arXiv subjects

Philippe Cluzel

Publications and source records attributed to Philippe Cluzel.

4 recordsLinked to original sources

arXiv2026

Efficiently classifying shocks in complex systems requires dormant reporters

Many natural and engineered systems are large complex networks of interacting components, and external perturbations drive them along different dynamical paths. Identifying which perturbation occurred matters for diagnosis, control, and prediction. Yet often times only a few components can be jointly monitored. Which components should be monitored? Experimental practice usually favors placing reporters at the most sensitive sites, where perturbations produce the largest effects. Using a simple dynamical model for complex systems with heterogeneous connectivity, we ask how sparse reporter panels should be chosen to classify shocks from partial trajectories when repeated trials only approximately reproduce an ideal initial condition. Once that reproduction is imperfect, sensitivity ranked panels fall far short of optimal, and the shortfall grows with the noise. We find that the best panels mix two kinds of reporters. A promiscuous reporter responds to most shocks, so it separates them mainly by degree, and degree fluctuates from trial to trial. A dormant reporter responds to only a few shocks but strongly, and its answers do not scatter as much between trials. As noise grows, the cost of losing a dormant reporter rises to meet the cost of losing a promiscuous one. Panels of either kind alone classify worse than the mixture, and no property of the members collected individually explains the ordering. Most of all, we find that only a minuscule number of reporters are needed on a panel to accurately identify which shock hit the system. We implement an efficient algorithm to assemble such a panel. Together these results provide a low cost practical design principle for monitoring large complex dynamical systems.

q-bio.QM↗

arXiv2021

Multiplicative noise underlies Taylor's law in protein concentration fluctuations in single cells

Protein concentration in a living cell fluctuates over time due to noise in growth and division processes. From extensive single-cell experiments by using E. coli strains with different promoter strength (over two orders of magnitude) and under different nutrient conditions, we found that the variance of protein concentration fluctuations follows a robust square power-law dependence on its mean, which belongs to a general phenomenon called Taylor's law. To understand the mechanistic origin of this observation, we use a minimal mechanistic model to describe the stochastic growth and division processes in a single cell with a feedback mechanism for regulating cell division. The model reproduces the observed Taylor's law. The predicted protein concentration distributions agree quantitatively with those measured in experiments for different nutrient conditions and a wide range of promoter strength. By using a mean-field approximation, we derived a single Langevin equation for protein concentration with multiplicative noise, which can be solved exactly to prove the square Taylor's law and to obtain an analytical solution for the protein concentration distribution function that agrees with experiments. Analysis of experiments by using our model showed that noise in production rates dominates over noise from cell division in contributing to protein concentration fluctuations. In general, a multiplicative noise in the underlying stochastic dynamics may be responsible for Taylor's law in other systems.

physics.bio-ph↗

arXiv2012

Environmental perturbations lift the degeneracy of the genetic code to regulate protein levels in bacteria

The genetic code underlying protein synthesis is a canonical example of a degenerate biological system. Degeneracies in physical and biological systems can be lifted by external perturbations thus allowing degenerate systems to exhibit a wide range of behaviors. Here we show that the degeneracy of the genetic code is lifted by environmental perturbations to regulate protein levels in living cells. By measuring protein synthesis rates from a synthetic reporter library in Escherichia coli, we find that environmental perturbations, such as reduction of cognate amino acid supply, lift the degeneracy of the genetic code by splitting codon families into a hierarchy of robust and sensitive synonymous codons. Rates of protein synthesis associated with robust codons are up to hundred-fold higher than those associated with sensitive codons under these conditions. We find that the observed hierarchy between synonymous codons is not determined by usual rules associated with tRNA abundance and codon usage. Rather, competition among tRNA isoacceptors for aminoacylation underlies the robustness of protein synthesis. Remarkably, the hierarchy established using the synthetic library also explains the measured robustness of synthesis for endogenous proteins in E. coli. We further found that the same hierarchy is reflected in the fitness cost of synonymous mutations in amino acid biosynthesis genes and in the transcriptional control of sigma factor genes. Our study reveals that the degeneracy of the genetic code can be lifted by environmental perturbations, and it suggests that organisms can exploit degeneracy lifting as a general strategy to adapt protein synthesis to their environment.

q-bio.GN↗

arXiv2007

Relationship between cellular response and behavioral variability in bacterial chemotaxis

Bacterial chemotaxis in Escherichia coli is a canonical system for the study of signal transduction. A remarkable feature of this system is the coexistence of precise adaptation in population with large fluctuating cellular behavior in single cells (Korobkova et al. 2004, Nature, 428, 574). Using a stochastic model, we found that the large behavioral variability experimentally observed in non-stimulated cells is a direct consequence of the architecture of this adaptive system. Reversible covalent modification cycles, in which methylation and demethylation reactions antagonistically regulate the activity of receptor-kinase complexes, operate outside the region of first-order kinetics. As a result, the receptor-kinase that governs cellular behavior exhibits a sigmoidal activation curve. This curve simultaneously amplifies the inherent stochastic fluctuations in the system and lengthens the relaxation time in response to stimulus. Because stochastic fluctuations cause large behavioral variability and the relaxation time governs the average duration of runs in response to small stimuli, cells with the greatest fluctuating behavior also display the largest chemotactic response. Finally, Large-scale simulations of digital bacteria suggest that the chemotaxis network is tuned to simultaneously optimize the random spread of cells in absence of nutrients and the cellular response to gradients of attractant.

q-bio.MN↗