We focus on developing a generic theory of computations and learning on the level of biochemical networks in single cells and single-cell organisms. We investigate how single cells employ working memory to integrate multiple time-varying signals as a means to generate stable identity, while simultaneously balancing plasticity in cellular responses. Formalizing these principles through computations with metastable states, we also explore whether single cells can learn. Using quantitative imaging, we experimentally validate the proposed conceptual basis how cells process non-stationary signals and learn to generalize their responses to a changing environment.
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- Bifurcation analysis
- numerical simulations of systems of ordinary and partial differential equations
- nonlinear time series analysis
- advances fluorescence imaging technicques
- genetic manipulations.
5 selected papers
- Nandan, A., Das, A., Lott, R., Koseska, A. (2021). Cells use molecular working memory to navigate in changing chemoattractant fields bioRxiv, 2021.11.11.468222.
- Koseska, A., and Bastiaens, P.I.H. (2020). Processing temporal growth factor patterns by an epidermal growth factor receptor network dynamically established in space. Annu Rev Cell Dev Biol 36, 359-383.
- Stanoev, A., Nandan, A.P., and Koseska, A. (2020). Organization at criticality enables processing of time-varying signals by receptor networks. Mol Syst Biol 16, e8870.
- Stanoev, A., Mhamane, A., Schuermann, K.C., Grecco, H.E., Stallaert, W., Baumdick, M., Brüggemann, Y., Joshi, M.S., Roda-Navarro, P., Fengler, S., Stockert, R., Roßmannek, L., Luig, J., Koseska, A., and Bastiaens, P.I.H. (2018). Interdependence between EGFR and phosphatases spatially established by vesicular dynamics generates a growth factor sensing and responding network. Cell Syst 7, 295-309.
- Koseska, A. and Bastiaens, P.I.H. (2017). Cell signaling as a cognitive process. EMBO J 36, 568-582.