Summary
The physical organization of bacterial chromosomes is inherently variable, with large conformational fluctuations both from cell to cell and over time. Yet, chromosomes must also be structured to facilitate processes such as transcription, replication, and segregation. A physical description of this dynamic statistical folding of bacterial chromosomes remains largely elusive.
Hi-C experiments probe chromosome organization by measuring average contact frequencies of chromosomal loci pairs. Despite the rapidly expanding database of high-resolution Hi-C data for many bacterial species and conditions, these data are still mainly interpreted on a case-by-case basis and with qualitative or heuristic methods.
My goal is to develop a principled unifying approach to infer and analyze the dynamic organization of chromosomes from bacterial Hi-C data. This data-driven approach aims to unravel the dynamic statistical folding of chromosomes – and its impact on functional processes – in growing and replicating bacteria.
We will infer a bacterial chromosome model from state-of-the-art data using learning methods at the intersection of information theory and statistical mechanics. By combining data-driven with mechanistic modelling approaches, we aim to:
•Decode information contained in Hi-C data by learning both 3D steady-state and 4D dynamic models for the statistical organization of chromosomes.
•Provide a unifying statistical mechanics analysis of the dynamic statistical folding of chromosomes across bacterial species under both steady-state and replicating conditions.
•Develop theoretical methods using pairwise and multi-contact statistics to study the topology of statistical chromosome folding.
My research will advance the field by providing a conceptual understanding of the physical and mechanistic principles that underlie chromosome organization in developing bacteria. This work could shed new light on vital functional processes such as chromosome segregation.
Hi-C experiments probe chromosome organization by measuring average contact frequencies of chromosomal loci pairs. Despite the rapidly expanding database of high-resolution Hi-C data for many bacterial species and conditions, these data are still mainly interpreted on a case-by-case basis and with qualitative or heuristic methods.
My goal is to develop a principled unifying approach to infer and analyze the dynamic organization of chromosomes from bacterial Hi-C data. This data-driven approach aims to unravel the dynamic statistical folding of chromosomes – and its impact on functional processes – in growing and replicating bacteria.
We will infer a bacterial chromosome model from state-of-the-art data using learning methods at the intersection of information theory and statistical mechanics. By combining data-driven with mechanistic modelling approaches, we aim to:
•Decode information contained in Hi-C data by learning both 3D steady-state and 4D dynamic models for the statistical organization of chromosomes.
•Provide a unifying statistical mechanics analysis of the dynamic statistical folding of chromosomes across bacterial species under both steady-state and replicating conditions.
•Develop theoretical methods using pairwise and multi-contact statistics to study the topology of statistical chromosome folding.
My research will advance the field by providing a conceptual understanding of the physical and mechanistic principles that underlie chromosome organization in developing bacteria. This work could shed new light on vital functional processes such as chromosome segregation.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101122863 |
| Start date: | 01-05-2024 |
| End date: | 30-04-2029 |
| Total budget - Public funding: | 2 000 000,00 Euro - 2 000 000,00 Euro |
Cordis data
Original description
The physical organization of bacterial chromosomes is inherently variable, with large conformational fluctuations both from cell to cell and over time. Yet, chromosomes must also be structured to facilitate processes such as transcription, replication, and segregation. A physical description of this dynamic statistical folding of bacterial chromosomes remains largely elusive.Hi-C experiments probe chromosome organization by measuring average contact frequencies of chromosomal loci pairs. Despite the rapidly expanding database of high-resolution Hi-C data for many bacterial species and conditions, these data are still mainly interpreted on a case-by-case basis and with qualitative or heuristic methods.
My goal is to develop a principled unifying approach to infer and analyze the dynamic organization of chromosomes from bacterial Hi-C data. This data-driven approach aims to unravel the dynamic statistical folding of chromosomes and its impact on functional processes in growing and replicating bacteria.
We will infer a bacterial chromosome model from state-of-the-art data using learning methods at the intersection of information theory and statistical mechanics. By combining data-driven with mechanistic modelling approaches, we aim to:
Decode information contained in Hi-C data by learning both 3D steady-state and 4D dynamic models for the statistical organization of chromosomes.
Provide a unifying statistical mechanics analysis of the dynamic statistical folding of chromosomes across bacterial species under both steady-state and replicating conditions.
Develop theoretical methods using pairwise and multi-contact statistics to study the topology of statistical chromosome folding.
My research will advance the field by providing a conceptual understanding of the physical and mechanistic principles that underlie chromosome organization in developing bacteria. This work could shed new light on vital functional processes such as chromosome segregation.
Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
12-03-2024
Geographical location(s)
