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How enhancers interpret morphogen gradients to generate gene expression patterns is a central question in developmental biology. Recent studies have proposed that enhancers can dictate whether, when, and at what rate promoters engage in transcription, but the complexity of endogenous enhancers calls for theoretical models with too many free parameters to quantitatively dissect these regulatory strategies. To overcome this limitation, we established a minimal promoter-proximal synthetic enhancer in embryos of Drosophila melanogaster. Here, a gradient of the Dorsal activator is read by a single Dorsal DNA binding site. Using live imaging to quantify transcriptional activity, we found that a single binding site can regulate whether promoters engage in transcription in a concentration-dependent manner. By modulating the binding-site affinity, we determined that a gene's decision to transcribe and its transcriptional onset time can be explained by a simple model where the promoter traverses multiple kinetic barriers before transcription can ensue.

Gene regulation is central to cellular function. Yet, despite decades of work, we lack quantitative models that can predict how transcriptional control emerges from molecular interactions at the gene locus. Thermodynamic models of transcription, which assume that gene circuits operate at equilibrium, have previously been employed with considerable success in the context of bacterial systems. However, the presence of ATP-dependent processes within the eukaryotic transcriptional cycle suggests that equilibrium models may be insufficient to capture how eukaryotic gene circuits sense and respond to input transcription factor concentrations. Here, we employ simple kinetic models of transcription to investigate how energy dissipation within the transcriptional cycle impacts the rate at which genes transmit information and drive cellular decisions. We find that biologically plausible levels of energy input can lead to significant gains in how rapidly gene loci transmit information, but discover that the regulatory mechanisms underlying these gains change depending on the level of interference from non-cognate activator binding. When interference is low, information is maximized by harnessing energy to push the sensitivity of the transcriptional response to input transcription factors beyond its equilibrium limits. Conversely, when interference is high, conditions favor genes that harness energy to increase transcriptional specificity by proofreading activator identity. Our analysis further reveals that equilibrium gene regulatory mechanisms break down as transcriptional interference increases, suggesting that energy dissipation may be indispensable in systems where non-cognate factor interference is sufficiently large.

Single cell biology has the potential to elucidate many critical biological processes and diseases, from development and regeneration to cancer. Single cell analyses are uncovering the molecular diversity of cells, revealing a clearer picture of the variation among and between different cell types. New techniques are beginning to unravel how differences in cell state—transcriptional, epigenetic, and other characteristics—can lead to different cell fates among genetically identical cells, which underlies complex processes such as embryonic development, drug resistance, response to injury, and cellular reprogramming. Single cell technologies also pose significant challenges relating to processing and analyzing vast amounts of data collected. To realize the potential of single cell technologies, new computational approaches are needed. On March 17–19, 2021, experts in single cell biology met virtually for the Keystone eSymposium “Single Cell Biology” to discuss advances both in single cell applications and technologies.

The responses of plants to their environment are often dependent on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, the lack of implementation of these technologies in plants has limited concomitant quantitative studies in this kingdom. Here, we applied the PP7 and MS2 RNA-labelling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we counted nascent RNA transcripts in real time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana. Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the proportion of cells that switch from an undetectable basal state to a high-transcription state, instead of modulating the rate of transcription across all cells in a graded fashion. This switch-like behaviour, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from stochasticity intrinsic to individual alleles instead of variability in cellular composition. Together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.

mRNA synthesis is one of the earliest readouts of the activity of a transcribed gene, which is of particular interest in the context of metazoan cell fate specification. These processes are intrinsically dynamic and stochastic, which makes in vivo single-cell measurements inevitable. Here, we present the application of a technology that has been widely used in single celled organisms to measure transcriptional activity in developing embryos of the fruit fly Drosophila melanogaster. The method allows for quantification of instantaneous polymerase occupancy of active gene loci and thereby enables the development and testing of models of gene regulation in development.

mRNA synthesis is one of the earliest readouts of the activity of a transcribed gene, which is of particular interest in the context of metazoan cell fate specification. These processes are intrinsically dynamic and stochastic, which makes in vivo single-cell measurements inevitable. Here, we present the application of a technology that has been widely used in single celled organisms to measure transcriptional activity in developing embryos of the fruit fly Drosophila melanogaster. The method allows for quantification of instantaneous polymerase occupancy of active gene loci and thereby enables the development and testing of models of gene regulation in development.

Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes.

Enhancers drive the transcription of genes in defined spatio-temporal patterns of gene expression that orchestrate the development of animal body plans. The spatial aspects of enhancer function and gene expression pattern formation have been elucidated with elegant experiments in ​Drosophila melanogaster and other organisms. However, our knowledge regarding the temporal dynamics of pattern formation is still incomplete.In this work, I coupled the MS2 reporter system, which enables real-time, single-locus analysis of transcription, with the regulatory sequences of the gene ​even-skipped (eve)​, a fine model of enhancer function active in seven antero-posterior stripes that ring the fruit fly embryo before gastrulation.Our results show highly dynamic expression patterns that emerge from stochastic bursts of transcription in single loci. We found that the dynamic expression of ​eve stripes stems from shared transcriptional kinetics. Nuclei at the center of all stripes increase their frequency of transcriptional bursting. Nuclei in the inter-stripe boundaries remain quiescent and do not show any detectable bursting. All the while stripes shift anteriorly, propelled by expression flow. The bursting kinetics in the ectopic patterns of eve expression that result from lifting the repression of ​eve enhancers are similar to the kinetics in the endogenous stripes. These observations show that the various ​eve enhancers generate similar bursting kinetics regardless of the myriad of distinct inputs that shape their activity.This dissertation consists of four chapters. Chapter 1 describes the construction of our MS2-based ​eve reporter and the generation of the transgenic fruit flies carrying it. Chapter 2 goes through our imaging methods and their optimization. It ends with an account of our data analysis to turn movies of transcription in embryonic development into meaningful and quantifiable data. Chapter 3 summarizes our findings regarding the conserved kinetics of ​even-ski

Enhancers drive the transcription of genes in defined spatio-temporal patterns of gene expression that orchestrate the development of animal body plans. The spatial aspects of enhancer function and gene expression pattern formation have been elucidated with elegant experiments in ​Drosophila melanogaster and other organisms. However, our knowledge regarding the temporal dynamics of pattern formation is still incomplete.In this work, I coupled the MS2 reporter system, which enables real-time, single-locus analysis of transcription, with the regulatory sequences of the gene ​even-skipped (eve)​, a fine model of enhancer function active in seven antero-posterior stripes that ring the fruit fly embryo before gastrulation.Our results show highly dynamic expression patterns that emerge from stochastic bursts of transcription in single loci. We found that the dynamic expression of ​eve stripes stems from shared transcriptional kinetics. Nuclei at the center of all stripes increase their frequency of transcriptional bursting. Nuclei in the inter-stripe boundaries remain quiescent and do not show any detectable bursting. All the while stripes shift anteriorly, propelled by expression flow. The bursting kinetics in the ectopic patterns of eve expression that result from lifting the repression of ​eve enhancers are similar to the kinetics in the endogenous stripes. These observations show that the various ​eve enhancers generate similar bursting kinetics regardless of the myriad of distinct inputs that shape their activity.This dissertation consists of four chapters. Chapter 1 describes the construction of our MS2-based ​eve reporter and the generation of the transgenic fruit flies carrying it. Chapter 2 goes through our imaging methods and their optimization. It ends with an account of our data analysis to turn movies of transcription in embryonic development into meaningful and quantifiable data. Chapter 3 summarizes our findings regarding the conserved kinetics of ​even-ski

Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes.

Eukaryotic transcription generally occurs in bursts of activity lasting minutes to hours; however, state-of-the-art measurements have revealed that many of the molecular processes that underlie bursting, such as transcription factor binding to DNA, unfold on timescales of seconds. This temporal disconnect lies at the heart of a broader challenge in physical biology of predicting transcriptional outcomes and cellular decision-making from the dynamics of underlying molecular processes. Here, we review how new dynamical information about the processes underlying transcriptional control can be combined with theoretical models that predict not only averaged transcriptional dynamics, but also their variability, to formulate testable hypotheses about the molecular mechanisms underlying transcriptional bursting and control.

It is tempting to believe that we now own the genome. The ability to read and rewrite it at will has ushered in a stunning period in the history of science. Nonetheless, there is an Achilles' heel exposed by all of the genomic data that has accrued: We still do not know how to interpret them. Many genes are subject to sophisticated programs of transcriptional regulation, mediated by DNA sequences that harbor binding sites for transcription factors, which can up- or down-regulate gene expression depending upon environmental conditions. This gives rise to an input-output function describing how the level of expression depends upon the parameters of the regulated gene-for instance, on the number and type of binding sites in its regulatory sequence. In recent years, the ability to make precision measurements of expression, coupled with the ability to make increasingly sophisticated theoretical predictions, has enabled an explicit dialogue between theory and experiment that holds the promise of covering this genomic Achilles' heel. The goal is to reach a predictive understanding of transcriptional regulation that makes it possible to calculate gene expression levels from DNA regulatory sequence. This review focuses on the canonical simple repression motif to ask how well the models that have been used to characterize it actually work. We consider a hierarchy of increasingly sophisticated experiments in which the minimal parameter set learned at one level is applied to make quantitative predictions at the next. We show that these careful quantitative dissections provide a template for a predictive understanding of the many more complex regulatory arrangements found across all domains of life.

Information from developmental signaling pathways must be accurately decoded to generate transcriptional outcomes. In the case of Notch, the intracellular domain (NICD) transduces the signal directly to the nucleus. How enhancers decipher NICD in the real time of developmental decisions is not known. Using the MS2-MCP system to visualize nascent transcripts in single cells in Drosophila embryos, we reveal how two target enhancers read Notch activity to produce synchronized and sustained profiles of transcription. By manipulating the levels of NICD and altering specific motifs within the enhancers, we uncover two key principles. First, increased NICD levels alter transcription by increasing duration rather than frequency of transcriptional bursts. Second, priming of enhancers by tissue-specific transcription factors is required for NICD to confer synchronized and sustained activity; in their absence, transcription is stochastic and bursty. The dynamic response of an individual enhancer to NICD thus differs depending on the cellular context.

It is tempting to believe that we now own the genome. The ability to read and rewrite it at will has ushered in a stunning period in the history of science. Nonetheless, there is an Achilles' heel exposed by all of the genomic data that has accrued: We still do not know how to interpret them. Many genes are subject to sophisticated programs of transcriptional regulation, mediated by DNA sequences that harbor binding sites for transcription factors, which can up- or down-regulate gene expression depending upon environmental conditions. This gives rise to an input-output function describing how the level of expression depends upon the parameters of the regulated gene-for instance, on the number and type of binding sites in its regulatory sequence. In recent years, the ability to make precision measurements of expression, coupled with the ability to make increasingly sophisticated theoretical predictions, has enabled an explicit dialogue between theory and experiment that holds the promise of covering this genomic Achilles' heel. The goal is to reach a predictive understanding of transcriptional regulation that makes it possible to calculate gene expression levels from DNA regulatory sequence. This review focuses on the canonical simple repression motif to ask how well the models that have been used to characterize it actually work. We consider a hierarchy of increasingly sophisticated experiments in which the minimal parameter set learned at one level is applied to make quantitative predictions at the next. We show that these careful quantitative dissections provide a template for a predictive understanding of the many more complex regulatory arrangements found across all domains of life.

Information from developmental signaling pathways must be accurately decoded to generate transcriptional outcomes. In the case of Notch, the intracellular domain (NICD) transduces the signal directly to the nucleus. How enhancers decipher NICD in the real time of developmental decisions is not known. Using the MS2-MCP system to visualize nascent transcripts in single cells in Drosophila embryos, we reveal how two target enhancers read Notch activity to produce synchronized and sustained profiles of transcription. By manipulating the levels of NICD and altering specific motifs within the enhancers, we uncover two key principles. First, increased NICD levels alter transcription by increasing duration rather than frequency of transcriptional bursts. Second, priming of enhancers by tissue-specific transcription factors is required for NICD to confer synchronized and sustained activity; in their absence, transcription is stochastic and bursty. The dynamic response of an individual enhancer to NICD thus differs depending on the cellular context.

The main tenet of physical biology is that biological phenomena can be subject to the same quantitative and predictive understanding that physics has afforded in the context of inanimate matter. However, the inherent complexity of many of these biological processes often leads to the derivation of complex theoretical descriptions containing a plethora of unknown parameters. Such complex descriptions pose a conceptual challenge to the establishment of a solid basis for predictive biology. In this article, we present various exciting examples of how synthetic biology can be used to simplify biological systems and distill these phenomena down to their essential features as a means to enable their theoretical description. Here, synthetic biology goes beyond previous efforts to engineer nature and becomes a tool to bend nature to understand it. We discuss various recent and classic experiments featuring applications of this synthetic approach to the elucidation of problems ranging from bacteriophage infection, to transcriptional regulation in bacteria and in developing embryos, to evolution. In all of these examples, synthetic biology provides the opportunity to turn cells into the equivalent of a test tube, where biological phenomena can be reconstituted and our theoretical understanding put to test with the same ease that these same phenomena can be studied in the in vitro setting.

The main tenet of physical biology is that biological phenomena can be subject to the same quantitative and predictive understanding that physics has afforded in the context of inanimate matter. However, the inherent complexity of many of these biological processes often leads to the derivation of complex theoretical descriptions containing a plethora of unknown parameters. Such complex descriptions pose a conceptual challenge to the establishment of a solid basis for predictive biology. In this article, we present various exciting examples of how synthetic biology can be used to simplify biological systems and distill these phenomena down to their essential features as a means to enable their theoretical description. Here, synthetic biology goes beyond previous efforts to engineer nature and becomes a tool to bend nature to understand it. We discuss various recent and classic experiments featuring applications of this synthetic approach to the elucidation of problems ranging from bacteriophage infection, to transcriptional regulation in bacteria and in developing embryos, to evolution. In all of these examples, synthetic biology provides the opportunity to turn cells into the equivalent of a test tube, where biological phenomena can be reconstituted and our theoretical understanding put to test with the same ease that these same phenomena can be studied in the in vitro setting.

Embryonic cell fates are defined by transcription factors that are rapidly deployed, yet attempts to visualize these factors in vivo often fail because of slow fluorescent protein maturation. Here, we pioneer a protein tag, LlamaTag, which circumvents this maturation limit by binding mature fluorescent proteins, making it possible to visualize transcription factor concentration dynamics in live embryos. Implementing this approach in the fruit fly Drosophila melanogaster, we discovered stochastic bursts in the concentration of transcription factors that are correlated with bursts in transcription. We further used LlamaTags to show that the concentration of protein in a given nucleus heavily depends on transcription of that gene in neighboring nuclei; we speculate that this inter-nuclear signaling is an important mechanism for coordinating gene expression to delineate straight and sharp boundaries of gene expression. Thus, LlamaTags now make it possible to visualize the flow of information along the central dogma in live embryos.

Embryonic cell fates are defined by transcription factors that are rapidly deployed, yet attempts to visualize these factors in vivo often fail because of slow fluorescent protein maturation. Here, we pioneer a protein tag, LlamaTag, which circumvents this maturation limit by binding mature fluorescent proteins, making it possible to visualize transcription factor concentration dynamics in live embryos. Implementing this approach in the fruit fly Drosophila melanogaster, we discovered stochastic bursts in the concentration of transcription factors that are correlated with bursts in transcription. We further used LlamaTags to show that the concentration of protein in a given nucleus heavily depends on transcription of that gene in neighboring nuclei; we speculate that this inter-nuclear signaling is an important mechanism for coordinating gene expression to delineate straight and sharp boundaries of gene expression. Thus, LlamaTags now make it possible to visualize the flow of information along the central dogma in live embryos.

During development, stochastic promoter switching between active and inactive states results in transcriptional bursts. We tested whether burst kinetics are sufficient to quantitatively recapitulate the formation of patterns of accumulated mRNA in Drosophila embryos by dissecting the transcriptional dynamics of even-skipped stripe 2. Using a novel memory-adjusted hidden Markov model, single-cell live imaging and theoretical modeling, we show that the regulation of bursting in space and time alone is insufficient to predict stripe formation. In addition to bursting, we discovered that the duration of the window of time over which genes transcribe is regulated, and that this binary (on/off) control of where and when gene expression occurs, not transcriptional bursting, is the main regulatory strategy governing stripe formation. Thus, a quantitative description of the regulation of both bursting and the transcriptional time window are necessary to capture the full complement of molecular rules governing the transcriptional control of pattern formation.

Recent decades have seen dramatic advances in our ability to make quantitative measurements of the level of gene expression in organisms of all types. The data resulting from these experiments has raised the need for quantitative models that go beyond the verbal and cartoon-level descriptions that have been so useful in developing a qualitative picture of the nature of gene expression. The improvement in our quantitative description of regulatory networks and our corresponding ability to rewire these networks at will has led many to argue for an analogy between biological regulatory networks and their electronic counterparts. In the electronic setting, we can predict the output current given knowledge of the input voltage and the parameters characterizing the circuit. However, this has so far been nothing more than a hopeful analogy since the input-output functions of most quantitative models of transcriptional regulation are based on phenomenological fits with little-to-no connection to the microscopic parameters of the system. This thesis sharpens this analogy by presenting an integrated approach to understanding transcriptional regulation in bacteria in terms of the microscopic parameters involved in the decision-making processes. This is achieved by a three-pronged approach consisting of theoretical models, in vivo measurements and single-molecule experiments in vitro. The theoretical analysis is based upon two different families of models aimed at describing the output of several regulatory architectures as a function of their input parameters. Thermodynamic models of transcriptional regulation are used to predict the mean level of gene expression of several bacterial promoter architectures as a function of the concentration of the intervening regulatory proteins and their binding energies to DNA and to the associated transcriptional machinery. In recent years, however, an increasing body of work has been performed where levels of gene expression are

Recent decades have seen dramatic advances in our ability to make quantitative measurements of the level of gene expression in organisms of all types. The data resulting from these experiments has raised the need for quantitative models that go beyond the verbal and cartoon-level descriptions that have been so useful in developing a qualitative picture of the nature of gene expression. The improvement in our quantitative description of regulatory networks and our corresponding ability to rewire these networks at will has led many to argue for an analogy between biological regulatory networks and their electronic counterparts. In the electronic setting, we can predict the output current given knowledge of the input voltage and the parameters characterizing the circuit. However, this has so far been nothing more than a hopeful analogy since the input-output functions of most quantitative models of transcriptional regulation are based on phenomenological fits with little-to-no connection to the microscopic parameters of the system. This thesis sharpens this analogy by presenting an integrated approach to understanding transcriptional regulation in bacteria in terms of the microscopic parameters involved in the decision-making processes. This is achieved by a three-pronged approach consisting of theoretical models, in vivo measurements and single-molecule experiments in vitro. The theoretical analysis is based upon two different families of models aimed at describing the output of several regulatory architectures as a function of their input parameters. Thermodynamic models of transcriptional regulation are used to predict the mean level of gene expression of several bacterial promoter architectures as a function of the concentration of the intervening regulatory proteins and their binding energies to DNA and to the associated transcriptional machinery. In recent years, however, an increasing body of work has been performed where levels of gene expression are

Recent decades have seen dramatic advances in our ability to make quantitative measurements of the level of gene expression in organisms of all types. The data resulting from these experiments has raised the need for quantitative models that go beyond the verbal and cartoon-level descriptions that have been so useful in developing a qualitative picture of the nature of gene expression. The improvement in our quantitative description of regulatory networks and our corresponding ability to rewire these networks at will has led many to argue for an analogy between biological regulatory networks and their electronic counterparts. In the electronic setting, we can predict the output current given knowledge of the input voltage and the parameters characterizing the circuit. However, this has so far been nothing more than a hopeful analogy since the input-output functions of most quantitative models of transcriptional regulation are based on phenomenological fits with little-to-no connection to the microscopic parameters of the system. This thesis sharpens this analogy by presenting an integrated approach to understanding transcriptional regulation in bacteria in terms of the microscopic parameters involved in the decision-making processes. This is achieved by a three-pronged approach consisting of theoretical models, in vivo measurements and single-molecule experiments in vitro. The theoretical analysis is based upon two different families of models aimed at describing the output of several regulatory architectures as a function of their input parameters. Thermodynamic models of transcriptional regulation are used to predict the mean level of gene expression of several bacterial promoter architectures as a function of the concentration of the intervening regulatory proteins and their binding energies to DNA and to the associated transcriptional machinery. In recent years, however, an increasing body of work has been performed where levels of gene expression are

Metazoan genes are embedded in a rich milieu of regulatory information that often includes multiple enhancers possessing overlapping activities. In this study, we employ quantitative live imaging methods to assess the function of pairs of primary and shadow enhancers in the regulation of key patterning genes-knirps, hunchback, and snail-in developing Drosophila embryos. The knirps enhancers exhibit additive, sometimes even super-additive activities, consistent with classical gene fusion studies. In contrast, the hunchback enhancers function sub-additively in anterior regions containing saturating levels of the Bicoid activator, but function additively in regions where there are diminishing levels of the Bicoid gradient. Strikingly sub-additive behavior is also observed for snail, whereby removal of the proximal enhancer causes a significant increase in gene expression. Quantitative modeling of enhancer-promoter interactions suggests that weakly active enhancers function additively while strong enhancers behave sub-additively due to competition with the target promoter.

Metazoan genes are embedded in a rich milieu of regulatory information that often includes multiple enhancers possessing overlapping activities. In this study, we employ quantitative live imaging methods to assess the function of pairs of primary and shadow enhancers in the regulation of key patterning genes-knirps, hunchback, and snail-in developing Drosophila embryos. The knirps enhancers exhibit additive, sometimes even super-additive activities, consistent with classical gene fusion studies. In contrast, the hunchback enhancers function sub-additively in anterior regions containing saturating levels of the Bicoid activator, but function additively in regions where there are diminishing levels of the Bicoid gradient. Strikingly sub-additive behavior is also observed for snail, whereby removal of the proximal enhancer causes a significant increase in gene expression. Quantitative modeling of enhancer-promoter interactions suggests that weakly active enhancers function additively while strong enhancers behave sub-additively due to competition with the target promoter.

We present the use of recently developed live imaging methods to examine the dynamic regulation of even-skipped (eve) stripe 2 expression in the precellular Drosophila embryo. Nascent transcripts were visualized via MS2 RNA stem loops. The eve stripe 2 transgene exhibits a highly dynamic pattern of de novo transcription, beginning with a broad domain of expression during nuclear cycle 12 (nc12), and progressive refinement during nc13 and nc14. The mature stripe 2 pattern is surprisingly transient, constituting just ∼15 min of the ∼90-min period of expression. Nonetheless, this dynamic transcription profile faithfully predicts the limits of the mature stripe visualized by conventional in situ detection methods. Analysis of individual transcription foci reveals intermittent bursts of de novo transcription, with duration cycles of 4-10 min. We discuss a multistate model of transcription regulation and speculate on its role in the dynamic repression of the eve stripe 2 expression pattern during development.

We present the use of recently developed live imaging methods to examine the dynamic regulation of even-skipped (eve) stripe 2 expression in the precellular Drosophila embryo. Nascent transcripts were visualized via MS2 RNA stem loops. The eve stripe 2 transgene exhibits a highly dynamic pattern of de novo transcription, beginning with a broad domain of expression during nuclear cycle 12 (nc12), and progressive refinement during nc13 and nc14. The mature stripe 2 pattern is surprisingly transient, constituting just ∼15 min of the ∼90-min period of expression. Nonetheless, this dynamic transcription profile faithfully predicts the limits of the mature stripe visualized by conventional in situ detection methods. Analysis of individual transcription foci reveals intermittent bursts of de novo transcription, with duration cycles of 4-10 min. We discuss a multistate model of transcription regulation and speculate on its role in the dynamic repression of the eve stripe 2 expression pattern during development.

A canonical quantitative view of transcriptional regulation holds that the only role of operator sequence is to set the probability of transcription factor binding, with operator occupancy determining the level of gene expression. In this work, we test this idea by characterizing repression in vivo and the binding of RNA polymerase in vitro in experiments where operators of various sequences were placed either upstream or downstream from the promoter in Escherichia coli. Surprisingly, we find that operators with a weaker binding affinity can yield higher repression levels than stronger operators. Repressor bound to upstream operators modulates promoter escape, and the magnitude of this modulation is not correlated with the repressor-operator binding affinity. This suggests that operator sequences may modulate transcription by altering the nature of the interaction of the bound transcription factor with the transcriptional machinery, implying a new layer of sequence dependence that must be confronted in the quantitative understanding of gene expression., National Institutes of Health (U.S.) (Grant GM81648), National Institutes of Health (U.S.) (Grant GM43369)

There is a long and rich tradition of using ideas from both equilibrium thermodynamics and its microscopic partner theory of equilibrium statistical mechanics. In this chapter, we provide some background on the origins of the seemingly unreasonable effectiveness of ideas from both thermodynamics and statistical mechanics in biology. After making a description of these foundational issues, we turn to a series of case studies primarily focused on binding that are intended to illustrate the broad biological reach of equilibrium thinking in biology. These case studies include ligand-gated ion channels, thermodynamic models of transcription, and recent applications to the problem of bacterial chemotaxis. As part of the description of these case studies, we explore a number of different uses of the famed Monod–Wyman–Changeux (MWC) model as a generic tool for providing a mathematical characterization of two-state systems. These case studies should provide a template for tailoring equilibrium ideas to other problems of biological interest.