Stochastic mechanisms in gene expression

Dynamics of bacterial operons during genome-wide stresses is influenced by premature terminations and internal promoters

Bacterial gene networks have operons, each coordinating several genes under a primary promoter. Half of the operons in Escherichia coli have been reported to also contain internal promoters. We studied their role during genome-wide stresses targeting key transcription regulators, RNA polymerase (RNAP) and gyrase. Our results suggest that operons' responses are influenced by stress-related changes in premature elongation terminations and internal promoters' activity. Globally, this causes the responses of genes in the same operon to differ with the distance between them in a wave-like pattern. Meanwhile, premature terminations are affected by positive supercoiling buildup, collisions between elongating and promoter-bound RNAPs, and local regulatory elements. We report similar findings in E. coli under other stresses and in evolutionarily distant bacteria Bacillus subtilis, Corynebacterium glutamicum, and Helicobacter pylori. Our results suggest that the strength, number, and positioning of operons' internal promoters might have evolved to compensate for premature terminations, providing distal genes similar response strengths.

Microcapillary Array-Based High Throughput Screening for Protein Biomanufacturability

Gene expression is a complex phenomenon involving numerous interlinked variables, and studying these variables to control expression is essential in bioengineering and biomanufacturing. While cloning techniques for achieving plasmid libraries that cover large design spaces exist, multiplex techniques offering cell culture screening at similar scales are still lacking. We introduced a microcapillary array-based platform aimed at high-throughput, multiplex screening of miniature cell cultures through fluorescent reporters. The clone recovery mechanism provides 100× enrichment ratios compared to traditional techniques for establishing phenotype-to-genotype linkages. We conducted experiments to delineate the effects of three key plasmid design features─promoters, 5' untranslated regions, and amino acid sequences─on protein titer. We identified a small set of promoters that maximize protein titer from thousands of promoters with widely varying transcription rates. We established that mRNA half-lives, controlled by 5' untranslated regions, correlate with protein expression. Using dual-reporter imaging, we demonstrate relative analyses of multiple ribosome binding sites in operons. Lastly, we discuss the effect of structural protein hydrophobicity scores on their expression and cell growth profiles. Through multiple experiments with libraries of plasmid constructs, we demonstrate population binning, dual-reporter operon screening, chemical perturbation, and cell growth estimation using brightfield absorbance measurements with the platform.

Comparative single-cell analysis of transcriptional bursting reveals the role of genome organization in de novo transcript origination

Spermatogenesis is a key developmental process underlying the origination of newly evolved genes. However, rapid cell type-specific transcriptomic divergence of the Drosophila germline has posed a significant technical barrier for comparative single-cell RNA-sequencing studies. By quantifying a surprisingly strong correlation between species- and cell type-specific divergence in three closely related Drosophila species, we apply a statistical procedure to identify a core set of 198 genes that are highly predictive of cell type identity while remaining robust to species-specific differences that span over 25 to 30 My of evolution. We then utilize cell type classifications based on the 198-gene set to show how transcriptional divergence in cell type increases throughout spermatogenic developmental time. After validating these cross-species cell type classifications using RNA fluorescence in situ hybridization and imaging, we then investigate the influence of genome organization on the molecular evolution of spermatogenesis vis-a-vis transcriptional bursting. We first show altering transcriptional burst size contributes to premeiotic transcription and altering bursting frequency contributes to postmeiotic expression. We then report global differences in autosomal vs. X chromosomal transcription may arise in a developmental stage preceding full testis organogenesis by showing evolutionarily conserved decreases in X-linked transcription bursting kinetics in all examined somatic and germline cell types. Finally, we provide evidence supporting the cultivator model of de novo gene origination by demonstrating how the appearance of newly evolved testis-specific transcripts potentially provides short-range regulation of neighboring genes' transcriptional bursting properties during key stages of spermatogenesis.

Moment-based parameter inference with error guarantees for stochastic reaction networks

Inferring parameters of biochemical kinetic models from single-cell data remains challenging because of the uncertainty arising from the intractability of the likelihood function of stochastic reaction networks. Such uncertainty falls beyond current error quantification measures, which focus on the effects of finite sample size and identifiability but lack theoretical guarantees when likelihood approximations are needed. Here, we propose a method for the inference of parameters of stochastic reaction networks that works for both steady-state and time-resolved data and is applicable to networks with non-linear and rational propensities. Our approach provides bounds on the parameters via convex optimization over sets constrained by moment equations and moment matrices by taking observations to form moment intervals, which are then used to constrain parameters through convex sets. The bounds on the parameters contain the true parameters under the condition that the moment intervals contain the true moments, thus providing uncertainty quantification and error guarantees. Our approach does not need to predict moments and distributions for given parameters (i.e., it avoids solving or simulating the forward problem) and hence circumvents intractable likelihood computations or computationally expensive simulations. We demonstrate its use for uncertainty quantification, data integration, and prediction of latent species statistics through synthetic data from common non-linear biochemical models including the Schlögl model and the toggle switch, a model of post-transcriptional regulation at steady state, and a birth-death model with time-dependent data.

Incorporating spatial diffusion into models of bursty stochastic transcription

The dynamics of gene expression are stochastic and spatial at the molecular scale, with messenger RNA (mRNA) transcribed at specific nuclear locations and then transported to the nuclear boundary for export. Consequently, the spatial distributions of these molecules encode their underlying dynamics. While mechanistic models for molecular counts have revealed numerous insights into gene expression, they have largely neglected now-available subcellular spatial resolution down to individual molecules. Owing to the technical challenges inherent in spatial stochastic processes, tools for studying these subcellular spatial patterns are still limited. Here, we introduce a spatial stochastic model of nuclear mRNA with two-state (telegraph) transcriptional dynamics. Observations of the model can be concisely described as following a spatial Cox process driven by a stochastically switching partial differential equation. We derive analytical solutions for spatial and demographic moments and validate them with simulations. We show that the distribution of mRNA counts can be accurately approximated by a Poisson-beta distribution with tractable parameters, even with complex spatial dynamics. This observation allows for efficient parameter inference demonstrated on synthetic data. Altogether, our work adds progress towards a new frontier of subcellular spatial resolution in inferring the dynamics of gene expression from static snapshot data.

Comparative single cell analysis of transcriptional bursting reveals the role of genome organization on de novo transcript origination

Spermatogenesis is a key developmental process underlying the origination of newly evolved genes. However, rapid cell type-specific transcriptomic divergence of the Drosophila germline has posed a significant technical barrier for comparative single-cell RNA-sequencing (scRNA-Seq) studies. By quantifying a surprisingly strong correlation between species- and cell type-specific divergence in three closely related Drosophila species, we apply a new statistical procedure to identify a core set of 198 genes that are highly predictive of cell type identity while remaining robust to species-specific differences that span over 25-30 million years of evolution. We then utilize cell type classifications based on the 198-gene set to show how transcriptional divergence in cell type increases throughout spermatogenic developmental time. After validating these cross-species cell type classifications using RNA fluorescence in situ hybridization (FISH) and imaging, we then investigate the influence of genome organization on the molecular evolution of spermatogenesis vis-a-vis transcriptional bursting. We first show altering transcriptional burst size contributes to pre-meiotic transcription and altering bursting frequency contributes to post-meiotic expression. We then report global differences in autosomal vs. X chromosomal transcription may arise in a developmental stage preceding full testis organogenesis by showing evolutionarily conserved decreases in X-linked transcription bursting kinetics in all examined somatic and germline cell types. Finally, we provide evidence supporting the cultivator model of de novo gene origination by demonstrating how the appearance of newly evolved testis-specific transcripts potentially provides short-range regulation of neighboring genes' transcriptional bursting properties during key stages of spermatogenesis.

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (1) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct Escherichia coli promoters and (2) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

Gene Regulation by a Kinetic Riboswitch with Negative Feedback Loop

Understanding the folding behaviors and cellular roles is important to fully illuminate functions of riboswitches in vivo. Since riboswitches act without the need for protein factors, RNA structure prediction methods are ideally suited for computationally analyzing their cellular activities. Here, a helix-based RNA folding theory is used to predict the cotranscriptional folding pathways of the flavin mononucleotide (FMN)-binding riboswitch from Bacillus subtilis (B. subtilis) under different conditions. The results show that the efficient function is determined by a balance between the transcription speed, pausing, and the binding rates of the metabolite. According to the predicted behaviors, a general kinetic model is established to investigate how the riboswitch couples sensing and regulatory functions to help bacteria respond to environmental changes at the system levels.

Tackling the Temporal Stiffness of Kinetic Monte Carlo Simulations of Well-Mixed Chemical Systems via On-the-Fly Scaling and Cost-Error Optimization

Reaction kinetics in biological systems are often subject to stochastic effects due to the low populations of reacting molecules, necessitating the adoption of kinetic Monte Carlo methods for their study. Such methods, however, can be computationally expensive, especially in the case of stiff systems, where some reactions are executed at much higher frequencies than others. We present an algorithm that reduces the reaction rate constants of the fast processes on-the-fly, thereby saving computational time, while keeping the approximation error within desirable limits. The algorithm couples the Modified Next Reaction Method for simulating stochastic systems with the Common Random Number framework and calculates accurate metrics for both the computational cost and approximation error by generating multiple sets of trajectories that correspond to increasingly reduced (downscaled) reaction rate constants. The optimum downscale factor is chosen via optimization of two conflicting objectives: (a) maximizing the speedup and (b) minimizing the approximation error introduced, and it is straightforward to tune the performance of the method, favoring accuracy versus speed or vice versa. Our approach is demonstrated on a biology-inspired well-mixed stiff system and is shown to accelerate the stochastic simulation thereof from 66 h down to 90 min, achieving a speed-up factor of 44×, without distorting the dynamics of the system studied.

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation

We consider the problem of efficiently simulating stochastic models of chemical kinetics. The Gillespie stochastic simulation algorithm (SSA) is often used to simulate these models; however, in many scenarios of interest, the computational cost quickly becomes prohibitive. This is further exacerbated in the Bayesian inference context when estimating parameters of chemical models, as the intractability of the likelihood requires multiple simulations of the underlying system. To deal with issues of computational complexity in this paper, we propose a novel hybrid τ-leap algorithm for simulating well-mixed chemical systems. In particular, the algorithm uses τ-leap when appropriate (high population densities), and SSA when necessary (low population densities, when discrete effects become non-negligible). In the intermediate regime, a combination of the two methods, which uses the properties of the underlying Poisson formulation, is employed. As illustrated through a number of numerical experiments, the hybrid τ offers significant computational savings when compared with SSA without, however, sacrificing the overall accuracy. This feature is particularly welcomed in the Bayesian inference context, as it allows for parameter estimation of stochastic chemical kinetics at reduced computational cost.

Method of moments framework for differential expression analysis of single-cell RNA sequencing data

Differential expression analysis of single-cell RNA sequencing (scRNA-seq) data is central for characterizing how experimental factors affect the distribution of gene expression. However, distinguishing between biological and technical sources of cell-cell variability and assessing the statistical significance of quantitative comparisons between cell groups remain challenging. We introduce Memento, a tool for robust and efficient differential analysis of mean expression, variability, and gene correlation from scRNA-seq data, scalable to millions of cells and thousands of samples. We applied Memento to 70,000 tracheal epithelial cells to identify interferon-responsive genes, 160,000 CRISPR-Cas9 perturbed T cells to reconstruct gene-regulatory networks, 1.2 million peripheral blood mononuclear cells (PBMCs) to map cell-type-specific quantitative trait loci (QTLs), and the 50-million-cell CELLxGENE Discover corpus to compare arbitrary cell groups. In all cases, Memento identified more significant and reproducible differences in mean expression compared with existing methods. It also identified differences in variability and gene correlation that suggest distinct transcriptional regulation mechanisms imparted by perturbations.

Dynamics of two feed forward genetic motifs in the presence of molecular noise

Understanding the function of common motifs in gene regulatory networks is an important goal of systems biology. Feed forward loops (FFLs) are an example of such a motif. In FFLs, a gene (X) regulates another gene (Z) both directly and via an intermediary gene (Y). Previous theoretical studies have suggested several possible functions for FFLs, based on their transient responses to changes in input signals (using deterministic models) and their fluctuations around steady state (using stochastic models). In this paper we study stochastic models of the two most common FFLs, "coherent type 1" and "incoherent type 1". We incorporate molecular noise by treating DNA binding, transcription, translation, and decay as stochastic processes. By comparing the dynamics of these loops with models of alternative networks (in which X does not regulate Y), we explore how FFLs act to process information in the presence of noise. This work highlights the importance of incorporating realistic molecular noise in studying both the transient and steady-state behavior of gene regulatory networks.

Genetically Encoded Trensor Circuits Report HeLa Cell Treatment with Polyplexed Plasmid DNA and Small-Molecule Transfection Modulators

HeLa cell transfection with plasmid DNA (pDNA) is widely used to materialize biologicals and as a preclinical test of nucleic acid-based vaccine efficacy. We sought to genetically encode mammalian transfection sensor (Trensor) circuits and test their utility in HeLa cells for detecting molecules and methods for their propensity to influence transfection. We intended these Trensor circuits to be triggered if their host cell was treated with polyplexed pDNA or certain small-molecule modulators of transfection. We prioritized three promoters, implicated by others in feedback responses as cells import and process foreign material and stably integrated each into the genomes of three different cell lines, each upstream of a green fluorescent protein (GFP) open reading frame within a transgene. All three Trensor circuits showed an increase in their GFP expression when their host HeLa cells were incubated with pDNA and the degraded polyamidoamine dendrimer reagent, SuperFect. We next experimentally demonstrated the modulation of PEI-mediated HeLa cell transient transfection by four different small molecules, with Trichostatin A (TSA) showing the greatest propensity to boost transgene expression. The Trensor circuit based on the TRA2B promoter (Trensor-T) was triggered by incubation with TSA alone and not the other three small molecules. These data suggest that mammalian reporter circuits could enable low-cost, high-throughput screening to identify novel transfection methods and reagents without the need to perform actual transfections requiring costly plasmids or expensive fluorescent labels.

Molecular mechanisms of precise timing in cell lysis

Many biological systems exhibit precise timing of events, and one of the most known examples is cell lysis, which is a process of breaking bacterial host cells in the virus infection cycle. However, the underlying microscopic picture of precise timing remains not well understood. We present a novel theoretical approach to explain the molecular mechanisms of effectively deterministic dynamics in biological systems. Our hypothesis is based on the idea of stochastic coupling between relevant underlying biophysical and biochemical processes that lead to noise cancellation. To test this hypothesis, we introduced a minimal discrete-state stochastic model to investigate how holin proteins produced by bacteriophages break the inner membranes of gram-negative bacteria. By explicitly solving this model, the dynamic properties of cell lysis are fully evaluated, and theoretical predictions quantitatively agree with available experimental data for both wild-type and holin mutants. It is found that the observed threshold-like behavior is a result of the balance between holin proteins entering the membrane and leaving the membrane during the lysis. Theoretical analysis suggests that the cell lysis achieves precise timing for wild-type species by maximizing the number of holins in the membrane and narrowing their spatial distribution. In contrast, for mutated species, these conditions are not satisfied. Our theoretical approach presents a possible molecular picture of precise dynamic regulation in intrinsically random biological processes.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

Advanced methods for gene network identification and noise decomposition from single-cell data

Central to analyzing noisy gene expression systems is solving the Chemical Master Equation (CME), which characterizes the probability evolution of the reacting species' copy numbers. Solving CMEs for high-dimensional systems suffers from the curse of dimensionality. Here, we propose a computational method for improved scalability through a divide-and-conquer strategy that optimally decomposes the whole system into a leader system and several conditionally independent follower subsystems. The CME is solved by combining Monte Carlo estimation for the leader system with stochastic filtering procedures for the follower subsystems. We demonstrate this method with high-dimensional numerical examples and apply it to identify a yeast transcription system at the single-cell resolution, leveraging mRNA time-course experimental data. The identification results enable an accurate examination of the heterogeneity in rate parameters among isogenic cells. To validate this result, we develop a noise decomposition technique exploiting time-course data but requiring no supplementary components, e.g., dual-reporters.

Phase separation provides a mechanism to drive phenotype switching

Phenotypic switching plays a crucial role in cell fate determination across various organisms. Recent experimental findings highlight the significance of protein compartmentalization via liquid-liquid phase separation in influencing such decisions. However, the precise mechanism through which phase separation regulates phenotypic switching remains elusive. To investigate this, we established a mathematical model that couples a phase separation process and a gene expression process with feedback. We used the chemical master equation theory and mean-field approximation to study the effects of phase separation on the gene expression products. We found that phase separation can cause bistability and bimodality. Furthermore, phase separation can control the bistable properties of the system, such as bifurcation points and bistable ranges. On the other hand, in stochastic dynamics, the droplet phase exhibits double peaks within a more extensive phase separation threshold range than the dilute phase, indicating the pivotal role of the droplet phase in cell fate decisions. These findings propose an alternative mechanism that influences cell fate decisions through the phase separation process. As phase separation is increasingly discovered in gene regulatory networks, related modeling research can help build biomolecular systems with desired properties and offer insights into explaining cell fate decisions.

Feedback between stochastic gene networks and population dynamics enables cellular decision-making

Phenotypic selection occurs when genetically identical cells are subject to different reproductive abilities due to cellular noise. Such noise arises from fluctuations in reactions synthesizing proteins and plays a crucial role in how cells make decisions and respond to stress or drugs. We propose a general stochastic agent-based model for growing populations capturing the feedback between gene expression and cell division dynamics. We devise a finite state projection approach to analyze gene expression and division distributions and infer selection from single-cell data in mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the trade-off between phenotypic switching and selection enables robust decision-making essential for synthetic circuits and developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage and adaptation during antibiotic exposure in Escherichia coli.

The purpose and ubiquity of turnover

Turnover-constant component production and destruction-is ubiquitous in biology. Turnover occurs across organisms and scales, including for RNAs, proteins, membranes, macromolecular structures, organelles, cells, hair, feathers, nails, antlers, and teeth. For many systems, turnover might seem wasteful when degraded components are often fully functional. Some components turn over with shockingly high rates and others do not turn over at all, further making this process enigmatic. However, turnover can address fundamental problems by yielding powerful properties, including regeneration, rapid repair onset, clearance of unpredictable damage and errors, maintenance of low constitutive levels of disrepair, prevention of stable hazards, and transitions. I argue that trade-offs between turnover benefits and metabolic costs, combined with constraints on turnover, determine its presence and rates across distinct contexts. I suggest that the limits of turnover help explain aging and that turnover properties and the basis for its levels underlie this fundamental component of life.

Emerging insights into transcriptional condensates

Eukaryotic transcription, a fundamental process that governs cell-specific gene expression, has long been the subject of extensive investigations in the fields of molecular biology, biochemistry, and structural biology. Recent advances in microscopy techniques have led to a fascinating concept known as "transcriptional condensates." These dynamic assemblies are the result of a phenomenon called liquid‒liquid phase separation, which is driven by multivalent interactions between the constituent proteins in cells. The essential proteins associated with transcription are concentrated in transcriptional condensates. Recent studies have shed light on the temporal dynamics of transcriptional condensates and their potential role in enhancing the efficiency of transcription. In this article, we explore the properties of transcriptional condensates, investigate how they evolve over time, and evaluate the significant impact they have on the process of transcription. Furthermore, we highlight innovative techniques that allow us to manipulate these condensates, thus demonstrating their responsiveness to cellular signals and their connection to transcriptional bursting. As our understanding of transcriptional condensates continues to grow, they are poised to revolutionize our understanding of eukaryotic gene regulation.

A dual fluorescence protein expression system detects cell cycle dependent protein noise

Inherently identical cells exhibit significant phenotypic variation. It can be essential for many biological processes and is known to arise from stochastic, 'noisy', gene expression that is determined by intrinsic and extrinsic components. It is now obvious that the noise varies as a function of inducer concentration. However, its fluctuation over the cell cycle is limited. Applying dual colour fluorescence protein reporter system, Cyan Fluorescent Protein (CFP) and Yellow fluorescent protein (YFP) tagged multi-copy plasmids, we determine variation of the noise components over the phases in lac promoter induced by Isopropyl β-D-1-thiogalactopyranoside (IPTG) and in presence of additional Magnesium, Mg2+ ion. We, also, estimate the how such system deviates from observations of single-copy plasmid. Found 25 % difference between multi-copy system and single-copy system clarifies that observed noise is considerable and estimates population behaviour during the cell cycle. We show that total variation in cells induced with IPTG is determined by higher extrinsic than intrinsic noise. It increases from Lag to Exponential phase and decreases from Retardation to Stationary phase. By observing slow and fast dividing cells, we show that 5 mM Mg2+ increases population homogeneity compared to 2.5 mM Mg2+ in the environment. The experimental data obtained using dual colour fluorescence protein reporter system demonstrates that protein expression noise, depending on intra cellular ionic concentration, is tightly controlled by phase of the cell.

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients

We recently described a paradigm for engineering bacterial adaptation using plasmids coupled to the same origin of replication. In this study, we use plasmid coupling to generate spatially separated and phenotypically distinct populations in response to heterogeneous environments. Using a custom microfluidic device, we continuously tracked engineered populations along induced gradients, enabling an in-depth analysis of the spatiotemporal dynamics of plasmid coupling. Our observations reveal a pronounced phenotypic separation within 4 h exposure to an opposing gradient of AHL and arabinose. Additionally, by modulating the burden strength balance between coupled plasmids, we demonstrate the inherent limitations and tunability of this system. Intriguingly, phenotypic separation persists for an extended time, hinting at a biophysical spatial retention mechanism reminiscent of natural speciation processes. Complementing our experimental data, mathematical models provide invaluable insights into the underlying mechanisms and guide optimization of plasmid coupling for prospective applications of environmental copy number adaptation engineering across separated domains.

Increased heterogeneity in expression of genes associated with cancer progression and drug resistance

Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.

MetJ-Based Mutually Interfering SAM-ON/SAM-OFF Biosensors

SAM (S-adenosylmethionine) is an important metabolite that operates as a major donor of methyl groups and is a controller of various physiological processes. Its availability is also believed to be a major bottleneck in the biological production of numerous high-value metabolites. Here, we constructed SAM-sensing systems using MetJ, an SAM-dependent transcriptional regulator, as a core component. SAM is a corepressor of MetJ, which suppresses the MetJ promoter with an increasing cellular concentration of SAM (SAM-OFF sensor). The application of transcriptional interference and evolutionary tuning effectively inverted its response, yielding a SAM-ON sensor (signal increases with increasing SAM concentration). By linking two genes encoding fluorescent protein reporters in such a way that their transcription events interfere with each other's and by placing one of them under the control of MetJ, we could increase the effective signal-to-noise ratio of the SAM sensor while decreasing the batch-to-batch deviation in signal output, likely by canceling out the growth-associated fluctuation in translational resources. By taking the ratio of SAM-ON/SAM-OFF signals and by resetting the default pool size of SAM, we could rapidly identify SAM synthetase (MetK) mutants with increased cellular activity from a random library. The strategy described herein should be widely applicable for identifying activity mutants, which would be otherwise overlooked because of the strong homeostasis of metabolic networks.

A Kinetic Finite Volume Discretization of the Multidimensional PIDE Model for Gene Regulatory Networks

In this paper, a finite volume discretization scheme for partial integro-differential equations (PIDEs) describing the temporal evolution of protein distribution in gene regulatory networks is proposed. It is shown that the obtained set of ODEs can be formally represented as a compartmental kinetic system with a strongly connected reaction graph. This allows the application of the theory of nonnegative and compartmental systems for the qualitative analysis of the approximating dynamics. In this framework, it is straightforward to show the existence, uniqueness and stability of equilibria. Moreover, the computation of the stationary probability distribution can be traced back to the solution of linear equations. The discretization scheme is presented for one and multiple dimensional models separately. Illustrative computational examples show the precision of the approach, and good agreement with previous results in the literature.

A computational model of stem cells' internal mechanism to recapitulate spatial patterning and maintain the self-organized pattern in the homeostasis state

The complex functioning of multi-cellular tissue development relies on proper cell production rates to replace dead or differentiated specialized cells. Stem cells are critical for tissue development and maintenance, as they produce specialized cells to meet the tissues' demands. In this study, we propose a computational model to investigate the stem cell's mechanism, which generates the appropriate proportion of specialized cells, and distributes them to their correct position to form and maintain the organized structure in the population through intercellular reactions. Our computational model focuses on early development, where the populations overall behavior is determined by stem cells and signaling molecules. The model does not include complicated factors such as movement of specialized cells or outside signaling sources. The results indicate that in our model, the stem cells can organize the population into a desired spatial pattern, which demonstrates their ability to self-organize as long as the corresponding leading signal is present. We also investigate the impact of stochasticity, which provides desired non-genetic diversity; however, it can also break the proper boundaries of the desired spatial pattern. We further examine the role of the death rate in maintaining the system's steady state. Overall, our study sheds light on the strategies employed by stem cells to organize specialized cells and maintain proper functionality. Our findings provide insight into the complex mechanisms involved in tissue development and maintenance, which could lead to new approaches in regenerative medicine and tissue engineering.

Gene expression bias between the subgenomes of allopolyploid hybrids is an emergent property of the kinetics of expression

Hybridization coupled to polyploidy, or allopolyploidy, has dramatically shaped the evolution of flowering plants, teleost fishes, and other lineages. Studies of recently formed allopolyploid plants have shown that the two subgenomes that merged to form that new allopolyploid do not generally express their genes equally. Instead, one of the two subgenomes expresses its paralogs more highly on average. Meanwhile, older allopolyploidy events tend to show biases in duplicate losses, with one of the two subgenomes retaining more genes than the other. Since reduced expression is a pathway to duplicate loss, understanding the origins of expression biases may help explain the origins of biased losses. Because we expect gene expression levels to experience stabilizing selection, our conceptual frameworks for how allopolyploid organisms form tend to assume that the new allopolyploid will show balanced expression between its subgenomes. It is then necessary to invoke phenomena such as differences in the suppression of repetitive elements to explain the observed expression imbalances. Here we show that, even for phenotypically identical diploid progenitors, the inherent kinetics of gene expression give rise to biases between the expression levels of the progenitor genes in the hybrid. Some of these biases are expected to be gene-specific and not give rise to global differences in progenitor gene expression. However, particularly in the case of allopolyploids formed from progenitors with different genome sizes, global expression biases favoring one subgenome are expected immediately on formation. Hence, expression biases are arguably the expectation upon allopolyploid formation rather than a phenomenon needing explanation. In the future, a deeper understanding of the kinetics of allopolyploidy may allow us to better understand both biases in duplicate losses and hybrid vigor.

An alternative interpretation for tailing in survival curves for bacteria exposed to germicidal radiation

It has been proposed that transient and reversible phenotypic changes could modify the response of bacteria to germicidal radiation, eventually leading to tailing in the survival curves. If this were the case, changes in susceptibility to radiation would reflect variations in gene expression and should only occur in cells in which gene expression is active. To obtain experimental evidence supporting the involvement of phenotypic changes in the origin of tailing, we studied changes in the susceptibility to radiation of cells able to survive high fluences, using split irradiations. Stationary phase cells of Enterobacter cloacae and Deinococcus radiodurans, in which gene expression is active, and spores of Bacillus subtilis, which are dormant cells without active gene expression, were used as microbial models. While cells of E. cloacae and D. radiodurans became susceptible after surviving exposures to high fluences, tolerant spores exhibited unchanged response to radiation. The results can be interpreted assuming that noise in gene expression modifies bacterial susceptibility to radiation, and tailing is the result of intrinsic phenomena of bacterial physiology rather than a technical artifact. For either theoretical or practical purposes, deviations from simple exponential decay kinetics should be considered in estimations of the effects of germicidal radiation at high fluences.

Flagellar dynamics reveal fluctuations and kinetic limit in the Escherichia coli chemotaxis network

The Escherichia coli chemotaxis network, by which bacteria modulate their random run/tumble swimming pattern to navigate their environment, must cope with unavoidable number fluctuations ("noise") in its molecular constituents like other signaling networks. The probability of clockwise (CW) flagellar rotation, or CW bias, is a measure of the chemotaxis network's output, and its temporal fluctuations provide a proxy for network noise. Here we quantify fluctuations in the chemotaxis signaling network from the switching statistics of flagella, observed using time-resolved fluorescence microscopy of individual optically trapped E. coli cells. This approach allows noise to be quantified across the dynamic range of the network. Large CW bias fluctuations are revealed at steady state, which may play a critical role in driving flagellar switching and cell tumbling. When the network is stimulated chemically to higher activity, fluctuations dramatically decrease. A stochastic theoretical model, inspired by work on gene expression noise, points to CheY activation occurring in bursts, driving CW bias fluctuations. This model also shows that an intrinsic kinetic ceiling on network activity places an upper limit on activated CheY and CW bias, which when encountered suppresses network fluctuations. This limit may also prevent cells from tumbling unproductively in steep gradients.

Stochastic chemical kinetics of cell fate decision systems: From single cells to populations and back

Stochastic chemical kinetics is a widely used formalism for studying stochasticity of chemical reactions inside single cells. Experimental studies of reaction networks are generally performed with cells that are part of a growing population, yet the population context is rarely taken into account when models are developed. Models that neglect the population context lose their validity whenever the studied system influences traits of cells that can be selected in the population, a property that naturally arises in the complex interplay between single-cell and population dynamics of cell fate decision systems. Here, we represent such systems as absorbing continuous-time Markov chains. We show that conditioning on non-absorption allows one to derive a modified master equation that tracks the time evolution of the expected population composition within a growing population. This allows us to derive consistent population dynamics models from a specification of the single-cell process. We use this approach to classify cell fate decision systems into two types that lead to different characteristic phases in emerging population dynamics. Subsequently, we deploy the gained insights to experimentally study a recurrent problem in biology: how to link plasmid copy number fluctuations and plasmid loss events inside single cells to growth of cell populations in dynamically changing environments.

Mathematical Models of HIV-1 Dynamics, Transcription, and Latency

HIV-1 latency is a major barrier to curing infections with antiretroviral therapy and, consequently, to eliminating the disease globally. The establishment, maintenance, and potential clearance of latent infection are complex dynamic processes and can be best described with the help of mathematical models followed by experimental validation. Here, we review the use of viral dynamics models for HIV-1, with a focus on applications to the latent reservoir. Such models have been used to explain the multi-phasic decay of viral load during antiretroviral therapy, the early seeding of the latent reservoir during acute infection and the limited inflow during treatment, the dynamics of viral blips, and the phenomenon of post-treatment control. Finally, we discuss that mathematical models have been used to predict the efficacy of potential HIV-1 cure strategies, such as latency-reversing agents, early treatment initiation, or gene therapies, and to provide guidance for designing trials of these novel interventions.

"Bet hedging" against climate change in developing and adult animals: roles for stochastic gene expression, phenotypic plasticity, epigenetic inheritance and adaptation

Animals from embryos to adults experiencing stress from climate change have numerous mechanisms available for enhancing their long-term survival. In this review we consider these options, and how viable they are in a world increasingly experiencing extreme weather associated with climate change. A deeply understood mechanism involves natural selection, leading to evolution of new adaptations that help cope with extreme and stochastic weather events associated with climate change. While potentially effective at staving off environmental challenges, such adaptations typically occur very slowly and incrementally over evolutionary time. Consequently, adaptation through natural selection is in most instances regarded as too slow to aid survival in rapidly changing environments, especially when considering the stochastic nature of extreme weather events associated with climate change. Alternative mechanisms operating in a much shorter time frame than adaptation involve the rapid creation of alternate phenotypes within a life cycle or a few generations. Stochastic gene expression creates multiple phenotypes from the same genotype even in the absence of environmental cues. In contrast, other mechanisms for phenotype change that are externally driven by environmental clues include well-understood developmental phenotypic plasticity (variation, flexibility), which can enable rapid, within-generation changes. Increasingly appreciated are epigenetic influences during development leading to rapid phenotypic changes that can also immediately be very widespread throughout a population, rather than confined to a few individuals as in the case of favorable gene mutations. Such epigenetically-induced phenotypic plasticity can arise rapidly in response to stressors within a generation or across a few generations and just as rapidly be "sunsetted" when the stressor dissipates, providing some capability to withstand environmental stressors emerging from climate change. Importantly, survival mechanisms resulting from adaptations and developmental phenotypic plasticity are not necessarily mutually exclusive, allowing for classic "bet hedging". Thus, the appearance of multiple phenotypes within a single population provides for a phenotype potentially optimal for some future environment. This enhances survival during stochastic extreme weather events associated with climate change. Finally, we end with recommendations for future physiological experiments, recommending in particular that experiments investigating phenotypic flexibility adopt more realistic protocols that reflect the stochastic nature of weather.

Deciphering a global source of non-genetic heterogeneity in cancer cells

Cell-to-cell variability within a clonal population, also known as non-genetic heterogeneity, has created significant challenges for intervening with diseases such as cancer. While non-genetic heterogeneity can arise from the variability in the expression of specific genes, it remains largely unclear whether and how clonal cells could be heterogeneous in the expression of the entire transcriptome. Here, we showed that gene transcriptional activity is globally modulated in individual cancer cells, leading to non-genetic heterogeneity in the global transcription rate. Such heterogeneity contributes to cell-to-cell variability in transcriptome size and displays both dynamic and static characteristics, with the global transcription rate temporally modulated in a cell-cycle-coupled manner and the time-averaged rate being distinct between cells and heritable across generations. Additional evidence indicated the role of ATP metabolism in this heterogeneity, and suggested its implication in intrinsic cancer drug tolerance. Collectively, our work shed light on the mode, mechanism, and implication of a global but often hidden source of non-genetic heterogeneity.

The epithelial-mesenchymal plasticity landscape: principles of design and mechanisms of regulation

Epithelial-mesenchymal plasticity (EMP) enables cells to interconvert between several states across the epithelial-mesenchymal landscape, thereby acquiring hybrid epithelial/mesenchymal phenotypic features. This plasticity is crucial for embryonic development and wound healing, but also underlies the acquisition of several malignant traits during cancer progression. Recent research using systems biology and single-cell profiling methods has provided novel insights into the main forces that shape EMP, which include the microenvironment, lineage specification and cell identity, and the genome. Additionally, key roles have emerged for hysteresis (cell memory) and cellular noise, which can drive stochastic transitions between cell states. Here, we review these forces and the distinct but interwoven layers of regulatory control that stabilize EMP states or facilitate epithelial-mesenchymal transitions (EMTs) and discuss the therapeutic potential of manipulating the EMP landscape.

Evaluating single-cell variability in proteasomal decay

Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. Here we estimate the single-cell protein decay rates of two degron GFPs in Saccharomyces cerevisiae using time-lapse microscopy. We find substantial cell-to-cell variability in the decay rates of the degron GFPs. We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs. Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.

The σB alternative sigma factor circuit modulates noise to generate different types of pulsing dynamics

Single-cell approaches are revealing a high degree of heterogeneity, or noise, in gene expression in isogenic bacteria. How gene circuits modulate this noise in gene expression to generate robust output dynamics is unclear. Here we use the Bacillus subtilis alternative sigma factor σB as a model system for understanding the role of noise in generating circuit output dynamics. σB controls the general stress response in B. subtilis and is activated by a range of energy and environmental stresses. Recent single-cell studies have revealed that the circuit can generate two distinct outputs, stochastic pulsing and a single pulse response, but the conditions under which each response is generated are under debate. We implement a stochastic mathematical model of the σB circuit to investigate this and find that the system's core circuit can generate both response types. This is despite one response (stochastic pulsing) being stochastic in nature, and the other (single response pulse) being deterministic. We demonstrate that the main determinant for whichever response is generated is the degree with which the input pathway activates the core circuit, although the noise properties of the input pathway also biases the system towards one or the other type of output. Thus, our work shows how stochastic modelling can reveal the mechanisms behind non-intuitive gene circuit output dynamics.

A genetic switch controls Pseudomonas aeruginosa surface colonization

Efficient colonization of mucosal surfaces is essential for opportunistic pathogens like Pseudomonas aeruginosa, but how bacteria collectively and individually adapt to optimize adherence, virulence and dispersal is largely unclear. Here we identified a stochastic genetic switch, hecR-hecE, which is expressed bimodally and generates functionally distinct bacterial subpopulations to balance P. aeruginosa growth and dispersal on surfaces. HecE inhibits the phosphodiesterase BifA and stimulates the diguanylate cyclase WspR to increase c-di-GMP second messenger levels and promote surface colonization in a subpopulation of cells; low-level HecE-expressing cells disperse. The fraction of HecE+ cells is tuned by different stress factors and determines the balance between biofilm formation and long-range cell dispersal of surface-grown communities. We also demonstrate that the HecE pathway represents a druggable target to effectively counter P. aeruginosa surface colonization. Exposing such binary states opens up new ways to control mucosal infections by a major human pathogen.

Transcription factor fluctuations underlie cell-to-cell variability in a signaling pathway response

Stochastic differences among clonal cells can initiate cell fate decisions in development or cause cell-to-cell differences in the responses to drugs or extracellular ligands. One hypothesis is that some of this phenotypic variability is caused by stochastic fluctuations in the activities of transcription factors (TFs). We tested this hypothesis in NIH3T3-CG cells using the response to Hedgehog signaling as a model cellular response. Here, we present evidence for the existence of distinct fast- and slow-responding substates in NIH3T3-CG cells. These two substates have distinct expression profiles, and fluctuations in the Prrx1 TF underlie some of the differences in expression and responsiveness between fast and slow cells. Our results show that fluctuations in TFs can contribute to cell-to-cell differences in Hedgehog signaling.

Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

Bistability regulates TNFR2-mediated survival and death of T-regulatory cells

A subgroup of T cells called T-regulatory cells (Tregs) regulates the body's immune responses to maintain homeostasis and self-tolerance. Tregs are crucial for preventing illnesses like cancer and autoimmunity. However, contrasting patterns of Treg frequency are observed in different autoimmune diseases. The commonality of tumour necrosis factor receptor 2 (TNFR2) defects and decrease in Treg frequency on the onset of autoimmunity demands an in-depth study of the TNFR2 pathway. To unravel this mystery, we need to study the mechanism of cell survival and death in Tregs. Here, we construct an ordinary differential equation (ODE)-based model to capture the mechanism of cell survival and apoptosis in Treg cells via TNFR2 signalling. The sensitivity analysis reveals that the input stimulus, the concentration of tumour necrosis factor (TNF), is the most sensitive parameter for the model system. The model shows that the cell goes into survival or apoptosis via bistable switching. Through hysteretic switching, the system tries to cope with the changing stimuli. In order to understand how stimulus strength and feedback strength influence cell survival and death, we compute bifurcation diagrams and obtain cell fate maps. Our results indicate that the elevated TNF concentration and increased c-Jun N-terminal kinase (JNK) phosphorylation are the major contributors to the death of T-regulatory cells. Biological evidence cements our hypothesis and can be controlled by reducing the TNF concentration. Finally, the system was studied under stochastic perturbation to see the effect of noise on the system's dynamics. We observed that introducing random perturbations disrupts the bistability, reducing the system's bistable region, which can affect the system's normal functioning.

New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications

Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Polarization and cell-fate decision facilitated by the adaptor Ste50p in Saccharomyces cerevisiae

In response to pheromone, many proteins localize on the plasma membrane of yeast cell to reform it into a polarized shmoo structure. The adaptor protein Ste50p, known as a pheromone signal enhancer critical for shmoo polarization, has never been explored systematically for its localization and function in the polarization process. Time-lapse single-cell imaging and quantitation shown here characterizes Ste50p involvement in the establishment of cell polarity. We found that Ste50p patches on the cell cortex mark the point of shmoo initiation, these patches could move, and remain associated with the growing shmoo tip in a pheromone concentration time-dependent manner until shmoo maturation. A Ste50p mutant impaired in patch localization suffers a delay in polarization. By quantitative analysis we show that polarization correlates with the rising levels of Ste50p, enabling rapid cell responses to pheromone that correspond to a critical level of Ste50p at the initial G1 phase. We exploited the quantitative differences in the pattern of Ste50p expression to correlate with the cell-cell phenotypic heterogeneity, showing Ste50p involvement in the cellular differentiation choice. Taken together, these findings present Ste50p to be part of the early shmoo development phase, suggesting that Ste50p may be involved with the polarisome in the initiation of polarization, and plays a role in regulating the polarized growth of shmoo during pheromone response.

Transcription factor binding process is the primary driver of noise in gene expression

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.

A Half Century Defining the Logic of Cellular Life

Over more than fifty years, I have studied how the logic that controls and integrates cell function is built into the dynamic architecture of living cells. I worked with a succession of exceptionally talented students and postdocs, and we discovered that the bacterial cell is controlled by an integrated genetic circuit in which transcriptional and translational controls are interwoven with the three-dimensional deployment of key regulatory and morphological proteins. Caulobacter's interconnected genetic regulatory network includes logic that regulates sets of genes expressed at specific times in the cell cycle and mechanisms that synchronize the advancement of the core cyclical circuit with chromosome replication and cytokinesis. Here, I have traced my journey from New York City art student to Stanford developmental biologist.

A novel cis-regulatory element drives early expression of Nkx3.2 in the gnathostome primary jaw joint

The acquisition of movable jaws was a major event during vertebrate evolution. The role of NK3 homeobox 2 (Nkx3.2) transcription factor in patterning the primary jaw joint of gnathostomes (jawed vertebrates) is well known, however knowledge about its regulatory mechanism is lacking. In this study, we report a proximal enhancer element of Nkx3.2 that is deeply conserved in most gnathostomes but undetectable in the jawless hagfish and lamprey. This enhancer is active in the developing jaw joint region of the zebrafish Danio rerio, and was thus designated as jaw joint regulatory sequence 1 (JRS1). We further show that JRS1 enhancer sequences from a range of gnathostome species, including a chondrichthyan and mammals, have the same activity in the jaw joint as the native zebrafish enhancer, indicating a high degree of functional conservation despite the divergence of cartilaginous and bony fish lineages or the transition of the primary jaw joint into the middle ear of mammals. Finally, we show that deletion of JRS1 from the zebrafish genome using CRISPR/Cas9 results in a significant reduction of early gene expression of nkx3.2 and leads to a transient jaw joint deformation and partial fusion. Emergence of this Nkx3.2 enhancer in early gnathostomes may have contributed to the origin and shaping of the articulating surfaces of vertebrate jaws.