423947112. Epub 2015 Jun 17.

24.

Cell lineage branching as a strategy for proliferative control.

Buzi G, Lander AD, Khammash M.

BMC Biol. 2015 Feb 19;13:13. doi: 10.1186/s12915-015-0122-8.

25.

Adaptive hybrid simulations for multiscale stochastic reaction networks.

Hepp B, Gupta A, Khammash M.

J Chem Phys. 2015 Jan 21;142(3):034118. doi: 10.1063/1.4905196.

PMID:
25612700
26.

An efficient and unbiased method for sensitivity analysis of stochastic reaction networks.

Gupta A, Khammash M.

J R Soc Interface. 2014 Dec 6;11(101):20140979. doi: 10.1098/rsif.2014.0979.

27.

Fast variance reduction for steady-state simulation and sensitivity analysis of stochastic chemical systems using shadow function estimators.

Milias-Argeitis A, Lygeros J, Khammash M.

J Chem Phys. 2014 Jul 14;141(2):024104. doi: 10.1063/1.4886935.

PMID:
25027996
28.

A scalable computational framework for establishing long-term behavior of stochastic reaction networks.

Gupta A, Briat C, Khammash M.

PLoS Comput Biol. 2014 Jun 26;10(6):e1003669. doi: 10.1371/journal.pcbi.1003669. eCollection 2014 Jun.

29.

Equilibrium distributions of simple biochemical reaction systems for time-scale separation in stochastic reaction networks.

Mélykúti B, Hespanha JP, Khammash M.

J R Soc Interface. 2014 Aug 6;11(97):20140054. doi: 10.1098/rsif.2014.0054.

30.

Direct solution of the Chemical Master Equation using quantized tensor trains.

Kazeev V, Khammash M, Nip M, Schwab C.

PLoS Comput Biol. 2014 Mar 13;10(3):e1003359. doi: 10.1371/journal.pcbi.1003359. eCollection 2014 Mar.

31.

Overexpression of heat shock protein 72 attenuates NF-κB activation using a combination of regulatory mechanisms in microglia.

Sheppard PW, Sun X, Khammash M, Giffard RG.

PLoS Comput Biol. 2014 Feb 6;10(2):e1003471. doi: 10.1371/journal.pcbi.1003471. eCollection 2014 Feb.

32.

Acute appendicitis in the elderly: risk factors for perforation.

Omari AH, Khammash MR, Qasaimeh GR, Shammari AK, Yaseen MK, Hammori SK.

World J Emerg Surg. 2014 Jan 15;9(1):6. doi: 10.1186/1749-7922-9-6.

33.

Spatial stochastic dynamics enable robust cell polarization.

Lawson MJ, Drawert B, Khammash M, Petzold L, Yi TM.

PLoS Comput Biol. 2013;9(7):e1003139. doi: 10.1371/journal.pcbi.1003139. Epub 2013 Jul 25.

34.

The signal within the noise: efficient inference of stochastic gene regulation models using fluorescence histograms and stochastic simulations.

Lillacci G, Khammash M.

Bioinformatics. 2013 Sep 15;29(18):2311-9. doi: 10.1093/bioinformatics/btt380. Epub 2013 Jul 2.

PMID:
23821649
35.

Patterns of anterior abdominal stab wounds and their management at Princess Basma teaching hospital, North of Jordan.

Omari A, Bani-Yaseen M, Khammash M, Qasaimeh G, Eqab F, Jaddou H.

World J Surg. 2013 May;37(5):1162-8. doi: 10.1007/s00268-013-1931-y.

PMID:
23400590
36.

Systematic identification of signal-activated stochastic gene regulation.

Neuert G, Munsky B, Tan RZ, Teytelman L, Khammash M, van Oudenaarden A.

Science. 2013 Feb 1;339(6119):584-7. doi: 10.1126/science.1231456.

37.

SPSens: a software package for stochastic parameter sensitivity analysis of biochemical reaction networks.

Sheppard PW, Rathinam M, Khammash M.

Bioinformatics. 2013 Jan 1;29(1):140-2. doi: 10.1093/bioinformatics/bts642. Epub 2012 Oct 25.

PMID:
23104889
38.

Simulation of stochastic systems via polynomial chaos expansions and convex optimization.

Fagiano L, Khammash M.

Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Sep;86(3 Pt 2):036702. Epub 2012 Sep 17.

PMID:
23031048
39.

A pathwise derivative approach to the computation of parameter sensitivities in discrete stochastic chemical systems.

Sheppard PW, Rathinam M, Khammash M.

J Chem Phys. 2012 Jan 21;136(3):034115. doi: 10.1063/1.3677230.

40.

Changing pattern of intestinal obstruction in northern Jordan.

Omari AH, Alkhatib LL, Khammash MR.

World J Surg. 2012 Feb;36(2):437-40. doi: 10.1007/s00268-011-1361-7.

PMID:
22139327
41.

In silico feedback for in vivo regulation of a gene expression circuit.

Milias-Argeitis A, Summers S, Stewart-Ornstein J, Zuleta I, Pincus D, El-Samad H, Khammash M, Lygeros J.

Nat Biotechnol. 2011 Nov 6;29(12):1114-6. doi: 10.1038/nbt.2018.

42.

Quantitative characterization and analysis of the dynamic NF-κB response in microglia.

Sheppard PW, Sun X, Emery JF, Giffard RG, Khammash M.

BMC Bioinformatics. 2011 Jul 5;12:276. doi: 10.1186/1471-2105-12-276.

43.

Parasitic leiomyoma. A rare cause of inguinal mass in females.

Al Manasra AR, Malkawi AS, Khammash MR.

Saudi Med J. 2011 Jun;32(6):633-5.

PMID:
21666948
44.

Stochastic reduction method for biological chemical kinetics using time-scale separation.

Pahlajani CD, Atzberger PJ, Khammash M.

J Theor Biol. 2011 Mar 7;272(1):96-112. doi: 10.1016/j.jtbi.2010.11.023. Epub 2010 Nov 30.

PMID:
21126524
45.

Analysis of stochastic strategies in bacterial competence: a master equation approach.

Dandach SH, Khammash M.

PLoS Comput Biol. 2010 Nov 11;6(11):e1000985. doi: 10.1371/journal.pcbi.1000985.

46.

Identification from stochastic cell-to-cell variation: a genetic switch case study.

Munsky B, Khammash M.

IET Syst Biol. 2010 Nov;4(6):356-66. doi: 10.1049/iet-syb.2010.0013.

PMID:
21073235
47.

Parameter estimation and model selection in computational biology.

Lillacci G, Khammash M.

PLoS Comput Biol. 2010 Mar 5;6(3):e1000696. doi: 10.1371/journal.pcbi.1000696.

48.

The diffusive finite state projection algorithm for efficient simulation of the stochastic reaction-diffusion master equation.

Drawert B, Lawson MJ, Petzold L, Khammash M.

J Chem Phys. 2010 Feb 21;132(7):074101. doi: 10.1063/1.3310809.

49.

Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks.

Rathinam M, Sheppard PW, Khammash M.

J Chem Phys. 2010 Jan 21;132(3):034103. doi: 10.1063/1.3280166.

50.

Listening to the noise: random fluctuations reveal gene network parameters.

Munsky B, Trinh B, Khammash M.

Mol Syst Biol. 2009;5:318. doi: 10.1038/msb.2009.75. Epub 2009 Oct 13.

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