A mathematical model for synergistic eukaryotic gene activation

Gene Regulation in and out of Equilibrium

Determining whether and how a gene is transcribed are two of the central processes of life. The conceptual basis for understanding such gene regulation arose from pioneering biophysical studies in eubacteria. However, eukaryotic genomes exhibit vastly greater complexity, which raises questions not addressed by this bacterial paradigm. First, how is information integrated from many widely separated binding sites to determine how a gene is transcribed? Second, does the presence of multiple energy-expending mechanisms, which are absent from eubacterial genomes, indicate that eukaryotes are capable of improved forms of genetic information processing? An updated biophysical foundation is needed to answer such questions. We describe the linear framework, a graph-based approach to Markov processes, and show that it can accommodate many previous studies in the field. Under the assumption of thermodynamic equilibrium, we introduce a language of higher-order cooperativities and show how it can rigorously quantify gene regulatory properties suggested by experiment. We point out that fundamental limits to information processing arise at thermodynamic equilibrium and can only be bypassed through energy expenditure. Finally, we outline some of the mathematical challenges that must be overcome to construct an improved biophysical understanding of gene regulation.

Design of Synthetic Promoters for Gene Circuits in Mammalian Cells

Synthetic biology, the synthesis of engineering and biology, has rapidly matured and has dramatically increased the complexity of artificial gene circuits in recent years. The deployment of intricate synthetic gene circuits in mammalian cells requires the establishment of very precise and orthogonal control of transgene expression. In this chapter, we describe methods of modulating the expression of transgenes at the transcriptional level. Using cAMP-response element-binding protein (CREB)-dependent promoters as examples, a tool for the precise tuning of gene expression by using different core promoters and by varying the binding affinity of transcription factor operator sites is described.

Combinatorial Gene Regulation through Kinetic Control of the Transcription Cycle

Cells decide when, where, and to what level to express their genes by "computing" information from transcription factors (TFs) binding to regulatory DNA. How is the information contained in multiple TF-binding sites integrated to dictate the rate of transcription? The dominant conceptual and quantitative model is that TFs combinatorially recruit one another and RNA polymerase to the promoter by direct physical interactions. Here, we develop a quantitative framework to explore kinetic control, an alternative model in which combinatorial gene regulation can result from TFs working on different kinetic steps of the transcription cycle. Kinetic control can generate a wide range of analog and Boolean computations without requiring the input TFs to be simultaneously bound to regulatory DNA. We propose experiments that will illuminate the role of kinetic control in transcription and discuss implications for deciphering the cis-regulatory "code."

Analytic approaches to stochastic gene expression in multicellular systems

Deterministic thermodynamic models of the complex systems, which control gene expression in metazoa, are helping researchers identify fundamental themes in the regulation of transcription. However, quantitative single cell studies are increasingly identifying regulatory mechanisms that control variability in expression. Such behaviors cannot be captured by deterministic models and are poorly suited to contemporary stochastic approaches that rely on continuum approximations, such as Langevin methods. Fortunately, theoretical advances in the modeling of transcription have assembled some general results that can be readily applied to systems being explored only through a deterministic approach. Here, I review some of the recent experimental evidence for the importance of genetically regulating stochastic effects during embryonic development and discuss key results from Markov theory that can be used to model this regulation. I then discuss several pairs of regulatory mechanisms recently investigated through a Markov approach. In each case, a deterministic treatment predicts no difference between the mechanisms, but the statistical treatment reveals the potential for substantially different distributions of transcriptional activity. In this light, features of gene regulation that seemed needlessly complex evolutionary baggage may be appreciated for their key contributions to reliability and precision of gene expression.

Taking into account nucleosomes for predicting gene expression

The eukaryotic genome is organized in a chain of nucleosomes that consist of 145-147 bp of DNA wrapped around a histone octamer protein core. Binding of transcription factors (TF) to nucleosomal DNA is frequently impeded, which makes it a challenging task to calculate TF occupancy at a given regulatory genomic site for predicting gene expression. Here, we review methods to calculate TF binding to DNA in the presence of nucleosomes. The main theoretical problems are (i) the computation speed that is becoming a bottleneck when partial unwrapping of DNA from the nucleosome is considered, (ii) the perturbation of the binding equilibrium by the activity of ATP-dependent chromatin remodelers, which translocate nucleosomes along the DNA, and (iii) the model parameterization from high-throughput sequencing data and fluorescence microscopy experiments in living cells. We discuss strategies that address these issues to efficiently compute transcription factor binding in chromatin.

Catching transcriptional regulation by thermostatistical modeling

Gene expression is frequently regulated by multiple transcription factors (TFs). Thermostatistical methods allow for a quantitative description of interactions between TFs, RNA polymerase and DNA, and their impact on the transcription rates. We illustrate three different scales of the thermostatistical approach: the microscale of TF molecules, the mesoscale of promoter energy levels and the macroscale of transcriptionally active and inactive cells in a cell population. We demonstrate versatility of combinatorial transcriptional activation by exemplifying logic functions, such as AND and OR gates. We discuss a metric for cell-to-cell transcriptional activation variability known as Fermi entropy. Suitability of thermostatistical modeling is illustrated by describing the experimental data on transcriptional induction of NFκB and the c-Fos protein.

Versatility of cooperative transcriptional activation: a thermodynamical modeling analysis for greater-than-additive and less-than-additive effects

We derive a statistical model of transcriptional activation using equilibrium thermodynamics of chemical reactions. We examine to what extent this statistical model predicts synergy effects of cooperative activation of gene expression. We determine parameter domains in which greater-than-additive and less-than-additive effects are predicted for cooperative regulation by two activators. We show that the statistical approach can be used to identify different causes of synergistic greater-than-additive effects: nonlinearities of the thermostatistical transcriptional machinery and three-body interactions between RNA polymerase and two activators. In particular, our model-based analysis suggests that at low transcription factor concentrations cooperative activation cannot yield synergistic greater-than-additive effects, i.e., DNA transcription can only exhibit less-than-additive effects. Accordingly, transcriptional activity turns from synergistic greater-than-additive responses at relatively high transcription factor concentrations into less-than-additive responses at relatively low concentrations. In addition, two types of re-entrant phenomena are predicted. First, our analysis predicts that under particular circumstances transcriptional activity will feature a sequence of less-than-additive, greater-than-additive, and eventually less-than-additive effects when for fixed activator concentrations the regulatory impact of activators on the binding of RNA polymerase to the promoter increases from weak, to moderate, to strong. Second, for appropriate promoter conditions when activator concentrations are increased then the aforementioned re-entrant sequence of less-than-additive, greater-than-additive, and less-than-additive effects is predicted as well. Finally, our model-based analysis suggests that even for weak activators that individually induce only negligible increases in promoter activity, promoter activity can exhibit greater-than-additive responses when transcription factors and RNA polymerase interact by means of three-body interactions. Overall, we show that versatility of transcriptional activation is brought about by nonlinearities of transcriptional response functions and interactions between transcription factors, RNA polymerase and DNA.

Precise temporal control of the eye regulatory gene Pax6 via enhancer-binding site affinity

How transcription factors interpret the cis-regulatory logic encoded within enhancers to mediate quantitative changes in spatiotemporally restricted expression patterns during animal development is not well understood. Pax6 is a dosage-sensitive gene essential for eye development. Here, we identify the Prep1 (pKnox1) transcription factor as a critical dose-dependent upstream regulator of Pax6 expression during lens formation. We show that Prep1 activates the Pax6 lens enhancer by binding to two phylogenetically conserved lower-affinity DNA-binding sites. Finally, we describe a mechanism whereby Pax6 levels are determined by transcriptional synergy of Prep1 bound to the two sites, while timing of enhancer activation is determined by binding site affinity.

Allele interaction--single locus genetics meets regulatory biology

BACKGROUND

Since the dawn of genetics, additive and dominant gene action in diploids have been defined by comparison of heterozygote and homozygote phenotypes. However, these definitions provide little insight into the underlying intralocus allelic functional dependency and thus cannot serve directly as a mediator between genetics theory and regulatory biology, a link that is sorely needed.

METHODOLOGY/PRINCIPAL FINDINGS

We provide such a link by distinguishing between positive, negative and zero allele interaction at the genotype level. First, these distinctions disclose that a biallelic locus can display 18 qualitatively different allele interaction sign motifs (triplets of +, - and 0). Second, we show that for a single locus, Mendelian dominance is not related to heterozygote allele interaction alone, but is actually a function of the degrees of allele interaction in all the three genotypes. Third, we demonstrate how the allele interaction in each genotype is directly quantifiable in gene regulatory models, and that there is a unique, one-to-one correspondence between the sign of autoregulatory feedback loops and the sign of the allele interactions.

CONCLUSION/SIGNIFICANCE

The concept of allele interaction refines single locus genetics substantially, and it provides a direct link between classical models of gene action and gene regulatory biology. Together with available empirical data, our results indicate that allele interaction can be exploited experimentally to identify and explain intricate intra- and inter-locus feedback relationships in eukaryotes.

Akt2 and SGK3 are both determinants of postnatal hair follicle development

SGK3, which previously has been shown to play a key role in hair follicle development in mice, is a member of the AGC family of serine-threonine kinases. Mice lacking SGK3 have abnormal follicle cycling, which begins shortly after birth and ameliorates substantially with age. However, this developmental abnormality is not recapitulated in mice lacking closely related kinases Akt1, Akt2, or Akt3. To examine whether Akt2 interacts with SGK3 in postnatal hair development, we have generated and characterized Akt2/SGK3 double knockouts (DKOs). We find that the DKO mice have a defect in hair growth that is markedly worse than that of SGK3(-/-) mice and does not ameliorate with age. Morphologically, this defect is characterized by accelerated entry into catagen and through anagen, irregular hair follicle orientation, and increased expression of sebaceous glands. The defect is preceded by a profound failure to increase follicle matrix cell nuclear beta-catenin accumulation and proliferation at the onset of morphogenesis. Furthermore, in cultured keratinocytes, transfected Akt2 and SGK3 both stimulate transcription of a beta-catenin-LEF1-dependent reporter gene. Thus, SGK3 and Akt2 both appear to play important roles in postnatal hair follicle morphogenesis, likely because of their redundant regulation of beta-catenin-dependent transcriptional processes, which control hair follicle cell proliferation.

One thousand and one ways of making functionally similar transcriptional enhancers

Expression of most genes is regulated by the interaction of multiple transcription factors with cis-regulatory sequences. Many studies have focused on how changes in promoters and enhancers alter gene expression and phenotype. Recently, Hare et al., using elegant wet and computational approaches uncovered a series of enhancers driving the expression of the even-skipped gene in scavenger flies (Sepsidae). Despite the strong sequence divergence between the enhancers in sepsids and drosophilids, they lead to remarkably similar patterns of gene expression in transgenic Drosophila embryos. This can be explained by the existence of intra-enhancer compensatory mutations and the presence of overlapping/near binding sites for activators and repressors.

Maternal Groucho and bHLH repressors amplify the dose-sensitive X chromosome signal in Drosophila sex determination

In Drosophila, XX embryos are fated to develop as females, and XY embryos as males, because the diplo-X dose of four X-linked signal element genes, XSEs, activates the Sex-lethal establishment promoter, SxlPe, whereas the haplo-X XSE dose leaves SxlPe off. The threshold response of SxlPe to XSE concentrations depends in part on the bHLH repressor, Deadpan, present in equal amounts in XX and XY embryos. We identified canonical and non-canonical DNA-binding sites for Dpn at SxlPe and found that cis-acting mutations in the Dpn-binding sites caused stronger and earlier Sxl expression than did deletion of dpn implicating other bHLH repressors in Sxl regulation. Maternal Hey encodes one such bHLH regulator but the E(spl) locus does not. Elimination of the maternal corepressor Groucho also caused strong ectopic Sxl expression in XY, and premature Sxl activation in XX embryos, but Sxl was still expressed differently in the sexes. Our findings suggest that Groucho and associated maternal and zygotic bHLH repressors define the threshold XSE concentrations needed to activate SxlPe and that they participate directly in sex signal amplification. We present a model in which the XSE signal is amplified by a feedback mechanism that interferes with Gro-mediated repression in XX, but not XY embryos.

Rational design of memory in eukaryotic cells

The ability to logically engineer novel cellular functions promises a deeper understanding of biological systems. Here we demonstrate the rational design of cellular memory in yeast that employs autoregulatory transcriptional positive feedback. We built a set of transcriptional activators and quantitatively characterized their effects on gene expression in living cells. Modeling in conjunction with the quantitative characterization of the activator-promoter pairs accurately predicts the behavior of the memory network. This study demonstrates the power of taking advantage of components with measured quantitative parameters to specify eukaryotic regulatory networks with desired properties.

Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms

BACKGROUND

Genetic variation explains a considerable part of observed phenotypic variation in gene expression networks. This variation has been shown to be located both locally (cis) and distally (trans) to the genes being measured. Here we explore to which degree the phenotypic manifestation of local and distant polymorphisms is a dynamic feature of regulatory design.

RESULTS

By combining mathematical models of gene expression networks with genetic maps and linkage analysis we find that very different network structures and regulatory motifs give similar cis/trans linkage patterns. However, when the shape of the cis-regulatory input functions is more nonlinear or threshold-like, we observe for all networks a dramatic increase in the phenotypic expression of distant compared to local polymorphisms under otherwise equal conditions.

CONCLUSION

Our findings indicate that genetic variation affecting the form of cis-regulatory input functions may reshape the genotype-phenotype map by changing the relative importance of cis and trans variation. Our approach combining nonlinear dynamic models with statistical genetics opens up for a systematic investigation of how functional genetic variation is translated into phenotypic variation under various systemic conditions.

An analytical rate expression for the kinetics of gene transcription mediated by dimeric transcription factors

To model gene transcription kinetics, empirical fitting with the Hill function or S-system is often used. In this study, we derived an analytical expression for gene transcription rates in a manner similar to that developed for enzyme kinetics to describe the kinetics of gene transcription mediated by dimeric transcription factors (TFs) such as Gcn4p, a Saccharomyces cerevisiae master gene regulator. We showed that the analytical rate expression and its parameters estimated from several sets of experimental data could accurately reproduce the experimentally measured promoter-binding activity of Gcn4p. Furthermore, the analytical rate expression allowed us to derive analytically, rather than fit empirically, the parameters of the Hill function and S-system for use in modelling transcription kinetics. We found that a plot of gene transcription rate against Gcn4p concentration gave a sigmoidal dose-response curve with a positive co-operativity Hill coefficient (approximately 1.25), in accordance with previous experimental findings on the promoter binding of dimeric TFs. The characteristics of the dose-response curve around the estimated cellular Gcn4p concentration suggest that transcription regulation is efficiently controlled under physiological conditions. This work is a useful initial step towards analytically modelling and simulating complicated gene transcription networks.

General transfer matrix formalism to calculate DNA-protein-drug binding in gene regulation: application to OR operator of phage lambda

The transfer matrix methodology is proposed as a systematic tool for the statistical-mechanical description of DNA-protein-drug binding involved in gene regulation. We show that a genetic system of several cis-regulatory modules is calculable using this method, considering explicitly the site-overlapping, competitive, cooperative binding of regulatory proteins, their multilayer assembly and DNA looping. In the methodological section, the matrix models are solved for the basic types of short- and long-range interactions between DNA-bound proteins, drugs and nucleosomes. We apply the matrix method to gene regulation at the O(R) operator of phage lambda. The transfer matrix formalism allowed the description of the lambda-switch at a single-nucleotide resolution, taking into account the effects of a range of inter-protein distances. Our calculations confirm previously established roles of the contact CI-Cro-RNAP interactions. Concerning long-range interactions, we show that while the DNA loop between the O(R) and O(L) operators is important at the lysogenic CI concentrations, the interference between the adjacent promoters P(R) and P(RM) becomes more important at small CI concentrations. A large change in the expression pattern may arise in this regime due to anticooperative interactions between DNA-bound RNA polymerases. The applicability of the matrix method to more complex systems is discussed.

Designing biological systems

The design of artificial biological systems and the understanding of their natural counterparts are key objectives of the emerging discipline of synthetic biology. Toward both ends, research in synthetic biology has primarily focused on the construction of simple devices, such as transcription-based oscillators and switches. Construction of such devices should provide us with insight on the design of natural systems, indicating whether our understanding is complete or whether there are still gaps in our knowledge. Construction of simple biological systems may also lay the groundwork for the construction of more complex systems that have practical utility. To realize its full potential, biological systems design borrows from the allied fields of protein design and metabolic engineering. In this review, we describe the scientific accomplishments in this field, as well as its forays into biological part standardization and education of future biological designers.

A web server for transcription factor binding site prediction

Promoter prediction has gained increased attention in studies related to transcriptional regulation of gene expression.We developed a web server named PMSearch (Poly Matrix Search) which utilizes Position Frequency Matrices (PFMs) to predict transcription factor binding sites (TFBSs) in DNA sequences. PMSearch takes PFMs (either user-defined or retrieved from local dataset which currently contains 507 PFMs from Transfac Public 7.0 and JASPAR) and DNA sequences of interest as the input, then scans the DNA sequences with PFMs and reports the sites of high scores as the putative binding sites. The output of the server includes 1) A plot for the distribution of predicted TFBS along the DNA sequence, 2) A table listing location, score and motif for each putative binding site, and 3) Clusters of predicted binding sites. PMSearch also provides links for accessing clusters of PFMs that are similar to the input PFMs to facilitate complicated promoter analysis.

A thermodynamic model of transcriptome formation

The genome supplies information on both the quality and quantity of the transcriptome. However, as it remains unknown how a cell determines transcript levels from the genome sequences, despite comprehensive knowledge of the cellular components involved, the quantity information held by the genome cannot as yet be derived from nucleotide sequences. The model presented here explains on a thermodynamic basis how the components decode the genome to form and maintain the transcriptome. The model describes the level of a transcript as a pseudo-equilibrium between velocities of synthesis and degradation, both of which are controlled by sequence-specific interactions between protein factors and nucleic acids. Each of the transcript levels can be described by a single equation expressing a function of the activity concentrations of the protein factors. Quantitative information in the genome can thus be transformed into constants determined from the nucleotide sequences. Using this model, the transcriptome can be traced back to the protein factors and the state of chromosome packaging. The total description of transcript levels allows the model to be verified through comparison of derived hypotheses with comprehensive measurements of the transcriptome. The hypotheses thus derived in the present study are well supported by experimental microarray data, confirming the appropriateness of the model.

Compensatory cis-trans evolution and the dysregulation of gene expression in interspecific hybrids of Drosophila

Hybrids between species are often characterized by novel gene-expression patterns. A recent study on allele-specific gene expression in hybrids between species of Drosophila revealed cases in which cis- and trans-regulatory elements within species had coevolved in such a way that changes in cis-regulatory elements are compensated by changes in trans-regulatory elements. We hypothesized that such coevolution should often lead to gene misexpression in the hybrid. To test this hypothesis, we estimated allele-specific expression and overall expression levels for 31 genes in D. melanogaster, D. simulans, and their F1 hybrid. We found that 13 genes with cis-trans compensatory evolution are in fact misexpressed in the hybrid. These represent candidate genes whose dysregulation might be the consequence of coevolution of cis- and trans-regulatory elements within species. Using a mathematical model for the regulation of gene expression, we explored the conditions under which cis-trans compensatory evolution can lead to misexpression in interspecific hybrids.

A novel mammalian expression system derived from components coordinating nicotine degradation in arthrobacter nicotinovorans pAO1

We describe the design and detailed characterization of 6-hydroxy-nicotine (6HNic)-adjustable transgene expression (NICE) systems engineered for lentiviral transduction and in vivo modulation of angiogenic responses. Arthrobacter nicotinovorans pAO1 encodes a unique catabolic machinery on its plasmid pAO1, which enables this Gram-positive soil bacterium to use the tobacco alkaloid nicotine as the exclusive carbon source. The 6HNic-responsive repressor-operator (HdnoR-O(NIC)) interaction, controlling 6HNic oxidase production in A.nicotinovorans pAO1, was engineered for generic 6HNic-adjustable transgene expression in mammalian cells. HdnoR fused to different transactivation domains retained its O(NIC)-binding capacity in mammalian cells and reversibly adjusted transgene transcription from chimeric O(NIC)-containing promoters (P(NIC); O(NIC) fused to a minimal eukaryotic promoter [P(min)]) in a 6HNic-responsive manner. The combination of transactivators containing various transactivation domains with promoters differing in the number of operator modules as well as in their relative inter-O(NIC) and/or O(NIC)-P(min) spacing revealed steric constraints influencing overall NICE regulation performance in mammalian cells. Mice implanted with microencapsulated cells engineered for NICE-controlled expression of the human glycoprotein secreted placental alkaline phosphatase (SEAP) showed high SEAP serum levels in the absence of regulating 6HNic. 6HNic was unable to modulate SEAP expression, suggesting that this nicotine derivative exhibits control-incompatible pharmacokinetics in mice. However, chicken embryos transduced with HIV-1-derived self-inactivating lentiviral particles transgenic for NICE-adjustable expression of the human vascular endothelial growth factor 121 (VEGF121) showed graded 6HNic response following administration of different 6HNic concentrations. Owing to the clinically inert and highly water-soluble compound 6HNic, NICE-adjustable transgene control systems may become a welcome alternative to available drug-responsive homologs in basic research, therapeutic cell engineering and biopharmaceutical manufacturing.

GATA-4, GATA-5, and GATA-6 activate the rat liver fatty acid binding protein gene in concert with HNF-1alpha

Transcriptional regulation by GATA-4, GATA-5, and GATA-6 in intestine and liver was explored using a transgene constructed from the proximal promoter of the rat liver fatty acid binding protein gene (Fabpl). An immunohistochemical survey detected GATA-4 and GATA-6 in enterocytes, GATA-6 in hepatocytes, and GATA-5 in neither cell type in adult animals. In cell transfection assays, GATA-4 or GATA-5 but not GATA-6 activated the Fabpl transgene solely through the most proximal of three GATA binding sites in the Fabpl promoter. However, all three factors activated transgenes constructed from each Fabpl site upstream of a minimal viral promoter. GATA factors interact with hepatic nuclear factor (HNF)-1alpha, and the proximal Fabpl GATA site adjoins an HNF-1 site. GATA-4, GATA-5, or GATA-6 bounded to HNF-1alpha in solution, and all cooperated with HNF-1alpha to activate the Fabpl transgene. Mutagenizing all Fabpl GATA sites abrogated transgene activation by GATA factors, but GATA-4 activated the mutagenized transgene in the presence of HNF-1alpha. These in vitro results suggested GATA/HNF-1alpha interactions function in Fabpl regulation, and in vivo relevance was determined with subsequent experiments. In mice, the Fabpl transgene was active in enterocytes and hepatocytes, a transgene with mutagenized HNF-1 site was silent, and a transgene with mutagenized GATA sites had identical expression as the native transgene. Mice mosaic for biallelic Gata4 inactivation lost intestinal but not hepatic Fabpl expression in Gata4-deficient cells but not wild-type cells. These results demonstrate GATA-4 is critical for intestinal gene expression in vivo and suggest a specific GATA-4/HNF-1alpha physical and functional interaction in Fabpl activation.

A nonlinear discrete dynamical model for transcriptional regulation: construction and properties

Transcriptional regulation is a fundamental mechanism of living cells, which allows them to determine their actions and properties, by selectively choosing which proteins to express and by dynamically controlling the amounts of those proteins. In this article, we revisit the problem of mathematically modeling transcriptional regulation. First, we adopt a biologically motivated continuous model for gene transcription and mRNA translation, based on first-order rate equations, coupled with a set of nonlinear equations that model cis-regulation. Then, we view the processes of transcription and translation as being discrete, which, together with the need to use computational techniques for large-scale analysis and simulation, motivates us to model transcriptional regulation by means of a nonlinear discrete dynamical system. Classical arguments from chemical kinetics allow us to specify the nonlinearities underlying cis-regulation and to include both activators and repressors as well as the notion of regulatory modules in our formulation. We show that the steady-state behavior of the proposed discrete dynamical system is identical to that of the continuous model. We discuss several aspects of our model, related to homeostatic and epigenetic regulation as well as to Boolean networks, and elaborate on their significance. Simulations of transcriptional regulation of a hypothetical metabolic pathway illustrate several properties of our model, and demonstrate that a nonlinear discrete dynamical system may be effectively used to model transcriptional regulation in a biologically relevant way.

A sigmoidal transcriptional response: cooperativity, synergy and dosage effects

A sigmoidal transcriptional response (STR) is thought to act as a molecular switch to control gene expression. This nonlinear behaviour arises as a result of the cooperative recognition of a promoter/enhancer by transcription factors (TFs) and/or their synergy to attract the basal transcriptional machinery (BTM). Although this cooperation between TFs is additive in terms of energy, it leads to an exponential increase in affinity between the BTM and the pre-initiation complexes. This exponential increase in the strength of interactions is the principle that governs synergistic systems. Here, I propose a minimalist quasi-equilibrium model to explore qualitatively the STR taking into account cooperative recognition of the promoter/enhancer and synergy. Although the focus is on the effect of activators, a similar treatment can be applied to inhibitors. One of the main insights obtained from the model is that generation of a sigmoidal threshold is possible even in the absence of cooperative DNA binding provided the TFs synergistically interact with the BTM. On the contrary, when there is cooperative binding, the impact of synergy diminishes. It will also be shown that a sigmoidal response to a morphogenetic gradient can be used to generate a nested gradient of another morphogen. Previously, I had proposed that halving the amounts of TFs involved in sigmoidal transcriptional switches could account for the abnormal dominant phenotypes associated with some of these genes. This phenomenon, called haploinsufficiency (HI), has been recognised as the basis of many human diseases. Although a formal proof linking HI and a sigmoidal response is lacking, it is tempting to explore the model from the perspective of dosage effects.

Genetic "code": representations and dynamical models of genetic components and networks

Dynamical modeling of biological systems is becoming increasingly widespread as people attempt to grasp biological phenomena in their full complexity and make sense of an accelerating stream of experimental data. We review a number of recent modeling studies that focus on systems specifically involving gene expression and regulation. These systems include bacterial metabolic operons and phase-variable piliation, bacteriophages T7 and lambda, and interacting networks of eukaryotic developmental genes. A wide range of conceptual and mathematical representations of genetic components and phenomena appears in these works. We discuss these representations in depth and give an overview of the tools currently available for creating and exploring dynamical models. We argue that for modeling to realize its full potential as a mainstream biological research technique the tools must become more general and flexible, and formal, standardized representations of biological knowledge and data must be developed.

The regulatory role for the ERCC3 helicase of general transcription factor TFIIH during promoter escape in transcriptional activation

Eukaryotic transcriptional activators have been proposed to function, for the most part, by promoting the assembly of preinitiation complex through the recruitment of the RNA polymerase II transcriptional machinery to the promoter. Previous studies have shown that transcriptional activation is critically dependent on transcription factor IIH (TFIIH), which functions during promoter opening and promoter escape, the steps following preinitiation complex assembly. Here we have analyzed the role of TFIIH in transcriptional activation and show that the excision repair cross-complementing (ERCC) 3 helicase activity of TFIIH plays a regulatory role to stimulate promoter escape in activated transcription. The stimulatory effect of the ERCC3 helicase is observed until approximately 10-nt RNA is synthesized, and the helicase seems to act throughout the entire course of promoter escape. Analyses of the early phase of transcription show that a majority of the initiated complexes abort transcription and fail to escape the promoter; however, the proportion of productive complexes that escape the promoter apparently increases in response to activation. Our results establish that promoter escape is an important regulatory step stimulated by the ERCC3 helicase activity in response to activation and reveal a possible mechanism of transcriptional synergy.

Exploring the etiology of haploinsufficiency

The focus of this essay is the phenomenon of haploinsufficiency (HI), a manifestation of genetic dominance that arises when only one allele of a normally diploid locus is present. Specifically, I examine the nature of HI for transcription factor genes. Although the concept of HI applies to many such genes, there is a potentially large variety of mechanisms that underlie it. Even when the phenomenon is linked in all cases to reduced absolute gene expression levels, there are several well-documented cases where the explanation is not reduced expression per se but altered stoichiometry. I will discuss the notion of haploimbalance in general and evaluate the property of transcriptional synergy within the context of HI. This kind of non-linear behaviour can probably explain a large proportion of the cases of HI, as well as the variability in most HI phenotypes and the fact that several factors in the same pathway may be dosage sensitive. For the sake of generality, a theoretical analysis of simple non-linear HI systems is also attempted. This article is certainly another preliminary exploration of the complex matters of HI, which remain an intellectual challenge from many points of view.

Identifying regulatory networks by combinatorial analysis of promoter elements

Several computational methods based on microarray data are currently used to study genome-wide transcriptional regulation. Few studies, however, address the combinatorial nature of transcription, a well-established phenomenon in eukaryotes. Here we describe a new approach using microarray data to uncover novel functional motif combinations in the promoters of Saccharomyces cerevisiae. In addition to identifying novel motif combinations that affect expression patterns during the cell cycle, sporulation and various stress responses, we observed regulatory cross-talk among several of these processes. We have also generated motif-association maps that provide a global view of transcription networks. The maps are highly connected, suggesting that a small number of transcription factors are responsible for a complex set of expression patterns in diverse conditions. This approach may be useful for modeling transcriptional regulatory networks in more complex eukaryotes.

Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion

Feedback is a ubiquitous control mechanism of gene networks. Here, we have used positive feedback to construct a synthetic eukaryotic gene switch in Saccharomyces cerevisiae. Within this system, a continuous gradient of constitutively expressed transcriptional activator is translated into a cell phenotype switch when the activator is expressed autocatalytically. This finding is consistent with a mathematical model whose analysis shows that continuous input parameters are converted into a bimodal probability distribution by positive feedback, and that this resembles analog-digital conversion. The autocatalytic switch is a robust property in eukaryotic gene expression. Although the behavior of individual cells within a population is random, the proportion of the cell population displaying either low or high expression states can be regulated. These results have implications for understanding the graded and probabilistic mechanisms of enhancer action and cell differentiation.

A common motif within the negative regulatory regions of multiple factors inhibits their transcriptional synergy

DNA regulatory elements frequently harbor multiple recognition sites for several transcriptional activators. The response mounted from such compound response elements is often more pronounced than the simple sum of effects observed at single binding sites. The determinants of such transcriptional synergy and its control, however, are poorly understood. Through a genetic approach, we have uncovered a novel protein motif that limits the transcriptional synergy of multiple DNA-binding regulators. Disruption of these conserved synergy control motifs (SC motifs) selectively increases activity at compound, but not single, response elements. Although isolated SC motifs do not regulate transcription when tethered to DNA, their transfer to an activator lacking them is sufficient to impose limits on synergy. Mechanistic analysis of the two SC motifs found in the glucocorticoid receptor N-terminal region reveals that they function irrespective of the arrangement of the receptor binding sites or their distance from the transcription start site. Proper function, however, requires the receptor's ligand-binding domain and an engaged dimer interface. Notably, the motifs are not functional in yeast and do not alter the effect of p160 coactivators, suggesting that they require other nonconserved components to operate. Many activators across multiple classes harbor seemingly unrelated negative regulatory regions. The presence of SC motifs within them, however, suggests a common function and identifies SC motifs as critical elements of a general mechanism to modulate higher-order interactions among transcriptional regulators.

Mathematical modelling of metabolism

Mathematical models of the cellular metabolism have a special interest within biotechnology. Many different kinds of commercially important products are derived from the cell factory, and metabolic engineering can be applied to improve existing production processes, as well as to make new processes available. Both stoichiometric and kinetic models have been used to investigate the metabolism, which has resulted in defining the optimal fermentation conditions, as well as in directing the genetic changes to be introduced in order to obtain a good producer strain or cell line. With the increasing availability of genomic information and powerful analytical techniques, mathematical models also serve as a tool for understanding the cellular metabolism and physiology.