SEARCH · Search bioRxiv

Results for “gene expression”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11Linked to original sources

bioRxiv2019

Diurnal.plant.tools: comparative transcriptomic and co-expression analyses of diurnal gene expression of the Archaeplastida kingdom

Almost all organisms coordinate some aspects of their biology through the diurnal cycle. Photosynthetic organisms, and plants especially, have established complex programs that coordinate physiological, metabolic and developmental processes with the changing light. The diurnal regulation of the underlying transcriptional processes is observed when groups of functionally related genes (gene modules) are expressed at a specific time of the day. However, studying the diurnal regulation of these gene modules in the plant kingdom was hampered by the large amount of data required for the analyses. To meet this need, we used gene expression data from 17 diurnal studies spanning the whole Archaeplastida kingdom (Plantae kingdom in the broad sense) to make an online diurnal database. We have equipped the database with tools that allow user-friendly cross-species comparisons of gene expression profiles, entire co-expression networks, co-expressed clusters (involved in specific biological processes), time-specific gene expression, and others. We exemplify how these tools can be used by studying three important biological questions: (i) the evolution of cell division, (ii) the diurnal control of gene modules in algae and (iii) the conservation of diurnally-controlled modules across species. The database is freely available at http://diurnal.plant.tools/.

plant biology↗

bioRxiv2020

Accurate transcription start sites enable mining for the cis-regulatory determinants of tissue specific gene expression

Across tissues, gene expression is regulated by a combination of determinants, including the binding of transcription factors (TFs), along with other aspects of cellular state. Recent studies emphasize the importance of both genetic and epigenetic states - TF binding sites and binding site chromatin accessibility have emerged as potentially causal determinants of tissue specificity. To investigate the relative contributions of these determinants, we constructed three genome-scale datasets for both root and shoot tissues of the same Arabidopsis thaliana plants: TSS-seq data to identify Transcription Start Sites, OC-seq data to identify regions of Open Chromatin, and RNA-seq data to assess gene expression levels. For genes that are differentially expressed between root and shoot, we constructed a machine learning model predicting tissue of expression from chromatin accessibility and TF binding information upstream of TSS locations. The resulting model was highly accurate (over 90% auROC and auPRC), and our analysis of model contributions (feature weights) strongly suggests that patterns of TF binding sites within [~]500 nt TSS-proximal regions are predominant explainers of tissue of expression in most cases. Thus, in plants, cis-regulatory control of tissue-specific gene expression appears to be primarily determined by TSS-proximal sequences, and rarely by distal enhancer-like accessible chromatin regions. This study highlights the exciting future possibility of a native TF site-based design process for the tissue-specific targeting of plant gene promoters.

genomics↗

bioRxiv2018

IL-33 regulates gene expression in intestinal epithelial cells independently of its nuclear localization

IL-33 is a cytokine found in the extracellular space (mature IL-33) or in the cell nucleus (full-length IL-33). Nuclear accumulation of IL-33 has been reported in intestinal epithelial cells (IEC) during intestinal inflammation and cancer, but a functional role for this nuclear form remains unclear. To study the role of nuclear IL-33 in IEC, we generated transgenic mice expressing full-length IL-33 in the intestinal epithelium (Vfl33 mice). Expression of full-length IL-33 in the epithelium resulted in accumulation of IL-33 protein in the nucleus and secretion of IL-33. Over-expression of full-length IL-33 by IEC did not promote gut inflammation, but induced expression of genes in the IEC and lamina propria lymphocytes (LPL) that correlated negatively with genes expressed in inflammatory bowel diseases (IBD). Because the IL-33 receptor ST2 is expressed by IEC, there was the potential that both the mature and full-length forms could mediate this effect. To specifically interrogate the transcriptional role of nuclear IL-33, we intercrossed the Vfl33 mice with ST2-deficient mice. ST2 deficiency completely abrogated the transcriptional effects elicited by IL-33 expression, suggesting that the transcriptional effects of IL-33 on IEC are mediated by its mature, not its nuclear form.\n\nHighlightsO_LIExpression of full-length IL-33 in the epithelium resulted in accumulation of IL-33 protein in the nucleus and secretion of IL-33.\nC_LIO_LIFull-length IL-33 induced differential gene expression in IEC and LPL that was negatively associated with intestinal inflammatory diseases\nC_LIO_LIIL-33 regulated gene expression in IEC via its extracellular (mature) form not via its nuclearform.\nC_LI

immunology↗

bioRxiv2017

Similarity identification in gene expression patterns as a new approach in phenotype classification

Stratifying healthy and malignant phenotypes and identifying their biological states using high-throughput molecular data has been the focus of many computational approaches during the last decade. Using multivariate changes in expression of genes within biological pathways, as fingerprints of complex phenotypes, we developed a new methodology for Similarity Identification in Gene expressioN (SIGN). In this approach, we use centroid classifier to identify phenotype of each biological sample. To obtain similarity of a given biological sample with classes of phenotypes, we defined a new distance measure, transcriptional similarity coefficient (TSC) which captures similarity of gene expression patterns between a biological pathway in two samples or populations. We showed that TSC, as an interpretable and stable distance measure in SIGN, captures all oncogenic hallmarks for breast cancer even with low sample size, by comparing healthy and patient tumor samples in the largest breast cancer dataset. In this study, we demonstrate that SIGN is a flexible, yet robust approach for classification based on transcriptomics data. Comparing early and late relapses within each molecular subtypes of breast cancer, our method enabled subtype-specific stratification of breast cancer patients into groups with significantly different survival. Moreover, we used SIGN to classify with more than 99% specificity the site of extraction of healthy and tumor samples from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets. We showed that SIGN also enables robust identification of hematopoietic stem cell and progenitors within the hematopoietic hierarchy. We further explored chemical perturbation data in the Connectivity Map (CMAP) database and showed that SIGN was able to classify seven classes of drugs based on their mechanism of action. In conclusion, we showed that SIGN can be used to achieve interpretable and robust transcriptomic-based classification of healthy and malignant samples, as well as drugs based on their known mechanism of action, supporting the generalizability and relevance of the method for the analysis of gene expression profiles.

bioinformatics↗

bioRxiv2022

Being noisy in a crowd: differential selective pressure on gene expression noise in model gene regulatory networks

Expression noise, the variability of the amount of gene product among isogenic cells grown in identical conditions, originates from the inherent stochasticity of diffusion and binding of the molecular players involved in transcription and translation. It has been shown that expression noise is an evolvable trait and that central genes exhibit less noise than peripheral genes in gene networks. A possible explanation for this pattern is increased selective pressure on central genes since they propagate their noise to downstream targets, leading to noise amplification. To test this hypothesis, we developed a new gene regulatory network model with inheritable stochastic gene expression and simulated the evolution of gene-specific expression noise under constraint at the network level. Stabilizing selection was imposed on the expression level of all genes in the network and rounds of mutation, selection, replication and recombination were performed. We observed that local network features affect both the probability to respond to selection, and the strength of the selective pressure acting on individual genes. In particular, the reduction of gene-specific expression noise as a response to stabilizing selection on the mean expression is higher in genes with higher centrality metrics. Furthermore, global topological structures such as network diameter, centralization and average degree affect the average expression variance and average selective pressure acting on constituent genes. Our results demonstrate that selection at the network level leads to differential selective pressure at the gene level, and local and global network characteristics are an essential component of gene-specific expression noise evolution.

evolutionary biology↗

bioRxiv2020

Gene expression in the cardiovascular system of the domestic sheep (Ovis aries); a new tool to advance our understanding of cardiovascular disease.

Large animal models are of increasing importance in cardiovascular disease research as they demonstrate more similar cardiovascular features (in terms of anatomy, physiology and size) to humans than do rodent species. The maintenance of a healthy cardiovascular system requires expression of genes that contribute to essential biological activities and repression of those that are associated with functions likely to be detrimental to cardiovascular homeostasis. In this study we have used the transcriptome of the sheep, which has been utilised extensively to model human physiology and disease, to explore genes implicated in the process of vascular calcification. Vascular calcification is a major disruption to cardiovascular homeostasis where tissues of the cardiovascular system undergo ectopic calcification and consequent dysfunction. We investigate the gene expression profiles of genes involved in vascular calcification in a wide array of cardiovascular tissues and across multiple developmental stages, using RT-qPCR. The majority of transcriptomic studies on the mammalian cardiovascular system to date have focused on regional expression of specific genes. Here we also use RNA sequencing results from the sheep heart and cardiac valves to further explore the transcriptome of the cardiovascular system in this large animal. Our results demonstrate that there is a balance between genes that promote and those that suppress mineralisation during development and across cardiovascular tissues. We show extensive expression of genes encoding proteins involved in formation and maintenance of the extracellular matrix in cardiovascular tissues, and high expression of haematopoietic genes in the cardiac valves. Our analysis will support future research into the functions of implicated genes in the development of vascular calcification, and increase the utility of the sheep as a large animal model for understanding cardiovascular disease. This study provides a foundation to explore the transcriptome of the developing cardiovascular system and is a valuable resource for the fields of mammalian genomics and cardiovascular research.

genomics↗

bioRxiv2023

A normalization method that controls for total RNA abundance affects the identification of differentially expressed genes, revealing bias toward morning-expressed responses

RNA-Sequencing is widely used to investigate changes in gene expression at the transcription level in plants. Most plant RNA-Seq analysis pipelines base the normalization approaches on the assumption that total transcript levels do not vary between samples. However, this assumption has not been demonstrated. In fact, many common experimental treatments and genetic alterations affect transcription efficiency or RNA stability, resulting in unequal transcript abundance. The addition of synthetic RNA controls is a simple correction that controls for variation in total mRNA levels. However, adding spike-ins appropriately is challenging with complex plant tissue, and carefully considering how they are added is essential to their successful use. We demonstrate that adding external RNA spike-ins as a normalization control produces differences in RNA-Seq analysis compared to traditional normalization methods, even between two times of day in untreated plants. We illustrate the use of RNA spike-ins with 3 RNA-Seq and present a normalization pipeline that accounts for differences in total transcriptional levels. We evaluate the effect of normalization methods on identifying differentially expressed genes in the context of identifying the effect of the time of day on gene expression and response to chilling stress in sorghum.

plant biology↗

bioRxiv2026

Sex-biased gene expression shapes sex differences in gene essentiality

Sex differences in disease incidence and progression are well documented, yet their underlying molecular mechanisms remain poorly understood. Multiple models suggest that baseline gene expression levels shape the impact of gene disruption, raising the possibility that sex-biased expression itself contributes to sex differences in cellular vulnerability. Here, we test this hypothesis by integrating sex-biased transcriptomic profiles with large-scale CRISPR loss-of-function screens to determine whether sex-biased expression predicts sex-biased gene essentiality across the genome. We find that gene expression level and sex chromosome dosage each explain a modest fraction of variance in essentiality, with substantially larger effects for sex chromosome genes than for autosomes. Across genes, sex effects on expression and essentiality are small in magnitude but directionally aligned, suggesting that sex differences in transcription can influence functional dependency. To resolve how these relationships arise, we applied gene-level mediation analyses to decompose sex effects on essentiality into expression-mediated and expression-independent components. This approach revealed multiple mechanistic architectures. On autosomes, most genes exhibited either sex-biased essentiality from direct sex effects (independent of expression) or sex-biased expression without functional consequence, while expression-mediated sex differences accounted for a smaller but substantial fraction of genes. In contrast, X chromosome genes were dominated by direct, expression-independent sex differences, consistent with strong effects of sex chromosome dosage, but also showed enrichment of expression-mediated architectures, particularly among X gametologs. Together, our results demonstrate that while sex-biased expression can generate sex-biased gene essentiality, this mechanism is not the default. Instead, sex-biased functional dependency is often driven by direct, expression-independent effects, particularly on the X chromosome, where dosage and compensatory mechanisms play a dominant role.

bioinformatics↗

bioRxiv2018

A systematic survey of human tissue-specific gene expression and splicing reveals new opportunities for therapeutic target identification and evaluation

Differences in the expression of genes and their splice isoforms across human tissues are fundamental factors to consider for therapeutic target evaluation. To this end, we conducted a transcriptome-wide survey of tissue-specific gene expression and splicing events in the unprecedented collection of 8527 high-quality RNA-seq samples from the GTEx project, covering 36 human peripheral tissues and 13 brain subregions. We derived a weighted tissue-specificity scoring scheme accounting for the similarity of related tissues and inherent variability across individual samples. We showed that ~50.6% of all annotated human genes show tissue-specific expression, including many low abundance transcripts vastly underestimated by previous array-based expression atlases. As utilities for drug discovery, we demonstrated that tissue-specificity is a highly desirable attribute of validated drug targets and tissue-specificity can be used to prioritize disease-associated genes from genome-wide association studies (GWAS). Using brain striatum-specific gene expression as an example, we provided a template to leverage tissue-specific gene expression to identify novel therapeutic targets. Mining of tissue-specific splicing further reveals new opportunities for tissue-specific targeting. Thus, the high quality transcriptome atlas provided by the GTEx is an invaluable resource for drug discovery and systematic analysis anchored on the human tissue specific gene expression provides a promising avenue to identify novel therapeutic target hypotheses.

genomics↗

bioRxiv2020

Epigenetic regulation of differentially expressed genes between various glioma types

Gliomas are the most frequent primary tumors of the central nervous system (CNS) and encompass two major subgroups: diffuse, malignant gliomas and benign, well differentiated gliomas showing a more circumscribed growth. Genome-wide next generation sequencing studies have uncovered specific genetic alterations, transcriptomic patterns and epigenetic profiles associated with different types of gliomas improving tumor diagnosis and having important implications for future clinical trials and patient management. We have recently created a unique resource encompassing genome-wide profiles of open chromatin, histone H3K27ac and H3Kme3 modifications, DNA methylation and transcriptomes of 33 glioma samples of different grades. Here, we took advantage of a wealth of data from those high-throughput experiments, intersected those data with topologically associating domains (TADs) and demonstrated that the chromatin organization and epigenetic landscape of enhancers have a strong impact on genes differentially expressed in low grade versus high grade gliomas. We identified TADs enriched in glioma grade-specific genes and/or epigenetic marks. We found a set of transcription factors, including REST, E2F1 and NFKB1, that are most likely to regulate gene expression in multiple TADs, containing glioma-related genes. Moreover, many genes associated with the cell-matrix adhesion Gene Ontology group, in particular 14 PROTOCADHERINs, were found to be regulated by the long range contacts with enhancers. Overall, the results presented here demonstrate the existence of epigenetic differences associated with chromatin organization driving differential gene expression in gliomas of different malignancy. We demonstrated that integration of whole genome epigenetic data with Hi-C data and transcriptomic profiles described in this work, can segregate low and high grade gliomas and reveal new regulatory networks that could explain some of the functional differences between gliomas of different malignancies. HighlightsO_LIIntegration of ATAC-seq, ChIP-seq and RNA-seq reveals glioma malignancy-related gene regulatory networks. C_LIO_LITADs segmentation contributes to gene-epigenetically modified enhancer relationships. C_LIO_LIContacts of active enhancers in gliomas of different malignancies might affect expression of genes involved in cancerogenesis, such as PROTOCADHERINs or EGFR. C_LI

cancer biology↗

bioRxiv2020

Comprehensive evaluation of human brain gene expression deconvolution methods

Gene expression measurements, similar to DNA methylation and proteomic measurements, are influenced by the cellular composition of the sample analysed. Deconvolution of bulk transcriptome data aims to estimate the cellular composition of a sample from its gene expression data, which in turn can be used to correct for composition differences across samples. Although a multitude of deconvolution methods have been developed, it is unclear whether their performance is consistent across tissues with different complexities of cellular composition. The human brain is unique in its transcriptomic diversity, expressing the highest diversity of alternative splicing isoforms and non-coding RNAs. It comprises a complex mixture of cell-types including transcriptionally similar sub-types of neurons, which undergo gene expression changes in response to neuronal activity. However, a comprehensive assessment of the accuracy of transcriptome deconvolution methods on human brain data is currently lacking. Here we carry out the first comprehensive comparative evaluation of the accuracy of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with transcriptome data from human pancreas and heart. We evaluate 8 transcriptome deconvolution approaches, covering all main classes: 4 partial deconvolution methods, each applied with 9 different cell-type signatures, 2 enrichment methods, and 2 complete deconvolution methods. We test the accuracy of cell-type estimates using in silico mixtures of single-cell RNA-seq data, mixtures of neuronal and glial RNA, as well as nearly 2,000 human brain samples. Our results bring several important insights into the performance of transcriptome deconvolution: (a) We find that cell-type signature data has a stronger impact on brain deconvolution accuracy than the choice of method. (b) We demonstrate that biological factors influencing brain cell-type signature data (e.g. brain region, in vitro cell culturing), have stronger effects on the deconvolution outcome than technical factors (e.g. RNA sequencing platform). (c) We find that partial deconvolution methods outperform complete deconvolution methods on human brain data. To facilitate wider implementation of correction for cellular composition, we develop a webtool that implements the best performing methods, and is available at https://voineagulab.shinyapps.io/BrainDeconvShiny/ .

genetics↗

bioRxiv2022

Characterizing the landscape of gene expression variance in humans

Gene expression variance has been linked to organismal function and fitness but remains a commonly ne-glected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease. Author SummaryGene expression variance, or the variation in the level of gene expression within a population, can have significant impacts on physiology, disease, and evolutionary adaptations. While the average level of gene expression is typically the focus of research, the variation around this average level (i.e., gene expression variance) can also be important for understanding complex traits and disease. Here, we investigate the landscape of transcriptional variance across tissues, populations, and studies. Using large publicly available RNA-seq data sets, we were able to identify the general properties associated with high- and low-variance genes, as well as factors driving variation in variance across genes. Specifically, we uncovered gene expression variance was significantly associated with gene length, nucleotide diversity, the degree of connectivity and the presence of non-coding RNA. Our results suggest that the mechanisms responsible for maintaining optimal levels of variation in high-versus low-variance differ, and that this variability is the result of different patterns of selection.

genomics↗

bioRxiv2015

Discrete Distributional Differential Expression (D3E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data

The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions. Here, we present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D3E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.

Bioinformatics↗

bioRxiv2019

A universal method for gene expression engineering

The precise expression of genes is one of the foundations of biotechnology. Here we present GeneEE, a straightforward method for generating artificial gene expression systems. We demonstrate that GeneEE segments, containing a 200 nucleotide DNA with random nucleotide composition, can facilitate constitutive and inducible gene expression. To highlight the universal character of our method, we demonstrate GeneEE-mediated gene and protein expression in six bacterial species and Bakers yeast.

bioengineering↗

bioRxiv2025

What is a differentially expressed gene?

The concept of Differentially Expressed Genes (DEGs) is central to RNA-Seq studies, yet their identification suffers from reproducibility issues. This is largely a consequence of the inherent biological and technical variation that cannot be captured with small numbers of replicates. When thresholds for p-values and log2 fold changes are introduced, this variability can propagate an incomplete description of the data, leading to differing interpretations. Here, we compare traditional binary DEG classification with a rank-based method, grounded in Bayesian statistics, using a published yeast dataset comprising over 40 replicates. This analysis reveals how the choice of thresholds and number of replicates results in discrepancies between studies and potentially interesting genes being overlooked. Furthermore, by comparing wild-type with wild-type samples, we show how variability in gene expression can be mistaken for differential expression. Evaluating current practices for navigating the accuracy-error trade-off in the search for differentially expressed genes leads us to advocate rank-based methods and Bayesian statistics to mitigate the limitations of binary classifications and communicate uncertainty.

bioinformatics↗

bioRxiv2025

Network properties constrain natural selection on gene expression in Caenorhabditis elegans

Gene regulatory networks (GRNs) integrate genetic and environmental signals to coordinate complex phenotypes and evolve through a balance of selection and drift. Using publicly available datasets from Caenorhabditis elegans, we investigated the extent of natural selection on transcript abundance by linking population-scale variation in gene expression to fecundity, a key fitness component. While the expression of most genes covaried only weakly with fitness, which is typical for polygenic traits, we identified seven transcripts under significant directional selection. These included nhr-114 and feh-1, implicating variation in nutrient-sensing and metabolic pathways as impacting fitness. Stronger directional selection on tissue-specific and older genes highlighted the germline and nervous system as focal points of adaptive change. Network position further constrained selection on gene expression; high-connectivity genes faced stronger stabilizing and directional selection, highlighting GRN architecture as a key factor in microevolutionary dynamics. The activity of transcription factors such as zip-3, which regulates mitochondrial stress responses, emerged as targets of selection, revealing potential links between energy homeostasis and fitness. Our findings demonstrate how GRNs mediate the interplay between selection and drift, shaping microevolutionary trajectories of gene expression and phenotypic diversity.

systems biology↗

bioRxiv2025

Effects of genomic location on ectopic integration and gene expression of a reporter gene cassette in Sulfolobus acidocaldarius

In eukaryotes and bacteria, it is well-established that the genomic location of ectopic gene integration influences the expression level due to replication-associated gene dosage effects as well as effects mediated by chromatin organization. In contrast, in archaea, the impact of genomic location on gene expression remained unexplored. Here, we investigated this impact in the model archaeon Sulfolobus acidocaldarius, a crenarchaeal species that has a chromatin architecture with mixed eukaryotic-like and bacterial-like features. We aimed to integrate a standardized {beta}-galactosidase (lacS) reporter cassette into diverse loci in the genome of S. acidocaldarius SK-1 for a comparative analysis. Nine integration mutant strains were successfully obtained, for which qRT-PCR analysis and lacS reporter gene assays revealed significant variation in transcriptional and translational expression of the reporter, respectively, demonstrating that genomic location strongly influences gene expression in S. acidocaldarius. However, variability in transcription levels and its regulation was shown to be primarily driven by transcriptional activity of neighboring genes, due to the high coding density in the S. acidocaldarius genome as well as a lack of insulator elements. Interestingly, translational activity exhibited a more apparent correlation with the distance to the closest origin of replication (R2 = 0.432) as compared to transcriptional activity (R2 = 0.026). In conclusion, this study not only provides insights into genome context effects, but also provides inspiration for the future design of genomic knock-in constructions in S. acidocaldarius.

microbiology↗

bioRxiv2018

Tuning gene expression variability and multi-gene regulation by dynamic transcription factor control

Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Using a fast-acting, light-responsive transcription factor in Saccharomyces cerevisiae, we show that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be precisely and independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to highly beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or complex construction of synthetic gene networks.

synthetic biology↗