Periodic Reporting for period 1 - StatCell (The Stressed Cell as a Physical Aging Problem)
Periodo di rendicontazione: 2022-07-01 al 2024-12-31
Statistical physics successfully accounts for phenomena involving a large number of components using a probabilistic approach with predictions for collective properties of the system. While biological cells contain a very large number of interacting components, (proteins, RNA molecules, metabolites, etc.), the cellular network is understood as a particular, highly specific, choice of interactions shaped by evolution, and therefore not amenable to a statistical physics description. Our approach is that when a cell encounters an acute, but non-lethal, stress, its perturbed state can be modelled as random network dynamics, rather than as a regulated response. Strong perturbations may therefore reveal the dynamics of the underlying network that are amenable to a statistical physics description. Based on the striking similarity between our data on stressed bacteria and physical aging in disordered systems, our goal is to develop an experimental and theoretical framework for the statistical physics description of cells exposed to strong perturbations. We will critically probe the predictions of the statistical model using a multidisciplinary approach combining three frontline methodologies: (1) dynamics of single bacteria under acute stress in microfluidic devices and single cell transcriptomics; (2) theoretical framework and simulations for cellular networks under acute stress; and (3) new biophysical measurements of the transition from the regulated to the disrupted cellular network. This approach should provide a paradigm shift in the analysis of cells under stress, differentiating between conditions described by the regulation of gene networks from those that can be quantitatively predicted by a statistical physics framework. The new knowledge should lead to innovative ways of controlling the cellular network under strong perturbations, with implications ranging from new methodologies for synthetic biology to new avenues for treating bacterial infections and cancer.
The work, towards the main goal of using statistical physics to predict the behavior of cells under stress, is divided into three main parts: (1) Experiments aiming at the quantitative characterization of single bacteria under acute versus gradual stress (2) Theoretical approach to the inner dynamics of each bacterium and their distribution over the bacterial population and (3) characterization of the transition from acute response to regulated response using tools for the characterization of phase-transitions. We have now shown that the response of cell to acute stress is a "disrupted state" that differs strongly from the typical regulated response to stress. We have characterized the difference between "disrupted" growth arrest bacteria and "regulated" growth-arrested bacteria at several different levels: we have shown in microfluidic devices that single disrupted bacteria show a high variability of gene expression when compared to regulated growth-arrested bacteria, that their metabolism is much higher and that they may be eradicated using treatments to which regulated bacteria are tolerant.
We have proposed a statistical model of a large random network with high connectivity in order to describe the behavior of cells after exposure to acute stress which leads to the disrupted growth arrest. The main achievements so far include:
- the characterization of the differences between the growth arrest state of disrupted bacteria and of regulated bacteria using bulk RNAseq
- the identification of potential weaknesses in disrupted bacteria that may be targeted by specifically designed treatments
- a theoretical framework for the identification of chaotic signature in disrupted bacteria
We have proposed a statistical model of a large random network with high connectivity in order to describe the behavior of cells after exposure to acute stress which leads to the disrupted growth arrest. The main achievements so far include:
- the characterization of the differences between the growth arrest state of disrupted bacteria and of regulated bacteria using bulk RNAseq
- the identification of potential weaknesses in disrupted bacteria that may be targeted by specifically designed treatments
- a theoretical framework for the identification of chaotic signature in disrupted bacteria
The potential impact of our results so far is in the identification of compounds that would specifically target disrupted bacteria. This would provide a rationale for the design of more effective antibiotic treatments, an avenue that we will pursue in collaboration with medical doctors.