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Bokes, P.

Publications and source records attributed to Bokes, P..

At least 19 recordsLinked to original sources

Event timing precision in stochastic gene expression withbursty production

The timing of many cellular events is controlled by the accumulation of a regulatory molecule to a threshold level. This timing can be characterized by the first-passage time (FPT), defined as the random time required to reach the threshold for the first time. In stochastic gene expression, fluctuations in molecular production and degradation lead to variability in event timing. Feedback regulation of the production rate is a natural mechanism for reducing FPT variability. Here, we first investigate how degradation reshapes the optimal feedback law for minimizing timing variability across low- and high-threshold regimes. Using backward equations and Lagrange multiplier formulation, we derive the corresponding optimality conditions under a fixed mean FPT constraint. Exact results for the low-threshold case and asymptotic analysis in the high-threshold, small-degradation regime show that the optimal production rates increase approximately linearly with molecule number before decreasing near the threshold. For larger degradation rates, the optimality conditions develop multiple solution branches and fold behavior, and the optimal production rate structure can become nonmonotonic. We then extend the framework to bursty production and numerically characterize the optimal feedback in both low- and high-threshold regimes. The results show how degradation and bursty synthesis qualitatively reshape the structure of the optimal feedback for stochastic cellular event timing.

systems biology↗

Modulation of noisy gene expression by general sequestration mechanisms

Sequestration of gene products through diverse mechanisms forms a fundamental layer of regulation in intracellular biochemical processes, including post-translational modification, promiscuous binding to genomic decoy sites, and partitioning into membraneless compartments formed through phase separation. Here, we develop a unified stochastic framework to quantify how such sequestration-type processes, when coupled to noisy gene expression, modulate cell-to-cell variation in protein levels. In this model, protein molecules reversibly switch between active (free) and inactive (sequestered) states, whose switching rates are arbitrary functions of the molecular counts. Using exact analytical calculations and the linear noise approximation, we derive expressions for the Fano factor of the active-protein level and identify fluctuation attenuation regimes in terms of the logarithmic sensitivities of the switching rates to protein abundances. We show that inactive-protein-dependent switching, of which genomic decoy binding is a natural example, can preserve Poisson-level fluctuations in the active-protein level under appropriate conditions. Enzymatic inactivation, a type of post-translational modification, emerges as a special case of active-protein-dependent sequestration, where greater responsiveness of the inactivation propensity attenuates active-protein fluctuations. In both decoy binding and enzymatic inactivation, protecting the inactive protein from decay lowers active-protein fluctuations. Finally, a noise-buffering regime associated with intracellular phase separation is recovered when the inactive-to-active switching rate depends inversely on the inactive-protein level. Together, the examples of genomic decoy binding, enzymatic inactivation, and intracellular phase separation suggest that noise buffering observed across diverse intracellular processes is rooted in a broader class of reversible sequestration mechanisms that attenuate protein-level fluctuations.

systems biology↗

Gene Expression Variability with Feedback Regulation Implemented via Protein-Dependent Cell Growth

Variability in gene expression among single cells and growing cell populations can arise from the stochastic nature of protein synthesis, which often occurs in random bursts. This study investigates the variability in the expression of a growth-sustaining protein, whose concentration is regulated by a negative feedback loop due to cell growth-induced dilution. We model the distribution of protein concentration using a Chapman-Kolmogorov equation for single cells and a population balance equation for growing cell populations. For single cells, we derive an explicit solution for the protein concentration distribution in state space and represent it as a Bessel function in Laplace space. For growing populations, we find that the distribution satisfies a Heun differential equation with singular boundary conditions. By addressing the central connection problem for the Heun equation, we quantify the population-level protein distribution and determine the Mathusian parameter, which characterizes population growth. This work provides a comprehensive analytical framework for understanding how stochastic protein synthesis impacts gene expression variability and population dynamics.

systems biology↗

Stochastic Gene Expression under Sequestration: Noise Reduction and Emergent Distributions

Gene expression noise can be modulated by protein sequestration, a mechanism we investigate through a stochastic modeling framework. We examine how the distribution of free (non-sequestered) protein depends on sequestration cooperativity (monomers, dimers, multimers) and on the timescale separation between sequestration and protein turnover. For non-cooperative sequestration, faster kinetics drive the distribution from a high-noise to a lower-noise gamma form, while the right-tail remains governed by the high-noise limit -- revealing a non-commutativity between tail asymptotics and fast sequestration. For cooperative sequestration, the distribution departs from gamma, exhibiting left skewness or multi-modality. These results highlight how sequestration mechanisms shape protein variability in nontrivial ways.

systems biology↗

Fluctuation Test with Phenotypic Switching: A Unified Stochastic Approximation Framework

This paper examines a structurally symmetric fluctuation test experiment in which cell populations grow from a single cell to a set size before undergoing treatment. During growth, cells may acquire tolerance to treatment through probabilistic events, which are passed to progeny. Motivated by recent research on drug tolerance in microbial and cancer cells, the model also allows tolerant cells to revert to a sensitive state, reflecting dynamic phenotypic switching. The master equation governing the probability distribution of tolerant cells is solved via the generating function method and the quasi-powers approximation. Depending on model parameters, the distribution may be approximated by a stable distribution (or its special case, the normal distribution) or through large deviations theory. In the regime of frequent switching, the large deviations approach provides better agreement with numerical solutions, particularly at distribution tails. Conversely, in the regime of infrequent switching, the general stable distributions offer improved accuracy over the Landau distribution, which represents a limiting distribution in case of unidirectional switching.

systems biology↗

Bursty Gene Expression in Single Cells and Expanding Populations: A Discrete Approach

Bursty protein production is a key source of gene expression noise. In this study, we analyze a Markovian model of cellular protein dynamics, where proteins are produced in geometrically distributed bursts. Extending this model, we incorporate a feedback mechanism by assuming that higher protein levels reduce both cell growth and protein decay rates. We study both single-cell dynamics and an expanding cell population. Without feedback, the protein level follows a negative binomial distribution with the same parameters in both cases. With feedback, however, single-cell and population-level distributions differ, each expressible as a mixture of two negative binomial distributions with framework-dependent parameters. Using numerical integration of the master and population balance equations, we calculate the time-dependent distributions in both settings. This work extends previous continuous models and provides new insights into how population expansion influences intrinsic cellular heterogeneity.

systems biology↗

Hysteresis and noise floor in gene expression optimised for persistence against lethal events

Bacterial cell persistence, crucial for survival under adverse conditions like antibiotic exposure, is intrinsically linked to stochastic fluctuations in gene expression. Certain genes, while inhibiting growth under normal circumstances, confer tolerance to antibiotics at elevated expression levels. The occurrence of antibiotic events lead to instantaneous cellular responses with varied survival probabilities correlated with gene expression levels. Notably, cells with lower protein concentrations face higher mortality rates. This study aims to elucidate an optimal strategy for protein expression conducive to cellular survival. Through comprehensive mathematical analysis, we determine the optimal burst size and frequency that maximise cell proliferation. Furthermore, we explore how the optimal expression distribution changes as the cost of protein expression to growth escalates. Our model reveals a hysteresis phenomenon, characterised by discontinuous transitions between deterministic and stochastic optima. Intriguingly, stochastic optima possess a noise floor, representing the minimal level of fluctuations essential for optimal cellular resilience.

systems biology↗

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

systems biology↗

Joint Distribution of Protein Concentration and Cell Volume Coupled by Feedback in Dilution

We consider a protein that negatively regulates the rate with which a cell grows. Since less growth means less protein dilution, this mechanism forms a positive feedback loop on the protein concentration. We couple the feedback model with a simple description of the cell cycle, in which a division event is triggered when the cell volume reaches a critical threshold. Following the division we either track only one of the daughter cells (single cell framework) or both cells (population frame-work). For both frameworks, we find an exact time-independent distribution of protein concentration and cell volume. We explore the consequences of dilution feedback on ergodicity, population growth rate, and the bias of the population distribution towards faster growing cells with less protein.

systems biology↗

Reversibility of resistance in a fluctuation test experiment modifies the tail of the Luria-Delbrück distribution

We consider a fluctuation test experiment in which cell colonies are grown from a single cell until they reach a given population size, and then they are exposed to treatment. While they grow, the cells may, with a low probability, acquire resistance to treatment and pass it on to their offspring. Unlike the classical Luria-Delbruck fluctuation test and motivated by recent work on drug-resistance acquisition in cancer/microbial cells, we allow for the resistant cell state to switch back to a drug-sensitive state. This modification does not affect the central part of the (Luria-Delbruck) distribution of the number of resistant survivors: the previously developed approximation by the Landau probability density function applies. However, the right tail of the modified distribution deviates from the power law decay of the Landau distribution. We demonstrate that the correction factor is equal to the Landau cumulative distribution function.

systems biology↗

Quantifying noise modulation from coupling of stochastic expression to cellular growth: An analytical approach

The overexpression of many proteins can often have a detrimental impact on cellular growth. This expression-growth coupling leads to positive feedback - any increase of intracellular protein concentration reduces the growth rate of cell size expansion that in turn enhances the concentration via reduced dilution. We investigate how such feedback amplifies intrinsic stochasticity in gene expression to drive a skewed distribution of the protein concentration. Our results provide an exact solution to this distribution by analytically solving the Chapman-Kolmogorov equation, and we use it to quantify the enhancement of noise/skewness as a function of expression-growth coupling. This analysis has important implications for the expression of stress factors, where high levels provide protection from stress, but come at the cost of reduced cellular proliferation. Finally, we connect these analytical results to the case of an actively degraded gene product, where the degradation machinery is working close to saturation.

systems biology↗

Heavy-tailed distributions in a stochastic gene autoregulation model

Synthesis of gene products in bursts of multiple molecular copies is an important source of gene expression variability. This paper studies large deviations in a Markovian drift-jump process that combines exponentially distributed bursts with deterministic degradation. Large deviations occur as a cumulative effect of many bursts (as in diffusion) or, if the model includes negative feedback in burst size, in a single big jump. The latter possibility requires a modification in the WKB solution in the tail region. The main result of the paper is the construction, via a modified WKB scheme, of matched asymptotic approximations to the stationary distribution of the drift-jump process. The stationary distribution possesses a heavier tail than predicted by a routine application of the scheme. MSC 202092C40; 60J76, 45D05, 41A60

systems biology↗

Protein noise and distribution in a two-stage gene-expression model extended by an mRNA inactivation loop

Chemical reaction networks involving molecular species at low copy numbers lead to stochasticity in protein levels in gene expression at the single-cell level. Mathematical modelling of this stochastic phenomenon enables us to elucidate the underlying molecular mechanisms quantitatively. Here we present a two-stage stochastic gene expression model that extends the standard model by an mRNA inactivation loop. The extended model exhibits smaller protein noise than the original two-stage model. Interestingly, the fractional reduction of noise is a non-monotonous function of protein stability, and can be substantial especially if the inactivated mRNA is stable. We complement the noise study by an extensive mathematical analysis of the joint steady-state distribution of active and inactive mRNA and protein species. We determine its generating function and derive a recursive formula for the protein distribution. The results of the analytical formula are cross-validated by kinetic Monte-Carlo simulation.

systems biology↗

A modified fluctuation test for elucidating drug resistance in microbial and cancer cells

Clonal populations of microbial and cancer cells are often driven into a drug-tolerant persister state in response to drug therapy, and these persisters can subsequently adapt to the new drug environment via genetic and epigenetic mechanisms. Estimating the frequency with which drug-tolerance states arise, and its transition to drug-resistance, is critical for designing efficient treatment schedules. Here we study a stochastic model of cell proliferation where drug-tolerant persister cells transform into a drug-resistant state with a certain adaptation rate, and the resistant cells can then proliferate in the presence of the drug. Assuming a random number of persisters to begin with, we derive an exact analytical expression for the statistical moments and the distribution of the total cell count (i.e., colony size) over time. Interestingly, for Poisson initial conditions the noise in the colony size (as quantified by the Fano factor) becomes independent of the initial condition and only depends on the adaptation rate. Thus, experimentally quantifying the fluctuations in the colony sizes provides an estimate of the adaptation rate, which then can be used to infer the starting persister numbers from the mean colony size. Overall, our analysis introduces a modification of the classical Luria-Delbruck experiment, also called the "Fluctuation Test", providing a valuable tool to quantify the emergence of drug resistance in cell populations.

systems biology↗

Exact and WKB-approximate distributions in a gene expression model with feedback in burst frequency, burst size, and protein stability

The expression of individual genes into functional protein molecules is a noisy dynamical process. Here we model the protein concentration as a jump-drift process which combines discrete stochastic production bursts (jumps) with continuous deterministic decay (drift). We allow the drift rate, the jump rate, and the jump size to depend on the protein level to implement feedback in protein stability, burst frequency, and burst size. We specifically focus on positive feedback in burst size, while allowing for arbitrary autoregulation in burst frequency and protein stability. Two versions of feedback in burst size are thereby considered: in the first, newly produced molecules instantly participate in feedback, even within the same burst; in the second, within-burst regulation does not occur due to the so-called infinitesimal delay. Without infinitesimal delay, the model is explicitly solvable; with its inclusion, an exact distribution to the model is unavailable, but we are able to construct a WKB approximation that applies in the asymptotic regime of small but frequent bursts. Comparing the asymptotic behaviour of the two model versions, we report that they yield the same WKB quasi-potential but a different exponential prefactor. We illustrate the difference on the case of a bimodal protein distribution sustained by a sigmoid feedback in burst size: we show that the omission of the infinitesimal delay overestimates the weight of the upper mode of the protein distribution. The analytic results are supported by kinetic Monte-Carlo simulations.

systems biology↗

Postponing production exponentially enhances the molecular memory of a stochastic switch

Delayed production can substantially alter the qualitative behaviour of feedback systems. Motivated by stochastic mechanisms in gene expression, we consider a protein molecule which is produced in randomly timed bursts, requires an exponentially distributed time to activate, and then partakes in positive regulation of its burst frequency. Asymptotically analysing the underlying master equation in the large-delay regime, we provide tractable approximations to time-dependent probability distributions of molecular copy numbers. Importantly, the presented analysis demonstrates that positive feedback systems with large production delays can constitute a stable toggle switch even if they operate with low copy numbers of active molecules.Competing Interest StatementThe authors have declared no competing interest.View Full Text

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The role of incoherent feedforward circuits in regulating precision of event timing

Triggering of cellular events often relies on the level of a key gene product crossing a critical threshold. Achieving precision in event timing in spite of noisy gene expression facilitates high-fidelity functioning of diverse processes from biomolecular clocks, apoptosis and cellular differentiation. Here we investigate the role of an incoherent feedforward circuit in regulating the time taken by a bacterial virus (bacteriophage lambda) to lyse an infected Escherichia coli cell. Lysis timing is the result of expression and accumulation of a single lambda protein (holin) in the E. coli cell membrane up to a critical threshold level, which triggers the formation of membrane lesions. This easily visualized process provides a simple model system for characterizing event-timing stochasticity in single cells. Intriguingly, lambdas lytic pathway synthesizes two functionally opposite proteins: holin and antiholin from the same mRNA in a 2:1 ratio. Antiholin sequesters holin and inhibits the formation of lethal membrane lesions, thus creating an incoherent feedforward circuit. We develop and analyze a stochastic model for this feedforward circuit that considers correlated bursty expression of holin/antiholin, and their concentrations are diluted from cellular growth. Interestingly, our analysis shows the noise in timing is minimized when both proteins are expressed at an optimal ratio, hence revealing an important regulatory role for antiholin. These results are in agreement with single cell data, where removal of antiholin results in enhanced stochasticity in lysis timing.

systems biology↗

Stationary distributions and metastable behaviour for self-regulating proteins with general lifetime distributions

Regulatory molecules such as transcription factors are often present at relatively small copy numbers in living cells. The copy number of a particular molecule fluctuates in time due to the random occurrence of production and degradation reactions. Here we consider a stochastic model for a self-regulating transcription factor whose lifespan (or time till degradation) follows a general distribution modelled as per a multidimensional phase-type process. We show that at steady state the protein copy-number distribution is the same as in a one-dimensional model with exponentially distributed lifetimes. This invariance result holds only if molecules are produced one at a time: we provide explicit counterexamples in the bursty production regime. Additionally, we consider the case of a bistable genetic switch constituted by a positively autoregulating transcription factor. The switch alternately resides in states of up- and downregulation and generates bimodal protein distributions. In the context of our invariance result, we investigate how the choice of lifetime distribution affects the rates of metastable transitions between the two modes of the distribution. The phase-type model, being non-linear and multi-dimensional whilst possessing an explicit stationary distribution, provides a valuable test example for exploring dynamics in complex biological systems.

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