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Sasha Eremina

Research Interests

Algorithms are most commonly associated with mathematics and computer science, but they also underlie biological systems. Modern biologists now view living organisms as systems of inherent algorithms, but they are facing the conundrum of how identical biological programmes are sometimes executed to produce different outcomes, and what are the functions of such variability. My research investigates heterogeneity in circadian rhythms, or biological clocks, by examining the dynamics of circadian gene expression in cyanobacteria. I am interested in investigating how the clock modulates stress response and how, in turn, it is affected by dynamic changes in the environment. Combining experiments with image analysis and modelling, I aim to develop a predictive framework of circadian rhythms under stress and further relate it to the long-term adaptation to fluctuating environments. This research further intends to establish novel quantitative tools for studying the dynamics of circadian oscillators and improve their use in synthetic biology.

 

Previous work

Prior to joining SLCU for my graduate studies, I obtained my Bachelor’s degree from the University of Edinburgh. I developed my interest in systems biology upon doing a year abroad at the University of California, San Diego, and then worked with Prof. Peter Swain at SynthSys, Edinburgh, to study information processing in yeast cell cycle during my undergraduate project.

 

Key Publications

Kaznadzey A., Shelyakin P., Belousova E., Eremina A., Shvyreva U., Bykova D., Emelianenko V., Korosteleva A., Tutukina M., Gelfand M. (2018). The genes of the sulphoquinovose catabolism in Escherichia coli are also associated with a previously unknown pathway of lactose degradation. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-21534-3

 

Usmanova D.R., Bogatyreva N.S., Ariño Bernad J., Eremina A.A., Gorshkova A.A., Kanevskiy G.M., Lonishin L.R., Meister A.V., Yakupova A.G., Kondrashov F.A., Ivankov D.M. (2018). Self-consistency test reveals systematic bias in programs for prediction change. Bioinformatics, bty340. https://doi.org/10.1093/bioinformatics/bty340

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