Model Of Gene Regulation Research Articles

Interpreting deep neural networks for the prediction of translation rates

BackgroundThe 5’ untranslated region of mRNA strongly impacts the rate of translation initiation. A recent convolutional neural network (CNN) model accurately quantifies the relationship between massively parallel synthetic 5’ untranslated regions (5’UTRs) and translation levels. However, the underlying biological features, which drive model predictions, remain elusive. Uncovering sequence determinants predictive of translation output may allow us to develop a more detailed understanding of translation regulation at the 5’UTR.ResultsApplying model interpretation, we extract representations of regulatory logic from CNNs trained on synthetic and human 5’UTR reporter data. We reveal a complex interplay of regulatory sequence elements, such as initiation context and upstream open reading frames (uORFs) to influence model predictions. We show that models trained on synthetic data alone do not sufficiently explain translation regulation via the 5’UTR due to differences in the frequency of regulatory motifs compared to natural 5’UTRs.ConclusionsOur study demonstrates the significance of model interpretation in understanding model behavior, properties of experimental data and ultimately mRNA translation. By combining synthetic and human 5’UTR reporter data, we develop a model (OptMRL) which better captures the characteristics of human translation regulation. This approach provides a general strategy for building more successful sequence-based models of gene regulation, as it combines global sampling of random sequences with the subspace of naturally occurring sequences. Ultimately, this will enhance our understanding of 5’UTR sequences in disease and our ability to engineer translation output.

A community effort to optimize sequence-based deep learning models of gene regulation.

A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural networks but diverged in architectures and training strategies. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide models into modular building blocks. We tested all possible combinations for the top three models, further improving their performance. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets, demonstrating the progress that can be driven by gold-standard genomics datasets.

MicroRNAs provide negative feedback and stability in gene regulatory network models of cell-state transitions

The development of multicellular organisms occurs through a series of cell state transitions controlled by gene regulatory networks. Central to these networks are transcription factors (TFs) which bind enhancers and activate the expression of other genes, some of which are also TFs. Gene regulatory networks (GRN) connect TFs and enhancers in a nonlinear circuit capable of producing complex behavior such as bifurcations between stable cell states. Our dynamic network modelling of the Embryonic Stem Cell (ESC) to Definitive Endoderm (DE) transition requires an as yet unknown negative feedback mechanism for stability. Here, we show that cell state specific microRNAs (miRNAs) can provide this negative feedback by inactivating other cell lineage determining TFs (ESC or DE) during the transition. Our model provides a mechanism to maintain stable cell states without requiring a large set of cell-type-specific repressive TFs, of which there are fewer known examples than activators. In support of this model, we use computational models and analyze gene and miRNA expression and chromatin accessibility data from human cell lines to detect enhancers activating the miRNAs consistent with our network model. Our analysis highlights the interplay between TFs and miRNAs during ESC to DE transition and proposes a novel model for gene regulation.

Scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution.

Understanding how regulatory DNA elements shape gene expression across individual cells is a fundamental challenge in genomics. Joint RNA-seq and epigenomic profiling provides opportunities to build unifying models of gene regulation capturing sequence determinants across steps of gene expression. However, current models, developed primarily for bulk omics data, fail to capture the cellular heterogeneity and dynamic processes revealed by single-cell multi-modal technologies. Here, we introduce scooby, the first model to predict scRNA-seq coverage and scATAC-seq insertion profiles along the genome from sequence at single-cell resolution. For this, we leverage the pre-trained multi-omics profile predictor Borzoi as a foundation model, equip it with a cell-specific decoder, and fine-tune its sequence embeddings. Specifically, we condition the decoder on the cell position in a precomputed single-cell embedding resulting in strong generalization capability. Applied to a hematopoiesis dataset, scooby recapitulates cell-specific expression levels of held-out genes and cells, and identifies regulators and their putative target genes through in silico motif deletion. Moreover, accurate variant effect prediction with scooby allows for breaking down bulk eQTL effects into single-cell effects and delineating their impact on chromatin accessibility and gene expression. We anticipate scooby to aid unraveling the complexities of gene regulation at the resolution of individual cells.

Emergence of activation or repression in transcriptional control under a fixed molecular context.

For decades, studies have noted that transcription factors (TFs) can behave as either activators or repressors of different target genes. More recently, evidence suggests TFs can act on transcription simultaneously in positive and negative ways. Here we use biophysical models of gene regulation to define, conceptualize and explore these two aspects of TF action: "duality", where TFs can be overall both activators and repressors at the level of the transcriptional response, and "coherent and incoherent" modes of regulation, where TFs act mechanistically on a given target gene either as an activator or a repressor (coherent) or as both (incoherent). For incoherent TFs, the overall response depends on three kinds of features: the TF's mechanistic effects, the dynamics and effects of additional regulatory molecules or the transcriptional machinery, and the occupancy of the TF on DNA. Therefore, activation or repression can be tuned by just the TF-DNA binding affinity, or the number of TF binding sites, given an otherwise fixed molecular context. Moreover, incoherent TFs can cause non-monotonic transcriptional responses, increasing over a certain concentration range and decreasing outside the range, and we clarify the relationship between non-monotonicity and common assumptions of gene regulation models. Using the mammalian SP1 as a case study and well controlled, synthetically designed target sequences, we find experimental evidence for incoherent action and activation, repression or non-monotonicity tuned by affinity. Our work highlights the importance of moving from a TF-centric view to a systems view when reasoning about transcriptional control.

Gene modelling and annotation for the Hawaiian bobtail squid, Euprymna scolopes

Coleoid cephalopods possess numerous complex, species-specific morphological and behavioural adaptations, e.g., a uniquely structured nervous system that is the largest among the invertebrates. The Hawaiian bobtail squid (Euprymna scolopes) is one of the most established cephalopod species. With its recent publication of the chromosomal-scale genome assembly and regulatory genomic data, it also emerges as a key model for cephalopod gene regulation and evolution. However, the latest genome assembly has been lacking a native gene model set. Our manuscript describes the generation of new long-read transcriptomic data and, made using this combined with a plethora of publicly available transcriptomic and protein sequence data, a new reference annotation for E. scolopes.

Life under the snow: A year-round transcriptome analysis of Antarctic mosses in natural habitats provides insight into the molecular adaptation of plants under extreme environment.

Mosses are vital components of ecosystems, exhibiting remarkable adaptability across diverse habitats from deserts to polar ice caps. Sanionia uncinata (Hedw.) Loeske, a dominant Antarctic moss survives extreme environmental condition through perennial lifecycles involving growth and dormancy alternation. This study explores genetic controls and molecular mechanisms enabling S. uncinata to cope with seasonality of the Antarctic environment. We analysed the seasonal transcriptome dynamics of S. uncinata collected monthly from February 2015 to January 2016 in King George Island, Antarctica. Findings indicate that genes involved in plant growth were predominantly upregulated in Antarctic summer, while those associated with protein synthesis and cell cycle showed marked expression during the winter-to-summer transition. Genes implicated in cellular stress and abscisic acid signalling were highly expressed in winter. Further, validation included a comparison of the Antarctic field transcriptome data with controlled environment simulation of Antarctic summer and winter temperatures, which revealed consistent gene expression patterns in both datasets. This proposes a seasonal gene regulatory model of S. uncinate to understand moss adaptation to extreme environments. Additionally, this data set is a valuable resource for predicting genetic responses to climatic fluctuations, enhancing our knowledge of Antarctic flora's resilience to global climate change.

Optimal Reconstruction of Probabilistic Boolean Networks.

In gene regulatory networks (GRNs), it is important to model gene regulation based on a priori information and experimental data. As a useful mathematical model, probabilistic Boolean networks (PBNs) have been widely applied in GRNs. This article addresses the optimal reconstruction problem of PBNs based on several priori Boolean functions and sampled data. When all candidate Boolean functions are known in advance, the optimal reconstruction problem is reformulated into an optimization problem. This problem can be well solved by a recurrent neural network approach which decreases the computational cost. When parts of candidate Boolean functions are known in advance, necessary and sufficient conditions are provided for the reconstruction of PBNs. In this case, two types of reconstruction problems are further proposed: one is aimed at minimizing the number of reconstructed Boolean functions, and the other one is aimed at maximizing the selection probability of the main dynamics under noises. At last, examples in GRNs are elaborated to demonstrate the effectiveness of the main results.

Multi-Omics Analysis Reveals Intricate Gene Networks Involved in Female Development in Melon.

Sexual differentiation is an important developmental phenomenon in cucurbits that directly affects fruit yield. The natural existence of multiple flower types in melon offers an inclusive structure for studying the molecular basis of sexual differentiation. The current study aimed to identify and characterize the molecular network involved in sex determination and female development in melon. Male and female pools separated by the F2 segregated generation were used for sequencing. The comparative multi-omics data revealed 551 DAPs and 594 DEGs involved in multiple pathways of melon growth and development, and based on functional annotation and enrichment analysis, we summarized four biological process modules, including ethylene biosynthesis, flower organ development, plant hormone signaling, and ubiquitinated protein metabolism, that are related to female development. Furthermore, the detailed analysis of the female developmental regulatory pathway model of ethylene biosynthesis, signal transduction, and target gene regulation identified some important candidates that might have a crucial role in female development. Two CMTs ((cytosine-5)-methyltransferase), one AdoHS (adenosylhomocysteinase), four ACSs (1-aminocyclopropane-1-carboxylic acid synthase), three ACOs (ACC oxidase), two ARFs (auxin response factor), four ARPs (auxin-responsive protein), and six ERFs (Ethylene responsive factor) were identified based on various female developmental regulatory models. Our data offer new and valuable insights into female development and hold the potential to offer a deeper comprehension of sex differentiation mechanisms in melon.

OR35-03 Uncovering Causative Mutations in Polycystic Ovary Syndrome

Abstract Disclosure: J. Srivastava: None. I. Ovcharenko: None. PCOS is a complex disorder manifesting as reproductive and metabolic abnormalities in women. The undefined etiology stems from our incomplete understanding of genetic aberrations contributing to the diseased phenotype. This has resulted in speculations surrounding the cause-consequence relationship between phenotypic features of PCOS and limited our knowledge of comprehensive genetic signatures of PCOS. Pleiotropic effects are salient features of genetic regulation in mammalian genomes. The contribution of a set of regulatory elements (REs) to upregulation of a single gene, or the genetic control of multigene loci by a single RE, give rise to heterogenous phenotypes. Enhancers in particular dictate the spatio-temporal scale of gene expression by controlling transcription factor (TF) binding events. The complex traits of PCOS are suggestive of pleiotropy originating from genetic and epigenetic changes that arise from nucleotide variations in REs of the genome. Genome Wide Association Studies (GWAS) have identified ∼100 risk variants associated with PCOS and implicated the involvement of several genes such as FSHB/R, THADA, DENND1A, etc. However, mechanisms by which they contribute to pathophysiology are unknown. Additionally, twin studies have implicated the heritability of PCOS to be ∼80% and yet, the heritability from GWAS variants accounts for less than 10%. Experimental data may account for the ‘missing heritability problem’ specifically through rare variants that are missed in GWAS. To address this issue, we developed a Deep Learning model of gene regulation in PCOS identifying hundreds of active REs orchestrating the activity of PCOS genes. We also established a foundation for quantifying the impact of single-nucleotide noncoding mutations on TF binding and RE activity. By combining thousands of experimental assays associated with a large number of biological events such as tissue-specific epigenetic maps of the human genome, multi-tissue profiling of TF binding and gene expression using data from high throughput experiments we devised a strategy to accurately identify functionally map causative mutations in the GWAS loci of PCOS genes. MPRA experimental data validates a large fraction of our predicted causative variants thus suggesting that our studies have a potential of a leap forward accurate dissection of genomic and epigenetic mechanisms of PCOS. Consolidated results of this analysis and experimental validation of our predictions will lead to detailing the regulatory mechanisms of PCOS. Presentation Date: Sunday, June 18, 2023

NG-SEM: an effective non-Gaussian structural equation modeling framework for gene regulatory network inference from single-cell RNA-seq data.

Inference of gene regulatory network (GRN) from gene expression profiles has been a central problem in systems biology and bioinformatics in the past decades. The tremendous emergency of single-cell RNA sequencing (scRNA-seq) data brings new opportunities and challenges for GRN inference: the extensive dropouts and complicated noise structure may also degrade the performance of contemporary gene regulatory models. Thus, there is an urgent need to develop more accurate methods for gene regulatory network inference in single-cell data while considering the noise structure at the same time. In this paper, we extend the traditional structural equation modeling (SEM) framework by considering a flexible noise modeling strategy, namely we use the Gaussian mixtures to approximate the complex stochastic nature of a biological system, since the Gaussian mixture framework can be arguably served as a universal approximation for any continuous distributions. The proposed non-Gaussian SEM framework is called NG-SEM, which can be optimized by iteratively performing Expectation-Maximization algorithm and weighted least-squares method. Moreover, the Akaike Information Criteria is adopted to select the number of components of the Gaussian mixture. To probe the accuracy and stability of our proposed method, we design a comprehensive variate of control experiments to systematically investigate the performance of NG-SEM under various conditions, including simulations and real biological data sets. Results on synthetic data demonstrate that this strategy can improve the performance of traditional Gaussian SEM model and results on real biological data sets verify that NG-SEM outperforms other five state-of-the-art methods.

ACCELERATION OF BOOLEAN GENE REGULATORY NETWORKS ANALYSIS USING FPGA

Gene expression does not occur arbitrarily and spontaneously, it obeys certain patterns that can be expressed as a connected graph or network. The disclosure of these patterns requires a large amount of experimental research and accumulation of necessary statistical information. Then this information is subjected to mathematical processing, which involves significant computing resources and takes a lot of time. Boolean networks are often used as the basis for building mathematical models in those calculations. Recently, models based on Boolean networks have increasingly grown in size and complexity causing increased demands on traditional software solutions and computing tools. Field-programmable gate arrays (FPGAs) are a powerful and reconfigurable platform for implementing efficient and high-performance computing. The use of FPGA will significantly speed up the process of calculating sequential chains of gene states, both through the use of hardware acceleration in the calculation of logical dependencies, and through the implementation of an array of parallel computing cores, each of which can perform its own individual task. Another solution that can significantly simplify the work of researchers of gene regulation networks is the creation of a universal computing architecture that will allow dynamic reconfiguration of its internal structure when the task or logical dependencies for the current Boolean network change. Such a solution will relieve the researcher of the need to perform the entire set of actions for the technological preparation of a new FPGA configuration, from making changes to the HDL code that describes the network to uploading the updated configuration to the hardware accelerator. The article discusses how to use FPGA for the implementation and modeling of arbitrary Boolean networks, describes the concept of a universal reconfigurable architecture of a logical dependency calculating core for an arbitrary Boolean network and proposes a practical implementation of such a calculating core for modeling gene regulation networks.

The Brucella abortus two-component system response regulator BvrR binds to three DNA regulatory boxes in the upstream region of omp25.

Brucella abortus is a facultative extracellular-intracellular bacterial zoonotic pathogen worldwide. It is also a major cause of abortion in bovines, generating economic losses. The two-component regulatory system BvrR/BvrS modulates the expression of genes required to transition from extracellular to intracellular lifestyles. However, few regulatory regions of BvrR direct target genes have been studied. In this study, we characterized the regulatory region of omp25, a gene encoding an outer membrane protein that is positively regulated by TCS BvrR/BvrS. By omp25-lacZ reporter fusions and β-galactosidase activity assays, we found that the region between-262 and + 127 is necessary for transcriptional activity, particularly a 111-bp long fragment located from-262 to -152. In addition, we demonstrated the binding of P-BvrR to three sites within the -140 to +1 region. Two of these sites were delimited between -18 to +1 and - 99 to -76 by DNase I footprinting and called DNA regulatory boxes 1 and 2, respectively. The third binding site (box 3) was delimited from -140 to -122 by combining EMSA and fluorescence anisotropy results. A molecular docking analysis with HDOCK predicted BvrR-DNA interactions between 11, 13, and 12 amino acid residue-nucleotide pairs in boxes 1, 2, and 3, respectively. A manual sequence alignment of the three regulatory boxes revealed the presence of inverted and non-inverted repeats of five to eight nucleotides, partially matching DNA binding motifs previously described for BvrR. We propose that P-BvrR binds directly to up to three regulatory boxes and probably interacts with other transcription factors to regulate omp25 expression. This gene regulation model could apply to other BvrR target genes and to orthologs of the TCS BvrR/BvrS and Omp25 in phylogenetically closed Rhizobiales.

Assessing in vivo the impact of gene context on transcription through DNA supercoiling.

Gene context can have significant impact on gene expression but is currently not integrated in quantitative models of gene regulation despite known biophysical principles and quantitative in vitro measurements. Conceptually, the simplest gene context consists of a single gene framed by two topological barriers, known as the twin transcriptional-loop model, which illustrates the interplay between transcription and DNA supercoiling. In vivo, DNA supercoiling is additionally modulated by topoisomerases, whose modus operandi remains to be quantified. Here, we bridge the gap between theory and in vivo properties by realizing in Escherichia coli the twin transcriptional-loop model and by measuring how gene expression varies with promoters and distances to the topological barriers. We find that gene expression depends on the distance to the upstream barrier but not to the downstream barrier, with a promoter-dependent intensity. We rationalize these findings with a first-principle biophysical model of DNA transcription. Our results are explained if TopoI and gyrase both act specifically, respectively upstream and downstream of the gene, with antagonistic effects of TopoI, which can repress initiation while facilitating elongation. Altogether, our work sets the foundations for a systematic and quantitative description of the impact of gene context on gene regulation.

Widespread priming of transcriptional regulatory elements by incipient accessibility or RNA polymerase II pause in early embryos of the sea urchin Strongylocentrotus purpuratus.

Transcriptional regulatory elements (TREs) are the primary nodes that control developmental gene regulatory networks. In embryo stages, larvae, and adult differentiated red spherule cells of the sea urchin Strongylocentrotus purpuratus, transcriptionally engaged TREs are detected by Precision Run-On Sequencing (PRO-seq), which maps genome-wide at base pair resolution the location of paused or elongating RNA polymerase II (Pol II). In parallel, TRE accessibility is estimated by the Assay for Transposase-Accessible Chromatin using Sequencing (ATAC-seq). Our analysis identifies surprisingly early and widespread TRE accessibility in 4-cell cleavage embryos that is not necessarily followed by concurrent or subsequent transcription. TRE transcriptional differences identified by PRO-seq provide more contrast among embryonic stages than ATAC-seq accessibility differences, in agreement with the apparent excess of accessible but inactive TREs during embryogenesis. Global TRE accessibility reaches a maximum around the 20-hour late blastula stage, which coincides with the consolidation of major embryo regionalizations and peak histone variant H2A.Z expression. A transcriptional potency model based on labile nucleosome TRE occupancy driven by DNA sequences and the prevalence of histone variants is proposed in order to explain the basal accessibility of transcriptionally inactive TREs during embryogenesis. However, our results would not reconcile well with labile nucleosome models based on simple A/T sequence enrichment. In addition, a large number of distal TREs become transcriptionally disengaged during developmental progression, in support of an early Pol II paused model for developmental gene regulation that eventually resolves in transcriptional activation or silencing. Thus, developmental potency in early embryos may be facilitated by incipient accessibility and transcriptional pause at TREs.

Diversity and genetic architecture of agro-morphological traits in a core collection of European traditional tomato.

European traditional tomato varieties have been selected by farmers given their consistent performance and adaptation to local growing conditions. Here we developed a multipurpose core collection, comprising 226 accessions representative of the genotypic, phenotypic, and geographical diversity present in European traditional tomatoes, to investigate the basis of their phenotypic variation, gene×environment interactions, and stability for 33 agro-morphological traits. Comparison of the traditional varieties with a modern reference panel revealed that some traditional varieties displayed excellent agronomic performance and high trait stability, as good as or better than that of their modern counterparts. We conducted genome-wide association and genome-wide environment interaction studies and detected 141 quantitative trait loci (QTLs). Out of those, 47 QTLs were associated with the phenotype mean (meanQTLs), 41 with stability (stbQTLs), and 53 QTL-by-environment interactions (QTIs). Most QTLs displayed additive gene actions, with the exception of stbQTLs, which were mostly recessive and overdominant QTLs. Both common and specific loci controlled the phenotype mean and stability variation in traditional tomato; however, a larger proportion of specific QTLs was observed, indicating that the stability gene regulatory model is the predominant one. Developmental genes tended to map close to meanQTLs, while genes involved in stress response, hormone metabolism, and signalling were found within regions affecting stability. A total of 137 marker-trait associations for phenotypic means and stability were novel, and therefore our study enhances the understanding of the genetic basis of valuable agronomic traits and opens up a new avenue for an exploitation of the allelic diversity available within European traditional tomato germplasm.

Trap spaces of Boolean networks are conflict-free siphons of their Petri net encoding

Boolean network modeling of gene regulation but also of post-transcriptomic systems has proven over the years that it can bring powerful analyses and corresponding insight to the many cases where precise biological data is not sufficiently available to build a detailed quantitative model. Besides simulation, the analysis of such models is mostly based on attractor computation, since those correspond roughly to observable biological phenotypes. The recent use of trap spaces made a real breakthrough in that field allowing to consider medium-sized models that used to be out of reach. However, with the continuing increase in model size and complexity of Boolean update functions, the state-of-the-art computation of minimal trap spaces based on prime implicants shows its limits due to the difficulty of the prime-implicant computation.In this article we explore and prove for the first time a connection between trap spaces of a general Boolean network and siphons of its Petri net encoding. Besides important theoretical applications in studying properties of trap spaces, the connection enables us to propose an alternative approach to compute minimal trap spaces, and hence complex attractors, of a general Boolean network. It replaces the need for prime implicants by a completely different technique, namely the enumeration of maximal siphons in the Petri net encoding of the original model. We then demonstrate its efficiency and compare it to the state-of-the-art methods on a large collection of real-world and randomly generated models.

Characterization of cell-fate decision landscapes by estimating transcription factor dynamics

Time-specific modulation of gene expression during differentiation by transcription factors promotes cell diversity. However, estimating their dynamic regulatory activity at the single-cell level and in a high-throughput manner remains challenging. We present FateCompass, an integrative approach that utilizes single-cell transcriptomics data to identify lineage-specific transcription factors throughout differentiation. By combining a probabilistic framework with RNA velocities or differentiation potential, we estimate transition probabilities, while a linear model of gene regulation is employed to compute transcription factor activities. Considering dynamic changes and correlations of expression and activities, FateCompass identifies lineage-specific regulators. Our validation using in silico data and application to pancreatic endocrine cell differentiation datasets highlight both known and potentially novel lineage-specific regulators. Notably, we uncovered undescribed transcription factors of an enterochromaffin-like population during invitro differentiation toward ß-like cells. FateCompass provides a valuable framework for hypothesis generation, advancing our understanding of the gene regulatory networks driving cell-fate decisions.

Mimicking bacterial learning and memory in tungsten based two-sided single layers of WSeO, WSeS, WSeSe, and WSeTe

We mimic bacterial learning and memory abilities in tungsten based two-sided single layers of WSeO, WSeS, WSeSe, and WSeTe, where the thickness of the material represents the growth in time. We aim to create a quantum memristor like system to show learning and memory abilities of bacteria with time while it grows. Its governing equation is derived, and it was found to be similar to the gene regulatory response model of the bacteria. Polarization is calculated from the Berry phase theory to plot its relation with the degauss parameter in time, which leads to bow-tie like memory switches similar to phase-change memories. We attribute this behavior to a specific bacteria, that is, Geobacter metallireducens. Mimicking bacteria’s learning and memory abilities will open a way to merge physical intelligence with quantum computing computationally.

Modeling the regulation of the Il12b locus by NF-κB dimers

Abstract The transcription factor NF-κB is composed of a family, which regulates the expression of various genes in immune and inflammatory responses. The NF-κB family consists of five members: RelA/p65, RelB, c-Rel, p50/p105, and p52/p100, which control the transcription of target genes by binding to regulatory κB sites in homo-or heterodimer forms. Unlike the widely known RelA, the mechanisms related to the regulation of target genes by c-Rel is less understood. Interleukin 12b(Il12b), which encodes the p40 subunit of IL12 and IL23, is one example of gene that depend on c-Rel for proper activation in macrophages. The differences in intrinsic binding properties of c-Rel- and RelA-containing dimers at various regulatory sites around Il12b can be a key to understand the mechanism of c-Rel-containing dimers’ role in Il12b regulation. Using c-Rel and Il12b reporter mice with fluorescence live-cell imaging, we observe the single-cell dynamics of nuclear c-Rel and Il12b gene expression in bone marrow-derived macrophages induced by NF-κB activating ligands. We analyze the live-cell imaging data and extract quantitative features of nuclear c-Rel and Il12b expression from the time-series data. For additional mechanistic insights of the Il12b gene regulation by NF-κB dimers, we propose a mathematical model of dynamic gene regulation which includes actions of RelA- and c-Rel-containing dimers at κB sites proximal and distal to the Il12b locus. Our combined experimental and mathematical modeling approach will reveal quantitative insights that may provide a more comprehensive understanding of regulatory mechanisms underlying Il12b gene expression employed by NF-κB dimers.