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Items: 16

1.

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.

2.

Assessment of the interaction between the flux-signaling metabolite fructose-1,6-bisphosphate and the bacterial transcription factors CggR and Cra.

Bley Folly B, Ortega AD, Hubmann G, Bonsing-Vedelaar S, Wijma HJ, van der Meulen P, Milias-Argeitis A, Heinemann M.

Mol Microbiol. 2018 Aug;109(3):278-290. doi: 10.1111/mmi.14008.

PMID:
29923648
3.

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.

4.

Dynamic single-cell NAD(P)H measurement reveals oscillatory metabolism throughout the E. coli cell division cycle.

Zhang Z, Milias-Argeitis A, Heinemann M.

Sci Rep. 2018 Feb 1;8(1):2162. doi: 10.1038/s41598-018-20550-7.

5.

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.

6.

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.

7.

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.

8.

Elucidation of Genetic Interactions in the Yeast GATA-Factor Network Using Bayesian Model Selection.

Milias-Argeitis A, Oliveira AP, Gerosa L, Falter L, Sauer U, Lygeros J.

PLoS Comput Biol. 2016 Mar 11;12(3):e1004784. doi: 10.1371/journal.pcbi.1004784. eCollection 2016 Mar.

9.

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.

10.

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.

11.

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
12.

Designing experiments to understand the variability in biochemical reaction networks.

Ruess J, Milias-Argeitis A, Lygeros J.

J R Soc Interface. 2013 Aug 28;10(88):20130588. doi: 10.1098/rsif.2013.0588. Print 2013 Nov 6.

13.

Steady-state simulation of metastable stochastic chemical systems.

Milias-Argeitis A, Lygeros J.

J Chem Phys. 2013 May 14;138(18):184109. doi: 10.1063/1.4804191.

PMID:
23676031
14.

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.

15.

Moment estimation for chemically reacting systems by extended Kalman filtering.

Ruess J, Milias-Argeitis A, Summers S, Lygeros J.

J Chem Phys. 2011 Oct 28;135(16):165102. doi: 10.1063/1.3654135.

PMID:
22047267
16.

Stochastic dynamics of genetic networks: modelling and parameter identification.

Cinquemani E, Milias-Argeitis A, Summers S, Lygeros J.

Bioinformatics. 2008 Dec 1;24(23):2748-54. doi: 10.1093/bioinformatics/btn527. Epub 2008 Oct 9.

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