Biology subjects

Vahdat, Z.

Publications and source records attributed to Vahdat, Z..

11 recordsLinked to original sources

bioRxiv2026

Stochastic and Heritable Responses in Cell-Size Regulation and Survival to pH Stress in C. glutamicum

Bacterial survival in fluctuating environments depends on the ability of individual cells to maintain physiological homeostasis under stress, yet how single cells recover from transient pH fluctuations remains poorly understood. We tracked the cell-size dynamics and survival of Corynebacterium glutamicum cells exposed to short, strong acid (pH 4, 20 min) or alkaline (pH 11, 15 min) pulses. Acid stress considerably increases the size added during the stress cycle, whereas alkaline stress increases it only modestly. Nevertheless, surviving cells maintain their division strategy and restore size homeostasis in the next cycle. Coordination between relatives is short-lived: sister cells have similar cell-cycle durations after stress, but durations become uncorrelated once the last division shared by two cells precedes the stress by more than about 1.5 generations. Survival of acid stress favors larger cells and is largely stochastic, with no detectable lineage heritability. In contrast, survival of alkaline stress favors smaller cells and is transiently heritable: co-survival probability remains high among relatives separated by up to two generations. These findings provide insight into pH survival in C. glutamicum and present statistical methods to study cell-size regulation and survival under transient, severe environmental fluctuations.

systems biology↗

bioRxiv2025

Regulation of Transcriptional Bursting and Spatial Patterning in Early Drosophila Embryo Development

Nascent RNA synthesis often occurs in periods of high transcriptional activity, interspersed with basal or no activity periods. This phenomenon, known as transcriptional bursting, drives high intercellular variability in gene expression levels. How do key patterning genes in early Drosophila melanogaster embryos overcome this variability to establish precise spatial patterns for tissue development? To address this question, we study single cell transcriptional activity from MS2-based live imaging data of four transgenic constructs (rho, Kr, sna shadow, sna proximal enhancers) and the endogenous eve gene. We developed an algorithm to infer promoter states in hundreds of cells within the embryo using transcriptional activity data. Results show that while mean transcription levels exhibit spatial gradients, the burst duration and interburst timing remain surprisingly invariant across the embryo and different constructs. The time between consecutive bursts was consistent with a memoryless exponential distribution, whereas the burst duration exhibited tighter control with lesser stochastic variations. Our analysis identified two regulatory mechanisms for gene expression gradients: (1) similar burst-timing statistics across genes, with the activity time (the time from the first to the last burst) being modulated to regulate distinct expression levels; (2) for the same gene with different enhancers (sna shadow and sna proximal), we observed changes in the mean burst duration and variability of the inter-burst timing. This study provides a comprehensive approach to analyzing transcriptional bursting kinetics, revealing activity time as a major regulator of spatiotemporal expression patterning in early embryonic development.

systems biology↗

bioRxiv2024

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↗

bioRxiv2024

Leveraging the transient statistics of quantal content to infer neuronal synaptic transmission

Quantal parameters of synapses are fundamental for the temporal dynamics of neurotransmitter release, forming the basis of interneuronal communication. We propose a class of models that capture the stochastic dynamics of quantal content (QC) - the number of SV fusion events per action potential (AP). Considering the probabilistic and time-varying nature of SV docking, undocking, and AP-triggered fusion, we derive an exact statistical distribution for the QC over time. Analyzing this distribution at steady-state and its associated autocorrelation function, we show that QC fluctuation statistics can be leveraged for inferring key presynaptic parameters, such as the probability of SV fusion (release probability), and SV replenishment at empty docking sites (refilling probability). Our model predictions are tested with electrophysiological data obtained from 50-Hz stimulation of auditory MNTB-LSO synapses in brainstem slices of juvenile mice. Our results show that while synaptic depression can be explained by low and constant refilling/release probabilities, this scenario is inconsistent with the statistics of the electrophysiological data that show a low QC Fano factor and almost uncorrelated successive QCs. Our systematic analysis yields a model that couples a high release probability to a time-varying refilling probability to explain both the synaptic depression and its associated statistical fluctuations. In summary, we provide a general approach that exploits stochastic signatures in QCs to infer neurotransmission-regulating processes that are indistinguishable from just analyzing averaged synaptic responses.

neuroscience↗

bioRxiv2024

Feedback and Feedforward Regulation of Interneuronal Communication

We formulate a mechanistic model capturing the dynamics of neurotransmitter release in a chemical synapse. The proposed modeling framework captures key aspects such as the random arrival of action potentials (AP) in the presynaptic (input) neuron, probabilistic docking and release of neurotransmitter-filled vesicles, and clearance of the released neurotransmitter from the synaptic cleft. Feedback regulation is implemented by having the released neurotransmitter impact the vesicle docking rate that occurs biologically through "autoreceptors" on the presynaptic membrane. Our analytical results show that these feedbacks can amplify or buffer fluctuations in neurotransmitter levels depending on the relative interplay of neurotransmitter clearance rate with the AP arrival rate and the vesicle replenishment rate, with faster clearance rates leading to noise amplification. We next consider a postsynaptic (output) neuron that fires an AP based on integrating upstream neurotransmitter activity. Investigating the postsynaptic AP firing times, we identify scenarios that lead to band-pass filtering, i.e., the output neuron frequency is maximized at intermediate input neuron frequencies. We extend these results to consider feedforward regulation where in addition to a direct excitatory synapse, the input neuron also impacts the output indirectly via an inhibitory interneuron, and we identify parameter regimes where feedforward neuronal networks result in band-pass filtering.

neuroscience↗

bioRxiv2023

Coupling Cell Size Regulation and Proliferation Dynamics of C.glutamicum Reveals Cell Division Based on Surface Area

Single cells actively coordinate growth and division to regulate their size, yet how this size homeostasis at the single-cell level propagates over multiple generations to impact clonal expansion remains fundamentally unexplored. Classical timer models for cell proliferation (where the duration of the cell cycle is an independent variable) predict that the stochastic variation in colony size will increase over time. In stark contrast, implementing size control according to adder strategy (where on average a fixed size added from cell birth to division) leads to colony size variations that eventually decay to zero. While these results assume a fixed size of the colony-initiating progenitor cell, further analysis reveals that the magnitude of the intercolony variation in population number is sensitive to heterogeneity in the initial cell size. We validate these predictions by tracking the growth of isogenic microcolonies of Corynebacterium glutamicum in microfluidic chambers. Approximating their cell shape to a capsule, we observe that the degree of random variability in cell size is different depending on whether the cell size is quantified as per length, surface area, or volume, but size control remains an adder regardless of these size metrics. A comparison of the observed variability in the colony population with predictions suggests that proliferation matches better with a cell division based on the cell surface. In summary, our integrated mathematical-experimental approach bridges the paradigms of single-cell size regulation and clonal expansion at the population levels. This innovative approach provides elucidation of the mechanisms of size homeostasis from the stochastic dynamics of colony size for rod-shaped microbes.

systems biology↗

bioRxiv2023

Characterizing the role of autaptic feedback in enhancing precision of neuronal firing times

In a chemical synapse, information flow occurs via the release of neurotransmitters from a presynaptic neuron that triggers an Action potential (AP) in the postsynaptic neuron. At its core, this occurs via the postsynaptic membrane potential integrating neurotransmitter-induced synaptic currents, and AP generation occurs when potential reaches a critical threshold. This manuscript investigates feedback implementation via an autapse, where the axon from the postsynaptic neuron forms an inhibitory synapse onto itself. Using a stochastic model of neuronal synaptic transmission, we formulate AP generation as a first-passage time problem and derive expressions for both the mean and noise of AP-firing times. Our analytical results supported by stochastic simulations identify parameter regimes where autaptic feedback transmission enhances the precision of AP firing times consistent with experimental data. These noise attenuating regimes are intuitively based on two orthogonal mechanisms - either expanding the time window to integrate noisy upstream signals; or by linearizing the mean voltage increase over time. Interestingly, we find regimes for noise amplification that specifically occur when the inhibitory synapse has a low probability of release for synaptic vesicles. In summary, this work explores feedback modulation of the stochastic dynamics of autaptic neurotransmission and reveals its function of creating more regular AP firing patterns.

neuroscience↗

bioRxiv2023

Stochastic dynamics of ecological populations subjected to environmental perturbations

The stochastic logistic model is widely used to capture random fluctuations arising from birth-death processes in ecological populations. We use this model to study the impact of environmental perturbations that may occur naturally or as a consequence of population harvesting. In our model formulation, environmental perturbations occur randomly as per a Poisson process, and the perturbations result in each individual dying with a certain probability of death. Moment closure schemes are employed to derive expressions for the mean and variability in population numbers. Moreover, to quantify the impact of population extinction in our model we compute the probability of extinction using the Finite State Projection (FSP) numerical method. Our analysis shows that rare environmental perturbations with a high probability of death lead to overall larger random fluctuations and extinction risk as compared to frequent perturbations with a low probability of death. Finally, we formulate the problem in the context of population harvesting to find the optimal harvesting rate that maximizes the cumulative yield.

ecology↗

bioRxiv2022

Stochastic analysis of frequency bandwidth and noise attenuation in neurotransmission

Action potential (AP)-triggered neurotransmitter release forms the key basis of inter-neuronal communication. We present a stochastic hybrid system model that captures the release of neurotransmitter-filled vesicles from a presynaptic neuron. More specifically, vesicles arrive as a Poisson process to attach at a given number of docking sites, and each docked vesicle has a certain probability of release when an AP is generated in the presynaptic neuron. The released neurotransmitters enhance the membrane potential of the postsynaptic neuron, and this increase is coupled to the continuous exponential decay of the membrane potential. The buildup of potential to a critical threshold level results in an AP firing in the postsynaptic neuron, with the potential subsequently resetting back to its resting level. Our model analysis develops formulas that quantify the fluctuations in the number of released vesicles and mechanistically connects them to fluctuations in both the postsynaptic membrane potential and the AP firing times. Increasing the frequency of APs in the presynaptic neuron leads to saturation effects on the postsynaptic side, resulting in a limiting frequency range of neurotransmission. Interestingly, AP firing in the postsynaptic neuron becomes more precise with increasing AP frequency in the presynaptic neuron. We also investigate how noise in AP timing varies with different parameters, such as the probability of releases, the number of docking sites, the voltage threshold for AP firing, and the timescale of voltage decay. In summary, our results provide a systematic understanding of how stochastic mechanisms in neurotransmission enhance or impinge the precision of AP fringing times.

neuroscience↗

bioRxiv2019

Channel Capacity Computations for Unregulated and Autoregulated Gene Expression

How living cells can reliably process biochemical cues in the presence of molecular noise is not fully understood. Here we investigate the fidelity of information transfer in the expression of a single gene. We use the established model of gene expression to examine how precisely the protein levels can be controlled by two distinct mechanisms: (i) the transcription rate of the gene, or (ii) the translation rate for the corresponding mRNA. The fidelity of gene expression is quantified with the information-theoretic notion of information capacity. Derived information capacity formulae reveal that transcriptional control generally provides a tangibly higher capacity as compared to the translational control. We next introduce negative feedback regulation in gene expression, where the protein directly inhibits its own transcription. While negative feedback reduces noise in the level of the protein for a given input signal, it also decreases the input-to-output sensitivity. Our results show that the combined effect of these two opposing forces is a reduced capacity in the presence of feedback. In summary, our analysis presents the first analytical quantification of information transfer in simple gene expression models, which provides insight into the fidelity of basic gene expression control mechanisms.

systems biology↗

bioRxiv2019

Characterizing neuronal synaptic transmission using stochastic hybrid systems

Action potential-triggered release of neurotransmitters at chemical synapses forms the key basis of communication between two neurons. To quantify the stochastic dynamics of the number of neurotransmitters released, we investigate a model where neurotransmitter-filled vesicles attach to a finite number of docking sites in the axon terminal, and are subsequently released when the action potential arrives. We formulate the model as a Stochastic Hybrid System (SHS) that combines three key noise mechanisms: random arrival of action potentials, stochastic refilling of docking sites, and probabilistic release of docked vesicles. This SHS representation is used to derive exact analytical formulas for the mean and noise (as quantified by Fano factor) in the number of vesicles released per action potential. Interestingly, results show that in relevant parameter regimes, noise in the number of vesicles released is sub-Poissonian at low frequencies, super-Poissonian at intermediate frequencies, and approaches a Poisson limit at high frequencies. In contrast, noise in the number of neurotransmitters in the synaptic cleft is always super-Poissonian, but is lowest at intermediate frequencies. We further investigate changes in these noise properties for non-Poissonian arrival of action potentials, and when the probability of release is frequency dependent. In summary, these results provide the first glimpse into synaptic parameters not only determining the mean synaptic strength, but also shaping its stochastic dynamics that is critical for information transfer between neurons.

neuroscience↗