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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

An Enhanced Variant-Aware Deep Learning Model for Individual Gene Expression Prediction

Accurate prediction of gene expression from individual whole-genome sequences is critical for understanding disease mechanisms and advancing precision medicine. Current methods, however, struggle with individual-specific genetic variations and integrating detailed sequence context. To address this, we introduce GenomicVariExpress (GVE), a novel deep learning model that leverages a pre-trained sequence encoder and incorporates an Enhanced Variant Integration Module (EVIM). EVIM explicitly encodes and fuses multi-dimensional variant features, such as type, allele frequency, and predicted functional impact, enabling GVE to precisely capture how individual variations modulate gene expression. We evaluate GVE using paired whole-genome and RNA sequencing data from the GTEx Whole Blood cohort. Our experiments demonstrate GVE consistently achieves superior performance compared to state-of-the-art baselines. An ablation study confirms EVIMs critical contribution to this improved performance. Furthermore, analyses highlight GVEs enhanced biological interpretability and its superior performance across multiple tissues and for genes influenced by rare variants. GVE represents a significant step towards accurate, individual-level gene expression prediction, offering a powerful tool for genomic function research and personalized healthcare applications.

bioinformatics↗

bioRxiv2022

PAF1 modulates innate immunity by both activation and repression of gene expression.

BackgroundSitting at the interface of gene expression and host-pathogen interaction, polymerase associated factor 1 complex (PAF1C) is a rising player in the innate immune response. The complex localizes to the nucleus and associates with chromatin to modulate RNA polymerase II (RNAPII) elongation of gene transcripts. Performing this function at both proximal and distal regulatory elements, PAF1C interacts with many host factors across such sites, along with several microbial proteins during infection. Therefore, translating the ubiquity of PAF1C into specific impacts on immune gene expression remains especially relevant. ResultsAdvancing past work, we treat PAF1 knockout cells with a slate of immune stimuli to identify key trends in PAF1-dependent gene expression with broad analytical depth. From our transcriptomic data, we confirm PAF1 is an activator of traditional immune response pathways as well as other cellular pathways correlated with pathogen defense. With this model, we employ computational approaches to refine how PAF1 may contribute to both gene activation and suppression. Specifically focusing on transcriptional motifs, we predict gene regulatory elements strongly associated with PAF1, including those implicated in an immune response. Overall, our results suggest PAF1 is potentially involved in innate immunity at several distinct axes of regulation. ConclusionsBy identifying PAF1-dependent gene expression across several pathogenic contexts, we confirm PAF1C to be a key mediator of innate immunity. Combining these transcriptomic profiles with potential regulatory networks corroborates the previously identified functions of PAF1C. With this, we foster new avenues for its study as a regulator of innate immunity, and our results will serve as a basis for targeted study of PAF1C in future validation studies.

molecular biology↗

bioRxiv2019

Enhancers predominantly regulate gene expression in vivo via transcription initiation.

Gene transcription occurs via a cycle of linked events including initiation, promoter proximal pausing and elongation of RNA polymerase II (Pol II). A key question is how do transcriptional enhancers influence these events to control gene expression? Here we have used a new approach to quantify transcriptional initiation and pausing in vivo, while simultaneously identifying transcription start sites (TSSs) and pause-sites (TPSs) from single RNA molecules. When analyzed in parallel with nascent RNA-seq, these data show that differential gene expression is achieved predominantly via changes in transcription initiation rather than Pol II pausing. Using genetically engineered mouse models deleted for specific enhancers we show that these elements control gene expression via Pol II recruitment and/or initiation rather than via promoter proximal pause release. Together, our data show that enhancers, in general, control gene expression predominantly by Pol II recruitment and initiation rather than via pausing.

genomics↗

bioRxiv2020

Adversarial Deconfounding Autoencoder for Learning Robust Gene Expression Embeddings

MotivationIncreasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in large numbers, inherently contain variations introduced by technical artifacts (e.g., batch effects) and uninteresting biological variables (e.g., age) in addition to the true signals of interest. These sources of variations, called confounders, produce embeddings that fail to transfer to different domains, i.e., an embedding learned from one dataset with a specific confounder distribution does not generalize to different distributions. To remedy this problem, we attempt to disentangle confounders from true signals to generate biologically informative embeddings. ResultsIn this paper, we introduce the AD-AE (Adversarial Deconfounding AutoEncoder) approach to deconfounding gene expression latent spaces. The AD-AE model consists of two neural networks: (i) an autoencoder to generate an embedding that can reconstruct original measurements, and (ii) an adversary trained to predict the confounder from that embedding. We jointly train the networks to generate embeddings that can encode as much information as possible without encoding any confounding signal. By applying AD-AE to two distinct gene expression datasets, we show that our model can (1) generate embeddings that do not encode confounder information, (2) conserve the biological signals present in the original space, and (3) generalize successfully across different confounder domains. We demonstrate that AD-AE outperforms standard autoencoder and other deconfounding approaches. AvailabilityOur code and data are available at https://gitlab.cs.washington.edu/abdincer/ad-ae. Contactabdincer@cs.washington.edu; suinlee@cs.washington.edu

bioinformatics↗

bioRxiv2022

Factors influencing patterns of gene expression in large bowel mucosa

AimVariation in genomic sequences that control gene expression in target tissues is increasingly recognized as the underlying basis of disease susceptibility, especially cancer. This is particularly relevant to common genetic variation impacting on cancer risk. Exogenous factors may also influence gene expression, potentially leaving a transcriptomic signature of environmental risk factors in normal tissues. Therefore, understanding endogenous and exogenous influences over gene expression patterns in normal tissue is critical to the study of functional genomics and its relationship to disease susceptibility. Here, we investigated demographic and sampling variables that could impact on gene expression in normal colorectal mucosa. MethodWe prospectively collected normal mucosa from 424 patients undergoing colorectal surgery or outpatient assessment through surgical stripping of normal mucosa from resected colorectal specimens or rectal biopsy. Gene expression was assessed using Illumina HT-12 microarrays and analysed against demographic (age, gender, BMI) and sampling factors (general anaesthesia, cleansing bowel agents, sample site, time to RNA preservation) using adjusted linear regression modelling. ResultsAge, gender, smoking status, sampling under anaesthetic, sample site and sampling method were associated with differential gene expression in the adjusted model. BMI or use of cleansing bowel preparation did not impact gene expression. Age was associated with differential expression of 16 genes and significant enrichment in pathways relevant to tumourigenesis, including immune process, cell proliferation, adhesion and death. Sample site was associated with differential expression of 2515 genes, with 1102 genes more highly expressed in the proximal colon (proximal to splenic flexure). Gender and sampling under anaesthetic were associated with differential expression of 99 and 851 genes respectively. Increased time to RNA preservation (45-90 minutes) was associated with enrichment of pathways consistent with tissue ischaemia including response to wounding, apoptotic process, and response to oxygen levels. ConclusionsDemographic and sampling factors influence gene expression patterns in normal colorectal mucosa, often with a large magnitude of effect. Meanwhile, greater time to RNA preservation is associated with patterns of gene expression consistent with tissue ischaemia which questions the generalisability of assessment of gene expression patterns generated from post-mortem studies. These results highlight the importance of fully adjusted expression analyses and may indicate mechanisms underlying age, gender and site-specific differences in CRC incidence, progression and outcome.

genomics↗

bioRxiv2021

Engineered endosymbionts capable of directing mammalian cell gene expression

Modular methods for directing mammalian gene expression would enable advances in tissue regeneration, enhance cell-based therapeutics and improve modulation of immune responses. To address this challenge, engineered endosymbionts (EES) that escape endosomal destruction, reside in the cytoplasm of mammalian cells, and secrete proteins that are transported to the nucleus to control host cell gene expression were developed. Microscopy confirmed that EES escape phagosomes, replicate within the cytoplasm, and can secrete reporter proteins into the cytoplasm that were then transported to the nucleus. Synthetic operons encoding the mammalian transcription factors, Stat-1 and Klf6 or Klf4 and Gata-3 were recombined into the EES genome. Using controlled induction, these EES were shown to direct gene expression in J774A.1 macrophage/monocyte cells and modulate the host cell fates. Expressing mammalian transcription factors from engineered intracellular bacteria as endosymbionts comprises a new tool for directing host cell gene expression for therapeutic and research purposes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/463266v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@2b5935org.highwire.dtl.DTLVardef@1edd8edorg.highwire.dtl.DTLVardef@3cb5b9org.highwire.dtl.DTLVardef@1be9b5f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2019

Gene expression analysis of Cyanophora paradoxa reveals conserved abiotic stress responses between basal algae and flowering plants

O_LIThe glaucophyte Cyanophora paradoxa represents the most basal member of the Archaeplastida kingdom, however the function and expression of most of its genes are unknown. This information is needed to uncover how functional gene modules, i.e. groups of genes performing a given function, evolved in the plant kingdom.\nC_LIO_LIWe have generated a gene expression atlas capturing responses of Cyanophora to various abiotic stresses. This data was included in the CoNekT-Plants database, enabling comparative transcriptomic analyses across two algae and six land plants.\nC_LIO_LIWe demonstrate how the database can be used to study gene expression, co-expression networks and gene function in Cyanophora, and how conserved transcriptional programs can be identified. We identified gene modules involved in phycobilisome biosynthesis, response to high light and cell division. While we observed no correlation between the number of differentially expressed genes and the impact on growth of Cyanophora, we found that the response to stress involves a conserved, kingdom-wide transcriptional reprogramming, which is activated upon most stresses in algae and land plants.\nC_LIO_LIThe Cyanophora stress gene expression atlas and the tools found in https://conekt.plant.tools/ database provide a useful resource to reveal functionally related genes and stress responses in the plant kingdom.\nC_LI

plant biology↗

bioRxiv2014

Epigenetic Modifications are Associated with Inter-species Gene Expression Variation in Primates

Changes in gene regulation level have long been thought to play an important role in evolution and speciation, especially in primates. Over the past decade, comparative genomic studies have revealed extensive inter-species differences in gene expression levels yet we know much less about the extent to which regulatory mechanisms differ between species. To begin addressing this gap, we performed a comparative epigenetic study in primate lymphoblastoid cell lines (LCLs), to query the contribution of RNA polymerase II (Pol II) and four histone modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) to inter-species variation in gene expression levels. We found that inter-species differences in mark enrichment near transcription start sites are significantly more often associated with inter-species differences in the corresponding gene expression level than expected by chance alone. Interestingly, we also found that first-order interactions among the histone marks and Pol II do not markedly contribute to the degree of association between the marks and inter-species variation in gene expression levels, suggesting that the marginal effects of the five marks dominate this contribution.

Genomics↗

bioRxiv2024

The Drosophila maternal-effect gene abnormal oocyte (ao) does not repress histone gene expression

The abnormal oocyte (ao) gene of Drosophila melanogaster is a maternal-effect lethal gene previously identified as encoding a transcriptional regulator of core histones. However, background genetic mutations in existing ao mutant strains could compromise their utility in manipulating histone levels. To distinguish the true ao phenotype from background effects, we created two new ao reagents: a CRISPR/Cas9-mediated knockout of the ao allele for genetic and molecular analyses and an epitope-tagged ao allele for cytological experiments. Using these reagents, we confirm previous findings that loss of ao causes maternal-effect lethality, which can be rescued by either a decrease in the histone gene copy number or by Y chromosome heterochromatin. Our data indicate that ao genetically interacts with the heterochromatin, as previously suggested. However, contrary to a prior study, we find neither Ao localization to histone genes nor ao repression of core histone transcript levels. Thus, the molecular basis for ao-associated maternal-effect lethality remains unknown. Article SummaryA series of foundational papers established that abnormal oocyte (ao), a euchromatic maternal-effect lethal gene, interacts with heterochromatin and the histone multigene cluster to dictate embryonic viability in D. melanogaster. An earlier report argued that ao encodes a protein that localizes to and represses histone gene expression, thereby connecting histone gene overexpression with ao mutant maternal-effect lethality. Using new reagents for genetics and cytology, we recapitulate findings that ao encodes a maternal-effect lethal gene, whose loss is ameliorated by excess heterochromatin or loss of histone genes. However, we find that ao does not affect histone gene expression. Thus, how ao loss causes maternal-effect lethality remains unknown.

genetics↗

bioRxiv2020

Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast

In addition to specific regulatory circuits, gene expression is also regulated by global physiological cues such as the cell growth rate and metabolic parameters. Here we examine these global control mechanisms by analyzing an orthogonal multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and intracellular amino acids in 22 steady-state yeast cultures. Our model indicates that transcript and protein abundance are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but are independently controlled by metabolic parameters relating to amino acid and nucleotide availability. Genes in central carbon metabolism, however, are regulated independently of these global physiological cues. Our findings can be used to augment gene expression profiling analyses in the distantly related yeast Schizosaccharomyces pombe and a human cancer cell model. Our results provide a framework to analyze gene expression profiles to gain novel biological insights, a key goal of systems biology.

systems biology↗

bioRxiv2024

When the tap runs dry: The multi-tissue gene expression and physiological responses of water deprived Peromyscus eremicus

The harsh and dry conditions of desert environments have resulted in genomic adaptations, allowing for desert organisms to withstand prolonged drought, extreme temperatures, and limited food resources. Here, we present a comprehensive exploration of gene expression across five tissues (kidney, liver, lung, gastrointestinal tract, and hypothalamus) and 19 phenotypic measurements to explore the whole-organism physiological and genomic response to water deprivation in the desert-adapted cactus mouse (Peromyscus eremicus). The findings encompass the identification of differentially expressed genes and correlative analysis between phenotypes and gene expression patterns across multiple tissues. Specifically, we found robust activation of the vasopressin renin-angiotensin-aldosterone system (RAAS) pathways, whose primary function is to manage water and solute balance. Animals reduce food intake during water deprivation, and upregulation of PCK1 highlights the adaptive response to reduced oral intake via its actions aimed at maintained serum glucose levels. Even with such responses to maintain water balance, hemoconcentration still occurred, prompting a protective downregulation of genes responsible for the production of clotting factors while simultaneously enhancing angiogenesis which is thought to maintains tissue perfusion. In this study, we elucidate the complex mechanisms involved in water balance in the desert-adapted cactus mouse, P. eremicus. By prioritizing a comprehensive analysis of whole-organism physiology and multi-tissue gene expression in a simulated desert environment, we describe the complex and successful response of regulatory processes.

genomics↗

bioRxiv2022

Brain and Organoid Manifold Alignment (BOMA), a machine learning framework for comparative gene expression analysis across brains and organoids

Organoids have become valuable models for understanding cellular and molecular mechanisms in human development including brains. However, whether developmental gene expression programs are preserved between human organoids and brains, especially in specific cell types, remains unclear. Importantly, there is a lack of effective computational approaches for comparative data analyses between organoids and developing humans. To address this, by considering the public data availability and research significance, we developed a machine learning framework, Brain and Organoid Manifold Alignment (BOMA) for comparative gene expression analysis of brains and organoids, to identify conserved and specific developmental trajectories as well as developmentally expressed genes and functions, especially at cellular resolution. BOMA first performs a global alignment and then uses manifold learning to locally refine the alignment, revealing conserved developmental trajectories between brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental scRNA-seq data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes using immunofluorescence. BOMA is open-source available as a web tool for general community use.

bioinformatics↗

bioRxiv2019

Critical role of rhythmic poly(A) tail regulation in circadian gene expression

The mammalian circadian clock is deeply rooted in rhythmic regulation of gene expression. Rhythmic transcriptional control mediated by the circadian transcription factors is thought to be the main driver of mammalian circadian gene expression. However, mounting evidence has demonstrated the importance of rhythmic post-transcriptional controls, and it remains unclear how the transcriptional and post-transcriptional mechanisms collectively control rhythmic gene expression. A recent study discovered rhythmicity in poly(A) tail length in mouse liver and its strong correlation with protein expression rhythms. To understand the role of rhythmic poly(A) regulation in circadian gene expression, we constructed a parsimonious model that depicts rhythmic control imposed upon basic mRNA expression and poly(A) regulation processes, including transcription, deadenylation, polyadenylation, and degradation. The model results reveal the rhythmicity in deadenylation as the strongest contributor to the rhythmicity in poly(A) tail length and the rhythmicity in the abundance of the mRNA subpopulation with long poly(A) tails (a rough proxy for mRNA translatability). In line with this finding, the model further shows that the experimentally observed distinct peak phases in the expression of deadenylases, regardless of other rhythmic controls, can robustly group the rhythmic mRNAs by their peak phases in poly(A) tail length and in abundance of the subpopulation with long poly(A) tails. This provides a potential mechanism to synchronize the phases of target gene expression regulated by the same deadenylases. Our findings highlight the critical role of rhythmic deadenylation in regulating poly(A) rhythms and circadian gene expression. Author SummaryThe biological circadian clock regulates various bodily functions such that they anticipate and respond to the day-and-night cycle. To achieve this, the circadian clock controls rhythmic gene expression, and these genes ultimately drive the rhythmicity of downstream biological processes. As a mechanism of driving circadian gene expression, rhythmic transcriptional control has attracted the central focus. However, mounting evidence has also demonstrated the importance of rhythmic post-transcriptional controls. Here we use mathematical modeling to investigate how transcriptional and post-transcriptional rhythms coordinately control rhythmic gene expression. We have particularly focused on rhythmic regulation of the length of poly(A) tail, a nearly universal feature of mRNAs that controls mRNA stability and translation. Our model reveals that the rhythmicity of deadenylation, the process that shortens the poly(A) tail, is the dominant contributor to the rhythmicity in poly(A) tail length and mRNA translatability. Particularly, the phase of deadenylation nearly overrides the other rhythmic processes in controlling the phases of poly(A) tail length and mRNA translatability. Our finding highlights the critical role of rhythmic deadenylation in circadian gene expression control.

systems biology↗

bioRxiv2026

Monitoring Gene Expression in Retina with Synthetic Serum Markers

Gene expression underlies retinal development, function, and pathogenesis. However, monitoring retinal gene expression in vivo is challenging. In this study, we apply synthetic serum markers, called Released Markers of Activity, or RMAs, to quantify gene expression in an intact retina through a simple blood test. We show that RMAs can quantify transduction of multiple retinal cell-types and monitor cell implantation or cell loss. We found RMAs are sensitive enough to measure transduction of rare cell populations, such as retinal ganglion cells and could detect transplantation of as few as 100 stem cells. Expression of RMAs showed no evidence of cell loss or immune activation in the retina. Overall, these findings demonstrate that RMAs are highly sensitive, tissue non-destructive gene expression reporters that can continuously track gene delivery and cell survival in intact retina through a simple blood test.

bioengineering↗

bioRxiv2025

Single-nucleus transcriptomics reveals time-dependent and cell-type-specific effects of psilocybin on gene expression

There is growing interest to investigate classic psychedelics and ketamine as therapeutics for mental illnesses. Previous studies have demonstrated that one dose of psilocybin or ketamine leads to persisting neural and behavioral changes. The durability of these effects suggests that there are likely alterations in gene expression at the transcriptional level. In this study, we performed single-nucleus RNA sequencing of the dorsal medial frontal cortex of male and female mice. Samples were collected at 1, 2, 4, 24, or 72 hours after psilocybin or ketamine administration and from control animals. At baseline, major subtypes of excitatory and GABAergic neurons selectively express particular serotonin receptor transcripts. The psilocybin-evoked differentially expressed genes in excitatory neurons are involved in synaptic plasticity, distinct from genes enriched in GABAergic neurons, which contribute to mitochondrial function and cellular metabolism, and non-neuronal glial cells. The effect of psilocybin on gene expression is time-dependent, including an early phase at 1 hour followed by a late phase at 72 hours of transcriptional response after administration, and differs from the changes following ketamine administration, which peaks at 2 - 4 hours. Collectively, the results provide a resource for understanding the cell type-specific and time-dependent changes in gene expression induced by psilocybin and ketamine in the mouse medial frontal cortex, which may underpin the drugs long-term effects on neural circuits and behavior.

neuroscience↗

bioRxiv2020

Expression quantitative trait methylation analysis reveals methylomic associations with gene expression in childhood asthma

Nasal airway epithelial methylation profiles have been associated with asthma, but the effects of such profiles on expression of distant cis-genes are largely unknown. We identified 16,867 significant methylation-gene expression pairs in nasal epithelium from Puerto Rican children and adolescents (with and without asthma) in an expression quantitative trait methylation (eQTM) analysis of cis-genes located within 1 Mb of the methylation probes tested. Most eQTM methylation probes were distant from their target genes, and more likely located in enhancer regions of their target genes in lung tissue than control probes. The top 500 eQTM genes were enriched in pathways for immune processes and epithelial integrity, and also more likely to be differentially expressed in atopic asthma. Moreover, we identified 5,934 paths through which methylation probes could affect atopic asthma through gene expression. Our findings suggest that distant epigenetic regulation of gene expression in airway epithelium plays a role in atopic asthma.

genomics↗

bioRxiv2018

Elevated de novo fatty acid biosynthesis gene expression promotes melanoma cell survival and drug resistance

Elevated de novo fatty acid biosynthesis (DNFA) is a hallmark adaptation in many cancers that supports survival, proliferation, and metastasis. Here we elucidate previously unexplored aspects of transcription regulation and clinical relevance of DNFA in melanomas. We show that elevated expression of DNFA genes is characteristic of many tumor types and correlates with poor prognosis. Elevated DNFA gene expression depends on transcription factor SREBP1 in multiple melanoma cell lines. SREBP1 predominantly binds to the transcription start sites of DNFA genes, directly regulating transcription via RNA polymerase II recruitment and productive elongation. We find that SREBP1-regulated DNFA represents an intrinsic survival mechanism in melanoma cells, regardless of proliferative state and oncogenic mutation status. Indeed, malignant melanoma cells exhibit elevated DNFA gene expression after pro-survival signaling pathways are blocked (e.g. by the BRAF inhibitor vemurafenib). Altogether, these results implicate SREBP1 and DNFA enzymes as enticing therapeutic targets in melanomas.

cancer biology↗

bioRxiv2025

Modeling synthetic serum marker kinetics for monitoring deep-tissue gene expression.

Serum markers could theoretically enable monitoring of gene expression dynamics with a simple blood draw. However, such markers are typically used to measure long-term changes, such as the progression of disease over multiple days or months. In this theoretical study, we determine the maximum theoretical temporal resolution and precision - the ability to distinguish rapid changes in gene expression and to obtain the true frequency of such changes, respectively. As a model of such processes, we used Released Markers of Activity (RMAs) - a class of synthetic serum markers that are expressed in neurons in the brain but transported into the blood. RMAs are orthogonal to physiological processes and have tunable levels, production rate, and onset time, providing well-defined data for serum marker production, tissue transport, and detection. We explore several scenarios where RMAs were used, including monitoring tissue transduction, changes in endogenous gene expression, and drug-induced marker expression. We demonstrate that the temporal resolution of monitoring primarily depends on the extrinsic noise level of the RMA signal and protein serum half-life. Additionally, we find that decreasing the serum half-life results in improved temporal precision at the cost of signal intensity. To enable broad use of this model, we developed a library and interface for running simulations to approximate marker trajectories to inform experimental design decisions and optimize marker detection. Author SummaryGene expression is a foundational driver of biological processes. We use synthetic serum markers that can report on gene expression with a blood draw. These synthetic markers have well-defined parameters and thus form a useful model for understanding the maximum theoretical temporal resolution and precision of monitoring gene expression. These two variables describe, respectively, the fastest changes in gene expression and how accurately the kinetics can be reflected through a serum marker measurement. Developing more robust reporter systems requires understanding how biological properties of these markers, such as serum half-life or measurement noise, affect their kinetics. Here, we developed computational models describing in vivo behavior of synthetic serum markers to identify the most critical parameters for optimization. We show that serum half-life and measurement noise are the main contributors to the temporal precision and resolution of monitoring. Additionally, we demonstrate the ability to predict marker levels in various contexts, including constitutive and drug-induced expression.

bioengineering↗