Biology subjects

Sukys, A.

Publications and source records attributed to Sukys, A..

4 recordsLinked to original sources

bioRxiv2025

Intercellular signaling drives robust cell fate and patterning in multicellularsystems

Cells do not act in isolation; they communicate with each other through a complex network of signals. In multicellular organisms, cell signaling buffers against fluctuations in gene expression. Its effect, however, on multistability--the ability of genetically identical cells to take on and maintain diverse stable states or phenotypes--is unclear. We develop spatially explicit stochastic models that couple fine-grained gene regulatory dynamics with intercellular signaling to study cell fate control in multicellular tissues. We show that intercellular signaling acts as a switch-like controller of cell fate, driving transitions from transient to stable states across tissues. Even weak signaling stabilizes short-lived expression states, and we derive mathematical expressions for the threshold signaling strength required to trigger tissue-wide stabilization. We also derive a universal spatial limit: stable region size grows only sublinearly with signaling strength, implying that large stable regions are prohibitively costly to maintain. The resulting trade-off between robustness and signaling in cell fate control highlights the delicate balance required during developmental processes to maintain spatial precision and provides insight into how organisms regulate tissue structure. OverviewCell fate decisions in developing tissues are governed by gene regulatory networks, but how communication between neighboring cells influences these decisions is poorly understood. We develop a mathematical framework showing that intercellular signaling acts as a controller of cell fate, suppressing spontaneous switching, and inducing stable, coordinated behaviors across tissues. Even weak signaling induces a sharp transition from unstable to stable population-wide dynamics. We also derive fundamental scaling laws showing that the size of stable regions increases sublinearly with signaling strength, revealing a trade-off between developmental robustness and resource cost. These results offer insight into how organisms achieve reliable spatial organization during development, despite molecular noise, and provide design principles for engineering synthetic tissues that balance stability, scalability, and resource efficiency.

systems biology↗

bioRxiv2024

Transcriptome-wide analysis of cell cycle-dependent bursty gene expression from single-cell RNA-seq data using mechanistic model-based inference

Bursty gene expression is characterised by two intuitive parameters, burst frequency and burst size, the cell-cycle dependence of which has not been extensively profiled at the transcriptome level. In this study, we estimate the burst parameters per allele in the G1 and G2/M cell-cycle phases for thousands of mouse genes by fitting mechanistic models of gene expression to mRNA count data, obtained by sequencing of single cells whose cell-cycle position has been inferred using a deep-learning method. We find that upon DNA replication, the median burst frequency approximately halves, while the burst size remains mostly unchanged. Genome-wide distributions of the burst parameter ratios between the G2/M and G1 phases are wide, indicating substantial heterogeneity in transcriptional regulation patterns. We also observe a significant negative correlation between the burst frequency and size ratios, suggesting that regulatory processes do not independently control the burst parameters. Finally, we argue that to accurately estimate the burst parameter ratios, mechanistic models must explicitly account for gene copy number variation and extrinsic noise due to the coupling of transcription to cell age within the cell cycle, but corrections for technical noise due to imperfect capture of RNA molecules in sequencing experiments are less critical.

systems biology↗

bioRxiv2022

Approximating Solutions of the Chemical Master Equation using Neural Networks

The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved analytically for most systems of practical interest. While Monte Carlo methods provide a principled means to probe the system dynamics, their high computational cost can render the estimation of molecule number distributions and other numerical tasks infeasible due to the large number of repeated simulations typically required. In this paper we aim to leverage the representational power of neural networks to approximate the solutions of the CME and propose a framework for Neural Estimation of Stochastic Simulations for Inference and Exploration (Nessie). Our approach is based on training a neural network to learn the distributions predicted by the CME from a relatively small number of stochastic simulations, thereby accelerating computationally intensive tasks such as parameter exploration and inference. We show on biologically relevant examples that simple neural networks with one hidden layer are able to capture highly complex distributions across parameter space. We provide a detailed discussion of the neural network implementation and code for easy reproducibility.

systems biology↗

bioRxiv2020

Stochastic time-dependent enzyme kinetics: closed-form solution and transient bimodality

We derive an approximate closed-form solution to the chemical master equation describing the Michaelis-Menten reaction mechanism of enzyme action. In particular, assuming that the probability of a complex dissociating into enzyme and substrate is significantly larger than the probability of a product formation event, we obtain expressions for the time-dependent marginal probability distributions of the number of substrate and enzyme molecules. For delta function initial conditions, we show that the substrate distribution is either unimodal at all times or else becomes bimodal at intermediate times. This transient bimodality, which has no deterministic counterpart, manifests when the initial number of substrate molecules is much larger than the total number of enzyme molecules and if the frequency of enzyme-substrate binding events is large enough. Furthermore, we show that our closed-form solution is different from the solution of the chemical master equation reduced by means of the widely used discrete stochastic Michaelis-Menten approximation, where the propensity for substrate decay has a hyperbolic dependence on the number of substrate molecules. The differences arise because the latter does not take into account enzyme number fluctuations while our approach includes them. We confirm by means of stochastic simulation of all the elementary reaction steps in the Michaelis-Menten mechanism that our closed-form solution is accurate over a larger region of parameter space than that obtained using the discrete stochastic Michaelis-Menten approximation.

systems biology↗