Biology subjects

Pedraza, J. M.

Publications and source records attributed to Pedraza, J. M..

10 recordsLinked to original sources

bioRxiv2023

Noise Propagation in Transcriptional Genetic Cascades

Cellular processes are inherently stochastic, leading to fluctuations in protein concentration quantifiable as noise in gene expression. Precise description of noise propagation in gene networks is essential for designing noise-tolerant gene circuits and understanding signal reliability in biological networks, but current models for noise propagation are primarily system-specific or limited to short gene cascades. Here we present an analytical expression for noise propagation in gene expression that works for long cascades and incorporates global noise. Since modelling all aspect of noise can be prohibitively complicated, in many situations only intrinsic or global noise is considered, but general criteria for when each type is dominant are still lacking. As an example of the possible use of our analytical expression, we examine the role different aspects of the network have on the balance between intrinsic and global noises and their propagation. We show that the type of cascade, cascade length, sensitivity, and basal transcription rates have an effect beyond simple protein abundance. This has practical implications for designing synthetic gene networks in prokaryotes and improving our knowledge on noise propagation in gene networks, and could shed light on how evolution may shape circuit sizes to balance signal fidelity and metabolic cost.

systems biology↗

bioRxiv2023

Mechanisms of Cell Size Regulation in Slow-Growing Escherichia coli Cells: Discriminating Models Beyond the Adder

Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: precursor protein degradation, nonlinear accumulation rate, and a threshold size termed the commitment size. These models fit mean trends but predict different distributions given the birth size. To validate these models, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. the degradation model could explain the division strategy for media where cells are larger, while the commitment size model could account for smaller cells. The power-law model, finally, better fits the data at intermediate regimes.

systems biology↗

bioRxiv2022

Modeling cell size regulation under complex and dynamic environments

In nature, cells face changes in environmental conditions that can modify their growth rate. In these dynamic environments, recent experiments found changes in cell size regulation. Currently, there are few clues about the origin of these cell size changes. In this work, we model cell division as a stochastic process that occurs at a rate proportional to the size. We propose that this rate is zero if the cell is smaller than a minimum size. We show how this model predicts some of the properties found in cell size regulation. For example, among our predictions, we found that the mean cell size is an exponential function of the growth rate under steady conditions. We predict that cells become smaller and the way the division strategy changes during dynamic nutrient depletion. Finally, we use the model to predict cell regulation in an arbitrary complex dynamic environment.

systems biology↗

bioRxiv2022

The role of division stochasticity on the robustness of bacterial size dynamics

Variables of bacterial division such as size at birth, growth rate, division time, and the position of the septal ring, all vary from cell to cell. Currently, it is unknown how these random fluctuations can combine to produce a robust mechanism of homeostasis. To address this question, we studied the dynamics of the cell division process from both experimental and theoretical perspectives. Our model predicts robustness in division times as sustained oscillations in metrics of the cell size distribution, such as the mean, variability, and the cell size autocorrelation function. These oscillations do not get damped, even considering stochasticity in division timing and the cell size at the beginning of the experiment. Damping appears just after inducing stochasticity in either the septum position or the growth rate. We compare the predictions of the full model with the size dynamics of E. coli bacteria growing in minimal media using either glucose or glycerol as carbon sources. We observe that growth in poorer media increases the noise in both partitioning position and growth rate. This additional noise results in oscillations with more damping. Although intracellular noise is known as a source of phenotypic variation, our results show that it can play a similar but subtler role in maintaining population-level homeostasis by causing rapid desynchronization of cell cycles..

systems biology↗

bioRxiv2020

PyEcoLib: a python library for simulating E. coli stochastic size dynamics

Recent studies describe bacterial division as a jump process triggered when it reaches a fixed number of stochastic discrete events at a rate depending on the cell-size. This theoretical approach enabled the computation of stochastic cell-size transient dynamics with arbitrary precision, with the possibility of being coupled to other continuous processes as gene expression. Here we synthesize most of this theory in the tool PyEcoLib, a python-based library to estimate bacterial cell size stochastic dynamics including continuous growth and division events. In this library, we include examples predicting statistical properties seen in experiments.

systems biology↗

bioRxiv2020

Continuous Rate Modelling of bacterial stochastic size dynamics

Bacterial division is an inherently stochastic process. However, theoretical tools to simulate and study the stochastic transient dynamics of cell-size are scarce. Here, we present a general theoretical approach based on the Chapman-Kolmogorov formalism to describe these stochastic dynamics including continuous growth and division events as jump processes. Using this approach, we analyze the effect of different sources of noise on the dynamics of the size distribution. Oscillations in the distribution central moments were found as consequence of the discrete translation invariance of the system with period of one doubling time, these oscillations are found in both the central moments of the size distribution and the auto-correlation function and do not disappear including stochasticity on division times or size heterogeneity on the population but only after include noise in either growth rate or septum position.

systems biology↗

bioRxiv2020

Division stochasticity can be transmitted to protein expression through chromosome replication

Stochastic fluctuations (noise) are a fundamental characteristic of protein production. Some sources of this stochasticity are still under debate. In this work, we explore how these fluctuations can originate from the stochasticity on division events. To do that, we consider the classical gene expression model with chromosome replication following the known Helmstetter & Cooper model. This model predicts intervals of the cell cycle where bacteria can have more than one copy of a particular gene. Considering the transcription rate as proportional to the number of chromosomes and division based on a continuous rate model, we explore how stochasticity in division or equivalently in cell size, could be transmitted to gene expression. Our simulations suggest that division can be an important source of such fluctuations only if chromosomes are replicating, otherwise, this noise is not well transmitted. This effect happens even if replication is deterministic. This work can be helpful for understanding cell cycle dynamics and their interplay with phenotypic variability.

systems biology↗

bioRxiv2020

An integrative approach points to membrane composition as a key factor in E. coli persistence

Many diverse bacteria can enter non- or slow-growing states where they are transiently tolerant to antibiotics. Despite its medical importance, the genetic mechanisms underlying this persistence remain largely unknown, especially for spontaneous (type II) persistence that arise during exponential growth in rich medium. To address this challenge, here we combine genomic, transcriptomic and lipidomic analysis to identify the persistence mechanisms. We first analyzed the genome of the high-persistence mutant Escherichia coli DS1 (hipQ) to identify candidate genes for the high persistence phenotype. We then compared the gene expression profile of spontaneous persisters to normally growing cells with RNAseq and find that the activation of stress response mechanisms is likely not very important in the entrance into hipQ-driven spontaneous persistence. Transcriptomic results also suggest that modifications in the cell membrane play an important role, as further corroborated by lipidomic profiles showing a higher level of unsaturated fatty acids in spontaneous persisters compared to induced persisters or normally growing cells. Taken together, our results indicate that changing membrane composition is a key process in persistence, and further our understanding of spontaneous persister cells from the DS1 (hipQ) context.

microbiology↗

bioRxiv2020

Noise signature in added size suggests bacteria target a commitment size to enable division

Recent experiments suggested that sizer-like division strategy, a deviation from the adder paradigm might be produced by additional degradation events of cell division machinery molecules. We revisited single cell size data from a recently microfluidics setup using the above model. We observed that such additional degradation process, although reproduces size observations in the mean sense, it is unable to capture cell size fluctuations. We further extended recently proposed power law models to include commitment size. Our proposal is in agreement of both mean and fluctuation profiles seen in experiments. Our approach suggests further uses of noise profiles on dissecting cell size regulatory mechanisms. SIGNIFICANCEWe contrast cell division models against bacteria cell size data in minimal media. Our results seems to support the idea that division starts once bacteria meet a given commitment size.

systems biology↗

bioRxiv2019

Stochasticity in bacterial division control: Preliminary consequences for protein concentration

The stochastic nature of protein concentration inside cells can have important consequences in their physiology and population fitness. Classical models of gene expression consider these processes as first-order reactions with little dependence with the cell size. However, the concentrations of the relevant molecules depend directly on the cellular volume. Here we model the cell size dynamics as exponential growth followed by division with occurrence rate proportional to the size. This framework, together with known models of chromosome replication and both protein and mRNA synthesis, lets us predict relationships between cell size and both protein number and concentration. As a main result, we find that protein production strategies (constant rate or rate proportional to either chromosome number, cell size or chromosome number times cell size) can be experimentally distinguished from the correlation between protein concentration and cell size.

systems biology↗