Biology subjects

Shahrezaei, V.

Publications and source records attributed to Shahrezaei, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Overinflation and overconcentration: why Cauchy perturbation kernels are the right choice for ABC-SMC

Approximate Bayesian computation sequential Monte Carlo (ABC-SMC) propagates its particles with a perturbation kernel, and with the standard Normal kernel it degrades sharply as the parameter dimension grows, a failure usually attributed to dimension itself. We show instead that it is governed by the quality of the summary statistics, with dimension entering only through a separate and milder mechanism, and that the two must act together for the Normal kernel to break. The first ingredient is covariance overinflation: the kernel covariance, estimated from the particle cloud, overshoots the true posterior covariance by a factor set by information loss in the summary statistics. We derive this overscaling factor in closed form for a Gaussian model with sufficient statistics and show that it stays modest at any dimension, shrinking toward its baseline value as the tolerance tightens; the extreme values seen in practice (of order 103) are a signature of insufficient summaries, not of dimension. The second ingredient is perturbation overconcentration: the normalised Normal step size concentrates around one as the dimension grows, so every proposal overshoots by the same factor. Either ingredient alone is harmless; only their combination breaks the Normal kernel. A Cauchy kernel (multivariate t with one degree of freedom) removes the concentration, keeping a positive acceptance rate under arbitrary overscaling at a bounded worst-case cost of 1.87x in expected squared jump distance. In a Metropolis-Hastings framework we derive closed-form acceptance rates for both kernels that illustrate the advantage of the Cauchy kernel in this limit. A series of full ABC-SMC computational experiments on five problems at d = 12, including a hierarchical gene-expression model, show the Cauchy reducing the sliced Wasserstein distance to the reference posterior by factors of up to 50 with the same simulation budget. Since the summary statistics are commonly insufficient for the models that require ABC, overinflation is structural and the Cauchy perturbation kernel is the right default for problems in higher dimensions.

systems biology↗

bioRxiv2024

An integrated stress response-independent role of GCN2 prevents excessive ribosome biogenesis and mRNA translation

The Integrated Stress Response (ISR) is a corrective physiological program to restore cellular homeostasis that is based on the attenuation of global protein synthesis and a resource-enhancing transcriptional programme. GCN2 is the oldest of four kinases that are activated by diverse cellular stresses to trigger the ISR and acts as the primary responder to amino acid shortage and ribosome collisions. Here, using a broad multi-omic approach, we uncover an ISR-independent role of GCN2. GCN2 inhibition or depletion in the absence of discernible stress causes excessive protein synthesis and ribosome biogenesis, perturbs the cellular translatome, and results in a dynamic and broad loss of metabolic homeostasis. Cancer cells that rely on GCN2 to keep protein synthesis in check under conditions of full nutrient availability depend on GCN2 for survival and unrestricted tumour growth. Our observations define an ISR-independent role of GCN2 in regulating the cellular proteome and translatome and suggest new avenues for cancer therapies based on unleashing excessive mRNA translation.

cancer biology↗

bioRxiv2024

Forecasting the adoption and spread of a community-based marine management initiative using agent-based models

While many successful initiatives for conserving nature exist, efforts to take them to scale have been inadequate. Moreover, conservation science currently lacks a systematic methodology for determining if or when interventions will reach effective scales and how programmatic decisions will affect the scaling process. This paper presents a modelling framework that aims to address both issues by operationalizing Diffusion of Innovations theory and local knowledge using agent-based modelling and Bayesian inference. By applying our framework to existing data on the spatiotemporal adoption of a community-based marine management initiative in Fiji, we demonstrate that our approach can identify the mechanisms that govern the observed adoption patterns. In this case, the relative advantage of the intervention, village social networks, and perceived knowledge stand out as important drivers of adoption. Using the identified causal processes, our approach can forecast business-as-usual and counterfactual future scenarios and hence inform conservation policy. Finally, we highlight the importance of spatiotemporal data for making detailed scaling predictions. We structure the paper as a step-by-step guide, highlighting our modelling decisions and possible limitations. Thus, besides presenting a case study, this paper serves as a template for practitioners and researchers to better model the scaling process of other conservation interventions.

ecology↗

bioRxiv2023

Inference of the core gene regulatory network underlying seam cell development in Caenorhabditis elegans

Gene regulatory networks are fundamental in cellular decision-making, yet even in well-studied systems, their topologies are often poorly characterised. The nematode Caenorhabditis elegans contains a population of stem-like cells, known as seam cells. While seam cells are essential to generate the majority of the animal epidermis as well as specific neurons, the architecture of the underlying gene network has not been elucidated. Here, we combine experiments, mathematical modelling and statistical inference to uncover the architecture of the seam cell gene network focusing on three core transcription factors (TFs), the GATA factors ELT-1, EGL-18 and the Engrailed homolog CEH-16. We use single-molecule FISH (smFISH) to quantify TF mRNA abundance in single seam cells in both wild type and mutant backgrounds. We then predict potential TF interactions and their quantitative strengths using a combination of Modular Response Analysis, ordinary differential equations and a Bayesian model discovery approach. Taken together, our findings suggest new relationships between core TFs in seam cells and highlight an approach that can be used to infer quantitative networks from smFISH data.

systems biology↗

bioRxiv2023

Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA-sequencing

Single-cell transcriptomics reveals significant variations in the transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA-sequencing through metabolic labelling protocols and cell cycle reporters. We develop a parallel and highly scalable Approximate Bayesian Computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modelling of dynamical correlations identifies global mechanisms of transcription regulation.

systems biology↗

bioRxiv2023

Multi-signal regulation of the GSK-3β homolog Rim11 governs meiosis entry in yeast

Starvation of budding yeast diploid cells induces the cell-fate program that drives meiosis and spore formation. Transcription activation of early meiotic genes (EMGs) requires the transcription activator Ime1, its DNA-binding partner Ume6, and GSK-3{beta} kinase Rim11. Phosphorylation of Ume6 by Rim11 is key for EMG activation. We report that Rim11 integrates multiple input signals to control Ume6 phosphorylation and EMG transcription. Under nutrient-rich conditions PKA represses Rim11 to low levels while TORC1 keeps Rim11 localized to the cytoplasm. Inhibiting PKA and TORC1 induces Rim11 expression and nuclear localization. Remarkably, nuclear Rim11 is required, but not sufficient, for Rim11-dependent Ume6 phosphorylation. Additionally, Ime1 is an essential anchor protein for phosphorylating Ume6. Subsequently, Ume6-Ime1 coactivator complexes form that drive EMG transcription. Our results demonstrate how varied signalling inputs (PKA/TORC1/Ime1) integrated by Rim11 determine EMG expression and entry into meiosis. We propose that the signalling-regulatory network described here generates robustness in cell-fate control.

molecular biology↗

bioRxiv2023

Bayesian model discovery for reverse-engineering biochemical networks from data

The reverse engineering of gene regulatory networks based on gene expression data is a challenging inference task. A related problem in computational systems biology lies in identifying signalling networks that perform particular functions, such as adaptation. Indeed, for many research questions, there is an ongoing search for efficient inference algorithms that can identify the simplest model among a larger set of related models. To this end, in this paper, we introduce SLIO_SCPLOWNC_SCPLOWG, a Bayesian sparse likelihood-free inference method using Gibbs sampling. We demonstrate that SLIO_SCPLOWNC_SCPLOWG can reverse engineer stochastic gene regulatory networks from single-cell data with high accuracy, outperforming state-of-the-art correlation-based methods. Furthermore, we show that SLIO_SCPLOWNC_SCPLOWG can successfully identify signalling networks that execute adaptation. Sparse hierarchical Bayesian inference thus provides a versatile tool for model discovery in systems biology and beyond.

systems biology↗

bioRxiv2023

Fission yeast obeys a linear size law under nutrient titration

Steady-state cell size and geometry depend on growth conditions. Here, we use an experimental setup based on continuous culture and single-cell imaging to study how cell volume, length, width and surface-to-volume ratio vary across a range of growth conditions including nitrogen and carbon titration, the choice of nitrogen source, and translation inhibition. Overall, we find cell geometry is not fully determined by growth rate and depends on the specific mode of growth rate modulation. However, under nitrogen and carbon titrations, we observe that the cell volume and the growth rate follow the same linear scaling. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/536544v1_fig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1d5c614org.highwire.dtl.DTLVardef@1bf877corg.highwire.dtl.DTLVardef@157f140org.highwire.dtl.DTLVardef@9fc243_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigureC_FLOATNO A. Graphical outline of the growth and imaging assays. B. Illustration of the procedure used to extract cell size and geometry data. C. Average surface-to-volume (S/V) ratio plotted as a function of average cell width across all steady-state cultures. The dark grey circle indicates the base growth medium, EMM2. Cultures limited in their growth by the concentration of ammonium, glucose, and cycloheximide in the medium are indicated with cyan lozenges, green triangles, and orange squares, respectively. Cultures limited by the choice of nitrogen source are indicated with light grey circles; the amino-acid nitrogen source used is labelled with its three-letter abbreviation, using Amm for the equivalent culture grown with ammonium chloride as its sole nitrogen source. D. Average cell volume plotted against the growth rate across all cultures, showing collapse for ammonium- and glucose-limited cultures and differing behaviour for nitrogen-source- and translation-limited cultures. Plotted in dark grey is a linear fit to the ammonium- and glucose-limited data, including the base medium, with 95% confidence interval; a similar fit (without CI) to the cycloheximide-limited cultures is indicated by a dashed orange line. E. Average surface-to-volume (S/V) ratio against growth rate across all cultures, with dashed lines for ammonium-, glucose-, and translation-limited cultures representing linear fits to the respective data, each including the base medium. Under ammonium limitation, the surface-to-volume ratio increases markedly as the growth rate decreases. A moderate increase is observed for glucose limitation and a moderate decrease is observed for protein synthesis inhibition with cycloheximide. There is no consistent trend with the growth rate when the quality of the nitrogen source is varied. F. Average surface area, G. cell length, H. cell width against growth rate, showing that ammonium-limited cells are thinner than glucose-limited cells at equivalent growth rates, consistent with their different surface-to-volume ratios and the relation between surface-to-volume ratio and cell width (see C). C_FIG

systems biology↗

bioRxiv2023

A coarse-grained resource allocation model of carbon and nitrogen metabolism in unicellular microbes

Coarse-grained resource allocation models (C-GRAMs) are simple mathematical models of whole cell physiology, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models provides insights on optimal allocation of cellular resources and have explained experimentally observed cellular growth laws. Here, we formulate a minimal C-GRAM with nitrogen and carbon pathways converging on biomass production, with parameterizations accounting for respiro-fermentative and purely respiratory growth. The model describes the effects of the uptake of sugars, ammonium, and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximised the growth rate. It robustly recovers cellular growth laws including the Monod law and the ribosomal growth law. Furthermore, we show how the growth-maximizing balance between carbon uptake, recycling, and excretion depends on the nutrient environment. Lastly, we find a robust linear correlation between the ribosome fraction and the abundance of amino acid equivalents in the optimal cell, which supports the view that simple regulation of translational gene expression can enable cells to achieve an approximately optimal growth state.

systems biology↗

bioRxiv2023

Modelling capture efficiency of single cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful in application to both allele and non-allele specific scRNA-seq data.

bioinformatics↗

bioRxiv2021

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros). To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of synthetic scRNA-seq data using multiscale modelling of stochastic gene regulatory networks in growing and dividing cells. Our tool produces realistic transcription data with a known ground truth network topology that can be used to benchmark different approaches for gene regulatory network inference. Using this tool we investigate the impact of different imputation methods on the performance of several network inference algorithms. Biomodelling.jl provides a versatile and useful tool for future development and benchmarking of network inference approaches using scRNA-seq data.

systems biology↗

bioRxiv2021

Efficient inference for agent-based models of real-world phenomena

The modelling of many real-world problems relies on computationally heavy simulations. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue based on machine learning methods. One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumnavigate the need for Bayesian sampling schemes and directly estimate the posterior distribution. We focus on stochastic simulations that track autonomous agents and present two case studies of real-world applications: tumour growths and the spread of infectious diseases. We demonstrate that good accuracy in inference can be achieved with a relatively small number of simulations, making our machine learning approaches orders of magnitude faster than classical simulation-based methods that rely on sampling the parameter space. However, we find that while some methods generally produce more robust results than others, no algorithm offers a one-size-fits-all solution when attempting to infer model parameters from observations. Instead, one must choose the inference technique with the specific real-world application in mind. The stochastic nature of the considered real-world phenomena poses an additional challenge that can become insurmountable for some approaches. Overall, we find machine learning approaches that create direct inference machines to be promising for real-world applications. We present our findings as general guidelines for modelling practitioners. Author summaryComputer simulations play a vital role in modern science as they are commonly used to compare theory with observations. One can thus infer the properties of a observed system by comparing the data to the predicted behaviour in different scenarios. Each of these scenarios corresponds to a simulation with slightly different settings. However, since real-world problems are highly complex, the simulations often require extensive computational resources, making direct comparisons with data challenging, if not insurmountable. It is, therefore, necessary to resort to inference methods that mitigate this issue, but it is not clear-cut what path to choose for any specific research problem. In this paper, we provide general guidelines for how to make this choice. We do so by studying examples from oncology and epidemiology and by taking advantage of developments in machine learning. More specifically, we focus on simulations that track the behaviour of autonomous agents, such as single cells or individuals. We show that the best way forward is problem-dependent and highlight the methods that yield the most robust results across the different case studies. We demonstrate that these methods are highly promising and produce reliable results in a small fraction of the time required by classic approaches that rely on comparisons between data and individual simulations. Rather than relying on a single inference technique, we recommend employing several methods and selecting the most reliable based on predetermined criteria.

bioinformatics↗

bioRxiv2021

A push-pull system of repressors matches levels of glucose transporters to extracellular glucose in budding yeast

A common cellular task is to match gene expression dynamically to a range of concentrations of a regulatory molecule. Studying glucose transport in budding yeast, we determine mechanistically how such matching occurs for seven hexose transporters. By combining time-lapse microscopy with mathematical modelling, we find that levels of transporters are history-dependent and are regulated by a push-pull system comprising two types of repressors. Repression by these two types varies with glucose in opposite ways, and not only matches the expression of transporters by their affinity to a range of glucose concentrations, but also the expression of some to how glucose is changing. We argue that matching is favoured by a rate-affinity trade-off and that the regulatory system allows yeast to import glucose rapidly enough to starve competitors. Matching expression to a pattern of input is fundamental, and we believe that push-pull repression is widespread.

systems biology↗

bioRxiv2021

Growth-Rate Dependent And Nutrient-Specific Gene Expression Resource Allocation In Fission Yeast

Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Quantitative studies using various growth conditions have singled out growth rate as a major physiological variable explaining relative protein abundances. Here, we used the simple eukaryote Schizosaccharomyces pombe to determine the importance of growth rate in explaining relative changes in protein and mRNA levels during growth on a series of non-limiting nitrogen sources. Although half of fission yeast genes were significantly correlated with the growth rate, this came alongside wide-spread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production with the notable exception of RNA polymerase II, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, metabolic enzymes, which represent [~]55-70% of the proteome by mass, showed mainly condition-specific regulation. Specifically, many enzymes involved in glycolysis and NAD-dependent metabolism as well as the fermentative and respiratory pathways were condition-dependent and not consistently correlated with growth. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.

systems biology↗

bioRxiv2020

Coordination of gene expression noise with cell size: extrinsic noise versus agent-based models of growing cell populations

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation - including static extrinsic noise - exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalises concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

systems biology↗

bioRxiv2019

Epimutations driven by small RNAs arise frequently but have limited duration in a metazoan organism

Epigenetic regulation involves changes in gene expression independent of DNA sequence variation that are inherited through cell division (Holliday, 2006). In addition to a fundamental role in cell differentiation, some epigenetic changes can also be transmitted transgenerationally through meiosis (Heard and Martienssen, 2014). Epigenetic alterations ("epimutations") could thus contribute to heritable variation within populations and be subject to evolutionary processes such as natural selection and drift (Burggren, 2016). However, this suggestion is controversial, partly because unlike classical mutations involving DNA sequence changes, key parameters such as the rate at which epimutations arise and their persistence are unknown. Here, we perform the first genome-wide study of epimutations in a metazoan organism. We use experimental evolution to characterise the rate, spectrum and stability of epimutations driven by small silencing RNAs in the model nematode C. elegans. We show that epimutations arise spontaneously at a rate [~]25 times greater than DNA sequence changes and typically have short half-lives of 2-3 generations. Nevertheless, some epimutations last at least 10 generations. Epimutations thus may contribute to evolutionary processes over a short timescale but are unlikely to bring about long-term divergence without further DNA sequence changes.

genetics↗

bioRxiv2019

Integrating multi-OMICS data through sparse Canonical Correlation Analysis for predicting complex traits: A comparative study

MotivationRecent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that by integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. As OMICS datasets are heterogeneous and high-dimensional (p >> n) integrating them can be done through Sparse Canonical Correlation Analysis (sCCA) that penalises the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sCCA have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets. ResultsThrough a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al. [2009], penalised matrix decomposition CCA proposed by Witten and Tibshirani [2009] and its extension proposed by Suo et al. [2017]. The aferomentioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets. Contacttr1915@ic.ac.uk

genomics↗

bioRxiv2019

Size-dependent increase in RNA Polymerase II initiation rates mediates gene expression scaling with cell size

Cell size varies during the cell cycle and in response to external stimuli. This requires the tight coordination, or \"scaling\", of mRNA and protein quantities with the cell volume in order to maintain biomolecules concentrations and cell density. Evidence in cell populations and single cells indicates that scaling relies on the coordination of mRNA transcription rates with cell size. Here we use a combination of single-molecule fluorescence in situ hybridisation (smFISH), time-lapse microscopy and mathematical modelling in single fission yeast cells to uncover the precise molecular mechanisms that control transcription rates scaling with cell size. Linear scaling of mRNA quantities is apparent in single fission yeast cells during a normal cell cycle. Transcription rates of both constitutive and regulated genes scale with cell size without evidence for transcriptional bursting. Modelling and experimental data indicate that scaling relies on the coordination of RNAPII transcription initiation rates with cell size and that RNAPII is a limiting factor. We show using real-time quantitative imaging that size increase is accompanied by a rapid concentration independent recruitment of RNAPII onto chromatin. Finally, we find that in multinucleated cells, scaling is set at the level of single nuclei and not the entire cell, making the nucleus the transcriptional scaling unit. Integrating our observations in a mechanistic model of RNAPII mediated transcription, we propose that scaling of gene expression with cell size is the consequence of competition between genes for limiting RNAPII.

molecular biology↗