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It is standard procedure to characterize the stochastic dynamics of physical systems in thermodynamic equilibrium by measuring spontaneous fluctuations and responses to small external perturbations. Because these two distinct measurements [...], The chemotaxis signalling network in Escherichia coli that controls the locomotion of bacteria is a classic model system for signal transduction (1,2). This pathway modulates the behaviour of flagellar motors to propel bacteria towards sources of chemical attractants. Although this system relaxes to a steady state in response to environmental changes, the signalling events within the chemotaxis network are noisy and cause large temporal variations of the motor behaviour even in the absence of stimulus (3). That the same signalling network governs both behavioural variability and cellular response raises the question of whether these two traits are independent. Here, we experimentally establish a fluctuation-response relationship in the chemotaxis system of living bacteria. Using this relationship, we demonstrate the possibility of inferring the cellular response from the behavioural variability measured before stimulus. In monitoring the pre- and post-stimulus switching behaviour of individual bacterial motors, we found that variability scales linearly with the response time for different functioning states of the cell. This study highlights that the fundamental relationship between fluctuation and response is not constrained to physical systems at thermodynamic equilibrium (4) but is extensible to living cells (5). Such a relationship not only implies that behavioural variability and cellular response can be coupled traits, but it also provides a general framework within which we can examine how the selection of a network design shapes this interdependence.

We use the lac operon in Escherichia coli as a prototype system to illustrate the current state, applicability, and limitations of modeling the dynamics of cellular networks. We integrate three different levels of description (molecular, cellular, and that of cell population) into a single model, which seems to capture many experimental aspects of the system.

The chemotaxis signalling network in Escherichia coli that controls the locomotion of bacteria is a classic model system for signal transduction. This pathway modulates the behaviour of flagellar motors to propel bacteria towards sources of chemical attractants. Although this system relaxes to a steady state in response to environmental changes, the signalling events within the chemotaxis network are noisy and cause large temporal variations of the motor behaviour even in the absence of stimulus. That the same signalling network governs both behavioural variability and cellular response raises the question of whether these two traits are independent. Here, we experimentally establish a fluctuation-response relationship in the chemotaxis system of living bacteria. Using this relationship, we demonstrate the possibility of inferring the cellular response from the behavioural variability measured before stimulus. In monitoring the pre- and post-stimulus switching behaviour of individual bacterial motors, we found that variability scales linearly with the response time for different functioning states of the cell. This study highlights that the fundamental relationship between fluctuation and response is not constrained to physical systems at thermodynamic equilibrium but is extensible to living cells. Such a relationship not only implies that behavioural variability and cellular response can be coupled traits, but it also provides a general framework within which we can examine how the selection of a network design shapes this interdependence. [ABSTRACT FROM AUTHOR]

Fluorescence correlation spectroscopy (FCS) has permitted the characterization of high concentrations of noncoding RNAs in a single living bacterium. Here, we extend the use of FCS to low concentrations of coding RNAs in single living cells. We genetically fuse a red fluorescent protein (RFP) gene and two binding sites for an RNA-binding protein, whose translated product is the RFP protein alone. Using this construct, we determine in single cells both the absolute [mRNA] concentration and the associated [RFP] expressed from an inducible plasmid. We find that the FCS method allows us to reliably monitor in real-time [mRNA] down to »40nM (i.e. approximately two transcripts per volume of detection). To validate these measurements, we show that [mRNA] is proportional to the associated expression of the RFP protein. This FCS-based technique establishes a framework for minimally invasive measurements of mRNA concentration in individual living bacteria., Molecular and Cellular Biology, Physics

A major obstacle to predictive understanding of evolution stems from the complexity of biological systems, which prevents detailed characterization of key evolutionary properties. Here, we highlight some of the major sources of complexity that arise when relating molecular mechanisms to their evolutionary consequences and ask whether accounting for every mechanistic detail is important to accurately predict evolutionary outcomes. To do this, we developed a mechanistic model of a bacterial promoter regulated by two proteins, allowing us to connect any promoter genotype to six phenotypes that capture the dynamics of gene expression following an environmental switch. Accounting for the mechanisms that govern how this system works enabled us to provide an in-depth picture of how regulated bacterial promoters might evolve. More importantly, we used the model to explore which factors that contribute to the complexity of this system are essential for understanding its evolution, and which can be simplified without information loss. We found that several key evolutionary properties - the distribution of phenotypic and fitness effects of mutations, the evolutionary trajectories during selection for regulation - can be accurately captured without accounting for all, or even most, parameters of the system. Our findings point to the need for a mechanistic approach to studying evolution, as it enables tackling biological complexity and in doing so improves the ability to predict evolutionary outcomes., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)