Eszter Lakatos

kep3I am a PhD student supervised by Professor Michael Stumpf. I obtained a BSc in Molecular Bionics (in 2012) followed by an MSc in Infobionics (2013), both from the Faculty of Information Technology and Bionics, PPCU, Hungary.
During my previous projects I have tackled different aspects of the intramuscular actin-myosin interaction (through simulations of the cytokinetic ring and AFM studies on vertebrate thick filaments) and worked on a recent model of the blood glucose control system.

My PhD focuses on cellular decision making processes with respect to noise in signalling pathways. In particular, my aim is to investigate various aspects of how noise can alter, challenge or help major decisions cells has to take during their lifetime. To do so I start from simplified biochemical models of crucial network components, conduct in silico experiments and study the stochasticity of the system.
I use a variety of stochastic modelling tools to obtain the population-level dynamics of the models, and combine these methods with frameworks including inference, distribution reconstruction and control design. I apply my techniques on the p53 tumour suppressor protein, and Nanog and Oct4-Sox2 contributing to stem cell dynamics.

Outside research I spend most of my time (i) all round town on my beloved red bike, (ii) in the kitchen experimenting with new dishes and (iii) singing with the London International Gospel Choir.

Publications and Selected Conferences

Protein degradation rate is the dominant mechanism accounting for the differences in protein abundance of basal p53 in a human breast and colorectal cancer cell line.
Lakatos* E, Salehi-Reyhani* A, Barclay* M, Stumpf MPH and Klug DR.
PLOS One. 2017 May 10; 12(5). doi: 10.1371/journal.pone.0177336

Control mechanisms for stochastic biochemical systems via computation of reachable sets.
Lakatos E, Stumpf MP.
bioRxiv 079723. doi: https://doi.org/10.1101/079723

MEANS: python package for Moment Expansion Approximation, iNference and Simulation.
Fan* S, Geissmann* Q, Lakatos* E, Lukauskas* S, Ale A, Babtie AC, Kirk PD, Stumpf MP.
Bioinformatics. 2016 Sep 15;32(18):2863-5. doi: 10.1093/bioinformatics/btw229.

Multivariate moment closure techniques for stochastic kinetic models.
Lakatos E, Ale A, Kirk PD, Stumpf MP.
J Chem Phys. 2015 Sep 7;143(9):094107. doi: 10.1063/1.4929837.

E. Lakatos and M. P. H. Stumpf: Investigating cellular decision making through potential landscapes; International Conference on Systems Biology, Barcelona, Spain, September 16-20, 2016
E. Lakatos*, A. Babtie*, P. D. W. Kirk and M. P. H. Stumpf: Using Topological Sensitivity and Reachability Analysis to explore uncertainty in large model spaces; SIAM Conference on Uncertainty Quantification, Lausanne, Switzerland, April 5-8, 2016
E. Lakatos, M. Barclay, A. Salehi-Reyhani and M. P. H. Stumpf: Investigating a tumour protein with Approximate Bayesian Computation; Annual Day of Applied Mathematics and Mathematical Physics, London, United Kingdom, December 16, 2015
E. Lakatos and M. P. H. Stumpf: Reachable state set computation of stochastic biological systems with controlled input and uncertainty; 25th Annual MASAMB (Mathematical and Statistical Aspects of Molecular Biology) Workshop, Helsinki, Finland, April 16-17, 2015
E. Lakatos, P. Kirk and M. P. H. Stumpf: Multivariate moment closure techniques for stochastic kinetic models; 11th International Workshop on Computation Systems Biology, Lisbon, Portugal, May 15-16, 2014
E. Lakatos, D. Meszena and G. Szederkenyi: Identifiablity analysis and improved parameter estimation of a human blood glucose control system model; Computational Methods in Systems Biology, 11th International Conference, Klosterneuburg, Austria, September 22-24, 2013. Proceedings
B. Decker, E. Lakatos and M. S. Z. Kellermayer: Structure of synthetic vertebrate myosin thick filaments explored with high-resolution AFM; Biophysical Society, 57th Annual Meeting, Philadelphia, Pennsylvania, US, February 2-6, 2013