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BIGR

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Biophysical Models of Bacterial Growth

Biology operates in a dynamic, changing environment, with fluctuations occurring over many time and length scales. Microorganisms are capable of duplicating themselves accurately over a short time in this noisy environment. This s... ver más

30/11/2028

WEIZMANN

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

WEIZMANN INSTITUTE OF SCIENCE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 1 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-11-16

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2023-COG: ERC CONSOLIDATOR GRANTS

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1 Participantes

WEIZMANN

1.71M€ | Lider

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Duración del proyecto: 60 meses Fecha Inicio: 2023-11-16
Fecha Fin: 2028-11-30

Líder del proyecto

WEIZMANN INSTITUTE OF SCIENCE

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto Biology operates in a dynamic, changing environment, with fluctuations occurring over many time and length scales. Microorganisms are capable of duplicating themselves accurately over a short time in this noisy environment. This self-replication, known as the cell cycle, must be tightly regulated in order for replication to be efficient. Key processes such as growth (both of volume and biomass), division and DNA replication must be coordinated. What biophysical cues are measured by the cell and what feedback is utilized to achieve this tight control is a fundamental, open and inherently interdisciplinary question. The goal of this proposal is to build integrated models which can account for the simultaneous regulation of multiple cellular traits, and account, quantitatively, for the coupling between the various cellular processes. We will consider coarse-grained models that operate both on long timescales – the coupling of DNA replication, gene expression and cell division – and short timescales, associated with water flow and ion transport across the membrane. Building on our expertise in the physics of stochastic processes, we will develop biophysical models that explain how microbes deal with fluctuations. We will develop new analysis tools that will enable us to learn from fluctuations, in particular through the powerful methodology of causal inference, which has not been previously applied in this context. The models will allow us to study the implications of variability on the population growth and fitness, and elucidate the design principles involved. Taken together, these models will take us toward comprehensive and predictive biophysical models of bacterial growth.

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