Remarkable recent experiments have demonstrated that single-celled organisms are able to respond not only to experienced changes in the past, but also to expected changes that have yet to occur evidently exploiting temporal correlations in the environment to predict the future.
But how reliably can a cell make such predictions, which are the mechanisms that implement them, and what are their fitness benefits?
In this project, you will address these questions experimentally, using single-cell microscopy to precisely quantify (in bits) the amount of predictive information cells extract from past experiences.
The primary experimental vehicle will be a single-cell FRET method developed in our group (Keegstra et al., 2017 eLife; Kamino et al.
2020, Science Advances). Your measurements will quantify information transmission and prediction in the E. coli chemotaxis system a cell signaling network for which sufficient quantitative knowledge exists to address further questions about mechanistic origins and functional benefits of prediction.
You will collaborate closely with the theory group of Pieter Rein ten Wolde (AMOLF), using concepts from information theory and stochastic thermodynamics to design and interpret experiments.
For further information on the project, contact Tom Shimizu (shimizu amolf.nl).
About the group
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