Summary
Gene expression is an inherently stochastic process. Random expression bursts cause tremendous cell-to-cell variations in mRNA and protein levels. The consequences are beneficial in some instances, e.g., in cell differentiation, and harmful in others, e.g., in bacterial drug tolerance. A key interest in biology is therefore to decipher the kinetics that characterise this noise. What is the distribution of transcription rates in a cell population? Are gene expression dynamics heritable? Do gene networks communicate via the ‘Morse code’ of expression burst? Detailed answers to these questions are pending due to insufficient methods to temporally resolve gene expression noise at single-molecule resolution. A ‘transparent cell’ is needed for which transcription and translation kinetics is accessible for many genes in parallel. DYNOME is my answer to this challenge. DYNOME combines (i) a super-resolution detect-and-bleach strategy, (ii) multi-colour barcoding to monitor up to six genes in parallel, (iii) a lineage tracking tool, and (iv), lab-on-a-chip bioreactors to steer growth conditions. Using this innovative bio-dynamics platform, I will monitor gene expression dynamics at the single-molecule level for many genes in single cells at the same time over many generations. My targets for DYNOME are stochastic decision-making events in bacteria and in eukaryotic cells that include (i) a stochastic phenotype switch (B. subtilis), (ii) the development of non-genetic drug resistance (E. coli), and (iii) cell cycle control (S. cerevisiae). The results will reach far beyond these organisms. Decrypting cell-individuality with kinetic models will be a breakthrough both in basic and in applied sciences with impacts on the development of drugs against bacterial invasions, the design of new and useful functionalities in cells, and on our understanding of how biological variability arises from the laws of statistical physics.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/864578 |
| Start date: | 01-02-2020 |
| End date: | 31-07-2025 |
| Total budget - Public funding: | 2 368 531,00 Euro - 2 368 531,00 Euro |
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Original description
Gene expression is an inherently stochastic process. Random expression bursts cause tremendous cell-to-cell variations in mRNA and protein levels. The consequences are beneficial in some instances, e.g., in cell differentiation, and harmful in others, e.g., in bacterial drug tolerance. A key interest in biology is therefore to decipher the kinetics that characterise this noise. What is the distribution of transcription rates in a cell population? Are gene expression dynamics heritable? Do gene networks communicate via the ‘Morse code’ of expression burst? Detailed answers to these questions are pending due to insufficient methods to temporally resolve gene expression noise at single-molecule resolution. A ‘transparent cell’ is needed for which transcription and translation kinetics is accessible for many genes in parallel. DYNOME is my answer to this challenge. DYNOME combines (i) a super-resolution detect-and-bleach strategy, (ii) multi-colour barcoding to monitor up to six genes in parallel, (iii) a lineage tracking tool, and (iv), lab-on-a-chip bioreactors to steer growth conditions. Using this innovative bio-dynamics platform, I will monitor gene expression dynamics at the single-molecule level for many genes in single cells at the same time over many generations. My targets for DYNOME are stochastic decision-making events in bacteria and in eukaryotic cells that include (i) a stochastic phenotype switch (B. subtilis), (ii) the development of non-genetic drug resistance (E. coli), and (iii) cell cycle control (S. cerevisiae). The results will reach far beyond these organisms. Decrypting cell-individuality with kinetic models will be a breakthrough both in basic and in applied sciences with impacts on the development of drugs against bacterial invasions, the design of new and useful functionalities in cells, and on our understanding of how biological variability arises from the laws of statistical physics.Status
SIGNEDCall topic
ERC-2019-COGUpdate Date
27-04-2024
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