Hill function-based models of transcriptional switches: impact of specific, nonspecific, functional and nonfunctional binding

Rethinking transcription factor dynamics and transcription regulation in eukaryotes

Transcription factors (TFs) control gene expression by binding to specific DNA motifs in cis-regulatory elements. Cooperativity has been thought to ensure TF binding specificity. Recent research suggests that, at least in yeast, the role of cooperativity has probably been overemphasized. Consequently, synergy - the collective recruitment of the transcriptional machinery by TFs bound at multiple DNA sites - emerges as a more significant mechanism for achieving the specificity of the transcriptional response. Furthermore, I argue that the concentration of TFs within phase-separated nuclear condensates and their covalent modifications play an underappreciated but crucial role in sharpening transcriptional responses through complementary mechanisms. A model integrating cooperativity, synergy, post-translational modifications, and phase separation provides a comprehensive framework to explain dynamic, context-specific transcriptional responses in eukaryotes.

AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis

Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology, particularly in early mammalian development, where tissue-level differentiation arises from seemingly cell-autonomous mechanisms. In this study, we present a multi-scale, spatial-stochastic simulation framework for mouse embryogenesis, focusing on inner cell mass (ICM) differentiation into epiblast (EPI) and primitive endoderm (PRE) at the blastocyst stage. Our framework models key regulatory and tissue-scale interactions in a biophysically realistic fashion, capturing the inherent stochasticity of intracellular gene expression and intercellular signaling, while efficiently simulating these processes by advancing event-driven simulation techniques. Leveraging the power of Simulation-Based Inference (SBI) through the AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, we conduct a large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features of ICM specification. Our results reveal mechanistic insights into how the combined action of autocrine and paracrine FGF4 signaling coordinates stochastic gene expression at the cellular scale to achieve robust and reproducible ICM patterning at the tissue scale. We further demonstrate that the ICM exhibits a specific time window of sensitivity to exogenous FGF4, enabling lineage proportions to be adjusted based on timing and dosage, thereby extending current experimental findings and providing quantitative predictions for both mutant and wild-type ICM systems. Notably, FGF4 signaling not only ensures correct EPI-PRE lineage proportions but also enhances ICM resilience to perturbations, reducing fate-proportioning errors by 10-20% compared to a purely cell-autonomous system. Additionally, we uncover a surprising role for variability in intracellular initial conditions, showing that high gene-expression heterogeneity can improve both the accuracy and precision of cell-fate proportioning, which remains robust when fewer than 25% of the ICM population experiences perturbed initial conditions. Our work offers a comprehensive, spatial-stochastic description of the biochemical processes driving ICM differentiation and identifies the necessary conditions for its robust unfolding. It also provides a framework for future exploration of similar spatial-stochastic systems in developmental biology.

Unifying framework explaining how parental regulatory divergence can drive gene expression in hybrids and allopolyploids

Hybridization and polyploidy are powerful evolutionary forces, inducing a range of phenotypic outcomes, including non-additive expression, subgenome dominance, deviations in genomic dosage, and transcriptome downsizing. The reasons for these patterns and whether they are universal adaptive responses to genome merging and doubling remain debated. To address this, we develop a thermodynamic model of gene expression based on transcription factor (TF)-promoter binding. Applied to hybridization between species with divergent gene expression levels, cell volumes, or euchromatic ratios, this model distinguishes the effects of hybridization from those of polyploidy. Our results align with empirical observations, suggesting that gene regulation patterns in hybrids and polyploids often stem from the constrained interplay between inherited diverged regulatory networks rather than from subsequent adaptive evolution. In addition, occurrence of certain phenotypic traits depend on specific assumptions about promoter-TF coevolution and their distribution within the hybrid's nucleoplasm, offering new research avenues to understand the underlying mechanisms. In summary, our model explains how the legacy of divergent species directly influences the phenotypic traits of hybrids and allopolyploids.

scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.

Regulation of biofilm gene expression by DNA replication in Bacillus subtilis

Bacillus subtilis relies on biofilms for survival in harsh environments. Extracellular polymeric substance (EPS) is a crucial component of biofilms, yet the dynamics of EPS production in single cells remain elusive. To unveil the modulation of EPS synthesis, we built a minimal network model comprising the SinI-SinR-SlrR module, Spo0A, and EPS. Stochastic simulations revealed that antagonistic interplay between SinI and SinR enables EPS production in bursts. SlrR widens these bursts and increases their frequency by stabilizing SinR-SlrR complexes and depleting free SinR. DNA replication and chromosomal positioning of key genes dictate pulsatile changes in the slrR:sinR gene dosage ratio (gr) and Spo0A-P levels, each promoting EPS production in distinct phases of the cell cycle. As the cell cycle lengthens with nutrient stress, the duty cycle of gr pulsing decreases, whereas the amplitude of Spo0A-P pulses elevates. This coordinated response facilitates keeping a constant proportion of EPS-secreting cells within colonies across diverse nutrient conditions. Our results suggest that bacteria may 'encode' eps expression through strategic chromosomal organization. This work illuminates how stochastic protein interactions, gene copy number imbalance, and cell-cycle dynamics orchestrate EPS synthesis, offering a deeper understanding of biofilm formation.

Development of a whole-cell biosensor for ethylene oxide and ethylene

Ethylene and ethylene oxide are widely used in the chemical industry, and ethylene is also important for its role in fruit ripening. Better sensing systems would assist risk management of these chemicals. Here, we characterise the ethylene regulatory system in Mycobacterium strain NBB4 and use these genetic parts to create a biosensor. The regulatory genes etnR1 and etnR2 and cognate promoter Petn were combined with a fluorescent reporter gene (fuGFP) in a Mycobacterium shuttle vector to create plasmid pUS301-EtnR12P. Cultures of M. smegmatis mc2-155(pUS301-EtnR12P) gave a fluorescent signal in response to ethylene oxide with a detection limit of 0.2 μM (9 ppb). By combining the epoxide biosensor cells with another culture expressing the ethylene monooxygenase, the system was converted into an ethylene biosensor. The co-culture was capable of detecting ethylene emission from banana fruit. These are the first examples of whole-cell biosensors for epoxides or aliphatic alkenes. This work also resolves long-standing questions concerning the regulation of ethylene catabolism in bacteria.

Experimental and computational methods to highlight behavioural variations in TonB-dependent transporter expression in Pseudomonas aeruginosa versus siderophore concentration

Iron is a key nutrient for bacterial growth. The source can be either heme or siderophore-Fe complexes. Siderophores are small molecules synthesized by bacteria to scavenge iron from the bacterial environment. The pathogen Pseudomonas aeruginosa can express at least 15 different iron uptake pathways and all but one involve a TonB-dependent transporter (TBDT) for the uptake of iron across the outer membrane. Little is known about how bacteria modulate and adapt the expression of their different iron import pathways according to their environment. Here, we have developed fluorescent reporters between the promoter region of genes encoding a TBDT and the fluorescent reporter mCherry. With these constructs, we can follow the expression of TBDTs under different growth conditions. Mathematical modelling of the data obtained showed the transcription and expression of the gene encoding the TBDT PfeA to have a sigmoidal shape, whereas it was logarithmic for the TBDT gene foxA. Maximum transcription for pfeA was reached in the presence of 3 µM enterobactin, the siderophore recognized by PfeA, whereas the maximum was not reached for foxA with 100 µM nocardamine, the siderophore of FoxA.

Analysis of genetic dominance in the UK Biobank

Classical statistical genetics theory defines dominance as any deviation from a purely additive, or dosage, effect of a genotype on a trait, which is known as the dominance deviation. Dominance is well documented in plant and animal breeding. Outside of rare monogenic traits, however, evidence in humans is limited. We systematically examined common genetic variation across 1060 traits in a large population cohort (UK Biobank, N = 361,194 samples analyzed) for evidence of dominance effects. We then developed a computationally efficient method to rapidly assess the aggregate contribution of dominance deviations to heritability. Lastly, observing that dominance associations are inherently less correlated between sites at a genomic locus than their additive counterparts, we explored whether they may be leveraged to identify causal variants more confidently.

Understanding the Genome-Wide Transcription Response To Various cAMP Levels in Bacteria Using Phenomenological Models

Attempts to understand gene regulation by global transcription factors have largely been limited to expression studies under binary conditions of presence and absence of the transcription factor. Studies addressing genome-wide transcriptional responses to changing transcription factor concentration at high resolution are lacking. Here, we create a data set containing the entire Escherichia coli transcriptome in Luria-Bertani (LB) broth as it responds to 10 different cAMP concentrations spanning the biological range. We use the Hill's model to accurately summarize individual gene responses into three intuitively understandable parameters, Emax, n, and k, reflecting the sensitivity, nonlinearity, and midpoint of the dynamic range. Our data show that most cAMP-regulated genes have an n of >2, with their k values centered around the wild-type concentration of cAMP. Additionally, cAMP receptor protein (CRP) affinity to a promoter is correlated with Emax but not k, hinting that a high-affinity CRP promoter need not ensure transcriptional activation at lower cAMP concentrations and instead affects the magnitude of the response. Finally, genes belonging to different functional classes are tuned to have different k, n, and Emax values. We demonstrate that phenomenological models are a better alternative for studying gene expression trends than classical clustering methods, with the phenomenological constants providing greater insights into how genes are tuned in a regulatory network. IMPORTANCE Different genes may follow different trends in response to various transcription factor concentrations. In this study, we ask two questions: (i) what are the trends that different genes follow in response to changing transcription factor concentrations and (ii) what methods can be used to extract information from the gene trends so obtained. We demonstrate a method to analyze transcription factor concentration-dependent genome-wide expression data using phenomenological models. Conventional clustering methods and principal-component analysis (PCA) can be used to summarize trends in data but have limited interpretability. The use of phenomenological models greatly enhances the interpretability and thus utility of conventional clustering. Transformation of dose-response data into phenomenological constants opens up avenues to ask and answer many different kinds of question. We show that the phenomenological constants obtained from the model fits can be used to generate insights about network topology and allows integration of other experimental data such as chromatin immunoprecipitation sequencing (ChIP-seq) to understand the system in greater detail.

Emergent correlations in gene expression dynamics as footprints of resource competition

Genetic circuits need a cellular environment to operate in, which naturally couples the circuit function with the overall functionality of gene regulatory network. To execute their functions, all gene circuits draw resources in the form of RNA polymerases, ribosomes, and tRNAs. Recent experiments pointed out that the role of resource competition on synthetic circuit outputs could be immense. However, the effect of complexity of the circuit architecture on resource sharing dynamics is yet unexplored. In this paper, we employ mathematical modelling and in-silico experiments to identify the sources of resource trade-off and to quantify its impact on the function of a genetic circuit, keeping our focus on regulation of immediate downstream proteins, which are often used as protein read-outs. We show that estimating gene expression dynamics from readings of downstream protein data might be unreliable when the resource is limited and ribosome affinities are asymmetric. We focus on the impact of mRNA copy number and ribosome binding site (RBS) strength on the nonlinear isocline that emerges with two regimes, prominently separated by a tipping point, and study how correlation and competition dominate each other depending on various circuit parameters. Focusing further on genetic toggle circuit, we have identified major effects of resource competition in this model motif and quantified the observations. The observations are testable in wet-lab experiments, as all the parameters chosen are experimentally relevant.

The integrative biology of genetic dominance

Dominance is a basic property of inheritance systems describing the link between a diploid genotype at a single locus and the resulting phenotype. Models for the evolution of dominance have long been framed as an opposition between the irreconcilable views of Fisher in 1928 supporting the role of largely elusive dominance modifiers and Wright in 1929, who viewed dominance as an emerging property of the structure of enzymatic pathways. Recent theoretical and empirical advances however suggest that these opposing views can be reconciled, notably using models investigating the regulation of gene expression and developmental processes. In this more comprehensive framework, phenotypic dominance emerges from departures from linearity between any levels of integration in the genotype‐to‐phenotype map. Here, we review how these different models illuminate the emergence and evolution of dominance. We then detail recent empirical studies shedding new light on the diversity of molecular and physiological mechanisms underlying dominance and its evolution. By reconciling population genetics and functional biology, we hope our review will facilitate cross‐talk among research fields in the integrative study of dominance evolution.

Discrete-state stochastic kinetic models for target DNA search by proteins: Theory and experimental applications

To perform their functions, transcription factors and DNA-repair/modifying enzymes randomly search DNA in order to locate their specific targets on DNA. Discrete-state stochastic kinetic models have been developed to explain how the efficiency of the search process is influenced by the molecular properties of proteins and DNA as well as by other factors such as molecular crowding. These theoretical models not only offer explanations on the relation of microscopic processes to macroscopic behavior of proteins, but also facilitate the analysis and interpretation of experimental data. In this review article, we provide an overview on discrete-state stochastic kinetic models and explain how these models can be applied to experimental investigations using stopped-flow, single-molecule, nuclear magnetic resonance (NMR), and other biophysical and biochemical methods.

Of numbers and movement - understanding transcription factor pathogenesis by advanced microscopy

Transcription factors (TFs) are life-sustaining and, therefore, the subject of intensive research. By regulating gene expression, TFs control a plethora of developmental and physiological processes, and their abnormal function commonly leads to various developmental defects and diseases in humans. Normal TF function often depends on gene dosage, which can be altered by copy-number variation or loss-of-function mutations. This explains why TF haploinsufficiency (HI) can lead to disease. Since aberrant TF numbers frequently result in pathogenic abnormalities of gene expression, quantitative analyses of TFs are a priority in the field. In vitro single-molecule methodologies have significantly aided the identification of links between TF gene dosage and transcriptional outcomes. Additionally, advances in quantitative microscopy have contributed mechanistic insights into normal and aberrant TF function. However, to understand TF biology, TF-chromatin interactions must be characterised in vivo, in a tissue-specific manner and in the context of both normal and altered TF numbers. Here, we summarise the advanced microscopy methodologies most frequently used to link TF abundance to function and dissect the molecular mechanisms underlying TF HIs. Increased application of advanced single-molecule and super-resolution microscopy modalities will improve our understanding of how TF HIs drive disease.

Stochastic cell renewal process and lengthening of cell cycle

Evolution of the stem cell population responsible for homeostatic cell renewal processes is analyzed. We assume that this regime is the product of a delicate balance between symmetric divisions that, after each cell cycle, originates a new stem cell or its disappearance (through cell differentiation). This dynamics leads to a monoclonal population, that is for an initial homogeneous set of stem cells, fixation of each clone is equiprobable. In this work we show that if there is an altered stem cell with a longer cell cycle than the rest, the fixation of this altered clone is more likely. We also study the consequeces of the appearance of successive alterations with these characteristics and their fixations. This effect is purely due to inherent characteristics of the cell renewal dynamics and as time goes by it leads to a quiescence state for stem cells owing to the recurrent fixation of such altered cells. Therefore it would contribute to the aging process.

DNA Content, Cell Size, and Cell Senescence

A recent study assessed the impact of cell size on various cell properties. Oversized yeast cells display slow cell division, cytoplasmic dilution, and transcriptomic and proteomic alterations. It highlights commonalities between aging yeast and mammalian cells, suggesting the existence of a range of DNA content: cell volume ratio ensuring optimal function.

NMR-based investigations into target DNA search processes of proteins

To perform their function, transcription factors and DNA-repair/modifying enzymes must first locate their targets in the vast presence of nonspecific, but structurally similar sites on genomic DNA. Before reaching their targets, these proteins stochastically scan DNA and dynamically move from one site to another on DNA. Solution NMR spectroscopy provides unique atomic-level insights into the dynamic DNA-scanning processes, which are difficult to gain by any other experimental means. In this review, we provide an introductory overview on the NMR methods for the structural, dynamic, and kinetic investigations of target DNA search by proteins. We also discuss advantages and disadvantages of these NMR methods over other methods such as single-molecule techniques and biochemical approaches.

Logic of two antagonizing intra-species quorum sensing systems in bacteria

Bacteria release signaling molecules into the surrounding environment and sense them when present in their proximity. Using this strategy, a cell estimates the number of neighbors in its surrounding. Upon sensing a critical number of individuals, bacteria coordinate a number of cellular processes. This density-dependent control of gene expression and physiology is called quorum sensing (QS). Quorum sensing controls a wide variety of functions in bacteria, including those related to motility, growth, virulence etc. Quorum sensing has been widely observed in bacteria while the individuals of the same species or different species compete and cooperate each other. Interestingly, many species possess more than one QS system (intra-species) and these QS systems interact each other to perform quorum sensing. Thus, several logical arrangements can be possible based on the interaction among intra-species QS systems - parallel, series, antagonizing, and agonizing. In this work, we perform simulations to understand the logic of interaction between two antagonizing intra-species QS systems. In such an interaction, one QS system gets fully expressed and the other only gets partially expressed. This is found to be dictated by the interplay between autoinducer's diffusivity and antagonizing strength. In addition, we speculate an important role of the intracellular regulators (eg. LuxR) in maintaining the uniform response among the individual cells from the different localities. We also expect the interplay between the autoinducer's diffusivity and distribution of cells in fine tuning the collective response. Interestingly, in a localized niche with a heterogeneous cell distribution, the cells are expected to perform a global quorum sensing via fully expressed QS system and a local quorum sensing via partially expressed QS system.

Mechanisms of Mendelian dominance

Genetic dominance has long been considered as a qualitative reflection of interallelic interactions. Dominance arises from many multiple sources whose unifying theme is the existence of non-linear relationships between the genotypic and phenotypic values. One of the clearest examples are dominant negative mutations (DNMs) in which a defective subunit poisons a macromolecular complex. Dominance can also be due to the presence of a heterozygous null allele, as is the case of haploinsufficiency. Dominance can also be influenced by epistatic (interloci) interactions. For instance, a pre-existing genetic variant can make possible the expression of a pathogenic variant in a seemingly "dominant" fashion. Such interactions, which can make an individual more or less sensitive to a particular pathogenic variant, will also be discussed here.