Publications
*corresponding (co)author
2023
Takhaveev, V., Özsezen, S., Smith, E.N., Zylstra, A., Chaillet, M.L., Chen, H., Papagiannakis, A., Milias-Argeitis, A. and Heinemann, M., 2023. Temporal segregation of biosynthetic processes is responsible for metabolic oscillations during the budding yeast cell cycle. Nature Metabolism, 5(2), pp.294-313.
https://doi.org/10.1038/s42255-023-00741-x
2022
Vuillemenot, L.A.P. and Milias-Argeitis*, A., 2022. Sfp1 integrates TORC1 and PKA activity towards yeast ribosome biogenesis. bioRxiv preprint.
https://doi.org/10.1101/2022.12.02.518855
Guerra, P., Vuillemenot, L.A.P., van Oppen, Y., Been, M. and Milias-Argeitis*, A., 2022. TORC1 and PKA activity towards ribosome biogenesis oscillates in synchrony with the budding yeast cell cycle. Journal of Cell Science 135(8).
https://doi.org/10.1242/jcs.260378
Kurdyaeva, T. and Milias-Argeitis*, A., 2022. Propagation of initial condition uncertainty for linear dynamical systems: beyond the Gaussian assumption. Proceedings of the 2022 European Control Conference (ECC), pp. 1391-1396.
https://ieeexplore.ieee.org/document/9838074
Novarina, D., Koutsoumpa, A. and Milias-Argeitis*, A., 2022. A user-friendly and streamlined protocol for CRISPR/Cas9 genome editing in budding yeast. STAR Protocols 3(2), 101358.
https://star-protocols.cell.com/protocols/1707 (open-access)
Kruitbosch, H., Mzayek, Y., Omlor, S., Guerra, P. and Milias-Argeitis*, A., 2022. A convolutional neural network for segmentation of yeast cells without manual training annotations. Bioinformatics, 38(5), pp.1427–1433.
https://academic.oup.com/bioinformatics/article/38/5/1427/6459168 (open-access)
Guerra, P., Vuillemenot, L.A., Rae, B., Ladyhina, V. and Milias-Argeitis*, A., 2022. Systematic in vivo characterization of fluorescent protein maturation in budding yeast. ACS synthetic biology, 11(3), pp.1129-1141.
https://pubs.acs.org/doi/abs/10.1021/acssynbio.1c00387 (open-access)
2021
Novarina, D., Guerra, P. and Milias-Argeitis*, A., 2021. Vacuolar localization via the N-terminal domain of Sch9 is required for TORC1-dependent phosphorylation and downstream signal transduction. Journal of Molecular Biology, 433(24), p.167326.
https://www.sciencedirect.com/science/article/pii/S0022283621005635 (open-access)
Kurdyaeva, T. and Milias-Argeitis*, A., 2021. Uncertainty propagation for deterministic models of biochemical networks using moment equations and the extended Kalman filter. Journal of the Royal Society Interface, 18(181), p.20210331.
https://pure.rug.nl/ws/portalfiles/portal/178828270/Final_JRSI.pdf (accepted version)
Kurdyaeva, T. and Milias-Argeitis*, A., 2021. Moment-based uncertainty propagation for deterministic biochemical network models with rational reaction rates. Proceedings of the 2021 European Control Conference (ECC), pp.878-883.
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9654833
Chen, H., Mulder, L., Wijma, H.J., Wabeke, R., Losa, J.P.V.C., Rovetta, M., de Leeuw, T.C., Millias-Argeitis, A. and Heinemann, M., 2021. A photo-switchable yeast isocitrate dehydrogenase to control metabolic flux through the citric acid cycle. BioRxiv preprint.
https://doi.org/10.1101/2021.05.25.445643
2020
Saldida, J., Muntoni, A.P., de Martino, D., Hubmann, G., Niebel, B., Schmidt, A.M., Braunstein, A., Milias-Argeitis*, A. and Heinemann*, M., 2020. Unbiased metabolic flux inference through combined thermodynamic and 13C flux analysis. BioRxiv preprint.
https://doi.org/10.1101/2020.06.29.177063
2019
Litsios, A., Huberts, D.H., Terpstra, H.M., Guerra, P., Schmidt, A., Buczak, K., Papagiannakis, A., Rovetta, M., Hekelaar, J., Hubmann, G. and Exterkate, M., Milias-Argeitis*, A. and Heinemann*, M., 2019. Differential scaling between G1 protein production and cell size dynamics promotes commitment to the cell division cycle in budding yeast. Nature Cell Biology, 21(11), pp.1382-1392.
https://www.nature.com/articles/s41556-019-0413-3
Özsezen, S., Papagiannakis, A., Chen, H., Niebel, B., Milias-Argeitis, A. and Heinemann, M., 2019. Inference of the high-level interaction topology between the metabolic and cell-cycle oscillators from single-cell dynamics. Cell Systems, 9(4), pp.354-365.
https://www.sciencedirect.com/science/article/pii/S2405471219303114
2018
Kurdyaeva, T. and Milias-Argeitis*, A., 2018. Efficient global sensitivity analysis of biochemical networks using Gaussian process regression. Proceedings of the 2018 IEEE Conference on Decision and Control (CDC), pp.2673-2678.
https://ieeexplore.ieee.org/abstract/document/8618902
Bley Folly, B., Ortega, A.D., Hubmann, G., Bonsing‐Vedelaar, S., Wijma, H.J., van der Meulen, P., Milias‐Argeitis, A. and Heinemann, M., 2018. Assessment of the interaction between the flux‐signaling metabolite fructose‐1, 6‐bisphosphate and the bacterial transcription factors CggR and Cra. Molecular Microbiology, 109(3), pp.278-290.
https://onlinelibrary.wiley.com/doi/full/10.1111/mmi.14008
Garcia, H.G., Benzinger, D., Rullan, M., Milias-Argeitis, A., Khammash, M., Deutschbauer, A.M., Langdon, E.M. and Gladfelter, A.S., 2018. Principles of Systems Biology, No. 30. Cell Systems, 7(1), pp.1-2.
https://www.sciencedirect.com/science/article/pii/S2405471218302813
Rullan, M., Benzinger, D., Schmidt, G.W., Milias-Argeitis*, A. and Khammash*, M., 2018. An optogenetic platform for real-time, single-cell interrogation of stochastic transcriptional regulation. Molecular Cell, 70(4), pp.745-756.
https://www.sciencedirect.com/science/article/pii/S1097276518303083 (open access)
Zhang, Z., Milias-Argeitis, A. and Heinemann, M., 2018. Dynamic single-cell NAD(P)H measurement reveals oscillatory metabolism throughout the E. coli cell division cycle. Scientific Reports, 8(1), pp.1-10.
https://www.nature.com/articles/s41598-018-20550-7 (open access)
2017
Kuzmanovska, I., Milias-Argeitis, A., Mikelson, J., Zechner, C. and Khammash, M., 2017. Parameter inference for stochastic single-cell dynamics from lineage tree data. BMC Systems Biology, 11(1), pp.1-13.
https://link.springer.com/article/10.1186/s12918-017-0425-1 (open access)
Gupta, A., Milias-Argeitis, A. and Khammash, M., 2017. Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate. Journal of The Royal Society Interface, 14(132), p.20170311.
https://royalsocietypublishing.org/doi/full/10.1098/rsif.2017.0311
2016
Milias-Argeitis, A., Rullan, M., Aoki, S.K., Buchmann, P. and Khammash, M., 2016. Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nature Communications, 7(1), pp.1-11.
https://www.nature.com/articles/ncomms12546 (open access)
Milias-Argeitis, A., Oliveira, A.P., Gerosa, L., Falter, L., Sauer, U. and Lygeros, J., 2016. Elucidation of genetic interactions in the yeast GATA-factor network using Bayesian model selection. PLoS Computational Biology, 12(3), e1004784.
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004784 (open access)
2015
Milias-Argeitis, A. and Khammash, M., 2015. Adaptive model predictive control of an optogenetic system. Proceedings of the 54th IEEE Conference on Decision and Control (CDC), pp. 1265-1270.
https://ieeexplore.ieee.org/abstract/document/7402385
Ruess, J., Parise, F., Milias-Argeitis, A., Khammash, M. and Lygeros, J., 2015. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. Proceedings of the National Academy of Sciences, 112(26), pp.8148-8153.
https://www.pnas.org/content/112/26/8148.short
Milias-Argeitis, A., Engblom, S., Bauer, P. and Khammash, M., 2015. Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks. Journal of The Royal Society Interface, 12(113), p.20150831.
https://royalsocietypublishing.org/doi/full/10.1098/rsif.2015.0831
2014
Milias-Argeitis, A., Lygeros, J. and Khammash, M., 2014. Fast variance reduction for steady-state simulation and sensitivity analysis of stochastic chemical systems using shadow function estimators. The Journal of Chemical Physics, 141(2), p.024104.
https://aip.scitation.org/doi/full/10.1063/1.4886935
Milias-Argeitis, A. and Khammash, M., 2014. Optimization-based Lyapunov function construction for continuous-time Markov chains with affine transition rates. Proceedings of the 53rd IEEE Conference on Decision and Control, pp. 4617-4622.
https://ieeexplore.ieee.org/abstract/document/7040110
2013
Ruess, J., Milias-Argeitis, A. and Lygeros, J., 2013. Designing experiments to understand the variability in biochemical reaction networks. Journal of The Royal Society Interface, 10(88), p.20130588.
https://royalsocietypublishing.org/doi/full/10.1098/rsif.2013.0588
Esfahani, P.M., Milias-Argeitis, A. and Chatterjee, D., 2013. Analysis of controlled biological switches via stochastic motion planning. Proceedings of the 2013 European Control Conference (ECC), pp. 93-98.
https://ieeexplore.ieee.org/abstract/document/6669626
Milias-Argeitis, A. and Lygeros, J., 2013. Steady-state simulation of metastable stochastic chemical systems. The Journal of Chemical Physics, 138(18), p.184109.
https://aip.scitation.org/doi/full/10.1063/1.4804191
2011
Milias-Argeitis, A. and Lygeros, J., 2011. Efficient stochastic simulation of metastable Markov chains. Proceedings of the 50th IEEE Conference on Decision and Control (CDC), pp. 2239-2244.
https://ieeexplore.ieee.org/abstract/document/6160818
Milias-Argeitis, A., Summers, S., Stewart-Ornstein, J., Zuleta, I., Pincus, D., El-Samad, H., Khammash, M. and Lygeros, J., 2011. In silico feedback for in vivo regulation of a gene expression circuit. Nature Biotechnology, 29(12), pp.1114-1116.
https://www.nature.com/articles/nbt.2018
Ruess, J., Milias-Argeitis, A., Summers, S. and Lygeros, J., 2011. Moment estimation for chemically reacting systems by extended Kalman filtering. The Journal of chemical physics, 135(16), p.10B621.
https://aip.scitation.org/doi/full/10.1063/1.3654135
2010
Milias-Argeitis, A., Porreca, R., Summers, S. and Lygeros, J., 2010, December. Bayesian model selection for the yeast GATA-factor network: a comparison of computational approaches. Proceedings of the 49th IEEE Conference on Decision and Control (CDC), pp. 3379-3384.
https://ieeexplore.ieee.org/abstract/document/5717307
Ramponi, F., Chatterjee, D., Milias-Argeitis, A., Hokayem, P. and Lygeros, J., 2010. Attaining mean square boundedness of a marginally stable stochastic linear system with a bounded control input. IEEE Transactions on Automatic Control, 55(10), pp.2414-2418.
https://ieeexplore.ieee.org/abstract/document/5497091
2009
Cinquemani, E., Milias-Argeitis, A., Summers, S. and Lygeros, J., 2009. Local identification of piecewise deterministic models of genetic networks. In International Workshop on Hybrid Systems: Computation and Control (pp. 105-119). Springer, Berlin, Heidelberg.
https://link.springer.com/chapter/10.1007/978-3-642-00602-9_8
2008
Cinquemani, E., Milias-Argeitis, A., Summers, S. and Lygeros, J., 2008. Stochastic dynamics of genetic networks: modelling and parameter identification. Bioinformatics, 24(23), pp.2748-2754.
https://academic.oup.com/bioinformatics/article/24/23/2748/180874
Cinquemani, E., Milias-Argeitis, A. and Lygeros, J., 2008. Identification of genetic regulatory networks: A stochastic hybrid approach. IFAC Proceedings volume, 41(2), pp.301-306.
https://www.sciencedirect.com/science/article/pii/S1474667016389650
2007
Milias-Argeitis, A. and Bitsoris, G., 2007. Design of state estimators for linear discrete-time systems with constrained error variance. Proceedings of the 2007 European Control Conference (ECC), pp. 336-343.