Biophysical Models of Bacterial Growth

Informacje na temat projektu

BIGR

Identyfikator umowy o grant: 101125981

DOI

10.3030/101125981

Data podpisania przez KE 16 Listopada 2023

Data rozpoczęcia 1 Grudnia 2023

Data zakończenia 30 Listopada 2028

Finansowanie w ramach

• European Research Council (ERC)

Koszt całkowity

€ 1 708 613,00

Wkład UE

€ 1 708 613,00

Koordynowany przez

WEIZMANN INSTITUTE OF SCIENCE
Israel

Periodic Reporting for period 1 - BIGR (Biophysical Models of Bacterial Growth)

Okres sprawozdawczy: 2023-12-01 do 2026-05-31

Podsumowanie kontekstu i ogólnych celów projektu

Living cells must grow and divide with remarkable accuracy despite operating in noisy and constantly changing environments. For bacteria, this challenge is particularly acute: in a single cell cycle, they must coordinate growth, gene expression, DNA replication, and division while maintaining stable internal conditions such as protein concentration and pressure. How cells achieve this level of coordination — and which physical signals they use to regulate it — remains a fundamental open problem at the interface of physics and biology.
This project set out to develop a quantitative, biophysical understanding of bacterial growth and division by focusing on the physical constraints and feedback mechanisms that govern cellular self-replication. Rather than studying individual processes in isolation, the project aimed to build integrated models that link cell growth, gene expression, DNA replication, and division across multiple time and length scales.
By combining theory, data analysis, and close interaction with experimental observations, the project seeks to uncover general design principles underlying cellular regulation. These insights are expected to advance our understanding of microbial physiology, inform future experimental work, and contribute to broader efforts in systems biology, biophysics, and quantitative biology.

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

The project developed and applied new theoretical frameworks to analyze how bacterial cells regulate their size, growth rate, and division timing. A major focus was the use of single-cell data to identify causal relationships between key events in the cell cycle, such as DNA replication initiation, chromosome segregation, and cell division. Novel analytical tools based on fluctuations, correlations and causal inference were introduced to distinguish between competing models of cell-cycle control without relying on invasive perturbations.
The recent work shed new light on which processes limit the rate of cell division in Escherichia coli (in collaboration with the Mannik lab) and demonstrated how different growth conditions alter the coupling between DNA replication and division. It also addressed a fundamental methodological question in cell-cycle analysis by establishing when different measures of cell size (such as length and volume) can or cannot be used interchangeably.

Beyond cell division, the project advanced predictive models of gene expression in growing cells, explaining how transcription and translation scale with cell growth and how cells maintain stable protein concentrations despite continuous growth. These models were extended and reviewed in a comprehensive framework (together with Prof. Ido Golding) that serves as a reference for researchers entering the field.

The project also broadened its scope beyond E. coli (notably, the important and understudied pathogen M. tuberculosis, in collaboration with the Aldridge lab) by analyzing growth and division, revealing unexpected diversity and heterogeneity in growth modes.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

The aforementioned results delivered a unified, physics-based perspective on bacterial growth that goes beyond descriptive models, providing mechanistic explanations grounded in measurable physical processes. By integrating novel methodology for studying correlations and fluctuations (such as causal inference) with biophysical modeling, the work moved the field past long-standing debates about cell-cycle control (e.g. in the context of cell length vs. volume, and also regarding the methodology of elucidating the mode of growth at the single-cell level) and established new standards for interpreting single-cell data.

The results have broad potential impact. They provide a conceptual foundation for interpreting single-cell experiments, inform the design of future experiments, and open new directions for studying robustness and variability in living systems. The theoretical frameworks developed here are general and can be applied to other organisms and contexts, e.g. in the context of gene expression as well as microbial evolution, relevant to understand phenomena such as antibiotic resistance.

Further uptake of these results will benefit from continued collaboration between theorists and experimentalists, as well as from the application of these models to new datasets and biological systems. Overall, the project advances fundamental knowledge of how living cells grow and divide, contributing key insights toward predictive, quantitative models of life at the cellular level.