Influence of catabolite repression and inducer exclusion on the bistable behavior of the lac operon

The phosphotransferase system gene ptsH affects persister formation in Klebsiella pneumoniae by regulating cyclic adenosine monophosphate levels

Klebsiella pneumoniae is one of the most common opportunistic pathogens causing hospital- and community-acquired infections. Antibiotic resistance in K. pneumoniae has emerged as a major clinical and public health threat. Persisters are specific antibiotic-tolerant bacterial cells. Studies on the mechanism underlying their formation mechanism and growth status are scarce. Therefore, it is urgent to explore the key genes and signalling pathways involved in the formation and recovery process of K. pneumoniae persisters to enhance the understanding and develop relevant treatment strategies. In this study, we treated K. pneumoniae with a lethal concentration of levofloxacin. It resulted in a distinct plateau of surviving levofloxacin-tolerant persisters. Subsequently, we obtained bacterial samples at five different time points during the formation and recovery of K. pneumoniae persisters to perform transcriptome analysis. ptsH gene was observed to be upregulated during the formation of persisters, and down-regulated during the recovery of the persisters. Further, we used CRISPR-Cas9 to construct ΔptsH, the ptsH-knockout K. pneumoniae strain, and to investigate the effect of ptsH on the persister formation. We observed that ptsH can promote the formation of persisters, reduce accumulation of reactive oxygen species, and enhance antioxidant capacity by reducing cyclic adenosine monophosphate (cAMP) levels. To the best of our knowledge, this is the first study to report that ptsH plays a vital role in forming K. pneumoniae persisters. This study provided important insights to further explore the mechanism underlying the formation of K. pneumoniae persisters and provided a potential target for treating infection with K. pneumoniae persisters.

Mechanistic aspects of IPTG (isopropylthio-β-galactoside) transport across the cytoplasmic membrane of Escherichia coli-a rate limiting step in the induction of recombinant protein expression

Coupling transcription of a cloned gene to the lac operon with induction by isopropylthio-β-galactoside (IPTG) has been a favoured approach for recombinant protein expression using Escherichia coli as a heterologous host for more than six decades. Despite a wealth of experimental data gleaned over this period, a quantitative relationship between extracellular IPTG concentration and consequent levels of recombinant protein expression remains surprisingly elusive across a broad spectrum of experimental conditions. This is because gene expression under lac operon regulation is tightly correlated with intracellular IPTG concentration due to allosteric regulation of the lac repressor protein (lacY). An in-silico mathematical model established that uptake of IPTG across the cytoplasmic membrane of E. coli by simple diffusion was negligible. Conversely, lacY mediated active transport was a rapid process, taking only some seconds for internal and external IPTG concentrations to equalize. Optimizing kcat and KM parameters by targeted mutation of the galactoside binding site in lacY could be a future strategy to improve the performance of recombinant protein expression. For example, if kcat were reduced whilst KM was increased, active transport of IPTG across the cytoplasmic membrane would be reduced, thereby lessening the metabolic burden on the cell and expediating accumulation of recombinant protein. The computational model described herein is made freely available and is amenable to optimize recombinant protein expression in other heterologous hosts.

ONE-SENTENCE SUMMARY

A computational model made freely available to optimize recombinant protein expression in Escherichia coli other heterologous hosts.

The potential of cold-shock promoters for the expression of recombinant proteins in microbes and mammalian cells

BACKGROUND

Low-temperature expression of recombinant proteins may be advantageous to support their proper folding and preserve bioactivity. The generation of expression vectors regulated under cold conditions can improve the expression of some target proteins that are difficult to express in different expression systems. The cspA encodes the major cold-shock protein from Escherichia coli (CspA). The promoter of cspA has been widely used to develop cold shock-inducible expression platforms in E. coli. Moreover, it is often necessary to employ expression systems other than bacteria, particularly when recombinant proteins require complex post-translational modifications. Currently, there are no commercial platforms available for expressing target genes by cold shock in eukaryotic cells. Consequently, genetic elements that respond to cold shock offer the possibility of developing novel cold-inducible expression platforms, particularly suitable for yeasts, and mammalian cells.

CONCLUSIONS

This review covers the importance of the cellular response to low temperatures and the prospective use of cold-sensitive promoters to direct the expression of recombinant proteins. This concept may contribute to renewing interest in applying white technologies to produce recombinant proteins that are difficult to express.

Lac Operon Boolean Models: Dynamical Robustness and Alternative Improvements

In Veliz-Cuba and Stigler 2011, Boolean models were proposed for the lac operon in Escherichia coli capable of reproducing the operon being OFF, ON and bistable for three (low, medium and high) and two (low and high) parameters, representing the concentration ranges of lactose and glucose, respectively. Of these 6 possible combinations of parameters, 5 produce results that match with the biological experiments of Ozbudak et al., 2004. In the remaining one, the models predict the operon being OFF while biological experiments show a bistable behavior. In this paper, we first explore the robustness of two such models in the sense of how much its attractors change against any deterministic update schedule. We prove mathematically that, in cases where there is no bistability, all the dynamics in both models lack limit cycles while, when bistability appears, one model presents 30% of its dynamics with limit cycles while the other only 23%. Secondly, we propose two alternative improvements consisting of biologically supported modifications; one in which both models match with Ozbudak et al., 2004 in all 6 combinations of parameters and, the other one, where we increase the number of parameters to 9, matching in all these cases with the biological experiments of Ozbudak et al., 2004.

cAMP Biosensors Based on Genetically Encoded Fluorescent/Luminescent Proteins

Cyclic adenosine monophosphate (cAMP) plays a key role in signal transduction pathways as a second messenger. Studies on the cAMP dynamics provided useful scientific insights for drug development and treatment of cAMP-related diseases such as some cancers and prefrontal cortex disorders. For example, modulation of cAMP-mediated intracellular signaling pathways by anti-tumor drugs could reduce tumor growth. However, most early stage tools used for measuring the cAMP level in living organisms require cell disruption, which is not appropriate for live cell imaging or animal imaging. Thus, in the last decades, tools were developed for real-time monitoring of cAMP distribution or signaling dynamics in a non-invasive manner. Genetically-encoded sensors based on fluorescent proteins and luciferases could be powerful tools to overcome these drawbacks. In this review, we discuss the recent genetically-encoded cAMP sensors advances, based on single fluorescent protein (FP), Föster resonance energy transfer (FRET), single luciferase, and bioluminescence resonance energy transfer (BRET) for real-time non-invasive imaging.

Co-culture of Lactobacillus delbrueckii and engineered Lactococcus lactis enhances stoichiometric yield of D-lactic acid from whey permeate

D-Lactic acid (D-LA) is an enantiomer of lactic acid, which has a niche application in synthesis of poly-lactic acid based (PLA) polymer owing to its contribution to the thermo-stability of stereo-complex PLA polymer. Utilization of renewable substrates such as whey permeate is pivotal to economically viable production of D-LA. In present work, we have demonstrated D-LA production from whey permeate by Lactobacillus delbrueckii and engineered Lactococcus lactis. We observed that lactose fermentation by a monoculture of L. delbrueckii yields D-LA and galactose as major products. The highest yield of D-LA obtained was 0.48 g g^-1 when initial lactose concentration was 30 g L^-1. Initial lactose concentration beyond 20 g L^-1 resulted in accumulation of glucose and galactose, and hence, reduced the stoichiometric yield of D-LA. L. lactis naturally produces L-lactic acid (L-LA), so a mutant strain of L. lactis (L. lactis Δldh ΔldhB ΔldhX) was used to prevent L-LA production and engineer it for D-LA production. Heterologous over-expression of D-lactate dehydrogenase (ldhA) in the recombinant strain L. lactis TSG1 resulted in 0.67 g g^-1 and 0.44 g g^-1 of D-LA yield from lactose and galactose, respectively. Co-expression of galactose permease (galP) and α-phosphoglucomutase (pgmA) with ldhA in the recombinant strain L. lactis TSG3 achieved a D-LA yield of 0.92 g g^-1 from galactose. A co-culture batch process of L. delbrueckii and L. lactis TSG3 achieved an enhanced stoichiometric yield of 0.90 g g^-1 and ~45 g L^-1D-LA from whey permeate (lactose). This is the highest reported yield of D-LA from lactose substrate, and the titres can be improved further by a suitably designed fed-batch co-culture process.

Simulation and prediction of protein production in fed-batch E. coli cultures: An engineering approach

An overall model describing the dynamic behavior of fed-batch E. coli processes for protein production has been built, calibrated and validated. Using a macroscopic approach, the model consists of three interconnected blocks allowing simulation of biomass, inducer and protein concentration profiles with time. The model incorporates calculation of the extra and intracellular inducer concentration, as well as repressor-inducer dynamics leading to a successful prediction of the product concentration. The parameters of the model were estimated using experimental data of a rhamnulose-1-phosphate aldolase-producer strain, grown under a wide range of experimental conditions. After validation, the model has successfully predicted the behavior of different strains producing two different proteins: fructose-6-phosphate aldolase and ω-transaminase. In summary, the presented approach represents a powerful tool for E. coli production process simulation and control.

PROKARYO: an illustrative and interactive computational model of the lactose operon in the bacterium Escherichia coli

Background

We are creating software for agent-based simulation and visualization of bio-molecular processes in bacterial and eukaryotic cells. As a first example, we have built a 3-dimensional, interactive computer model of an Escherichia coli bacterium and its associated biomolecular processes. Our illustrative model focuses on the gene regulatory processes that control the expression of genes involved in the lactose operon. Prokaryo, our agent-based cell simulator, incorporates cellular structures, such as plasma membranes and cytoplasm, as well as elements of the molecular machinery, including RNA polymerase, messenger RNA, lactose permease, and ribosomes.

Results

The dynamics of cellular ’agents’ are defined by their rules of interaction, implemented as finite state machines. The agents are embedded within a 3-dimensional virtual environment with simulated physical and electrochemical properties. The hybrid model is driven by a combination of (1) mathematical equations (DEQs) to capture higher-scale phenomena and (2) agent-based rules to implement localized interactions among a small number of molecular elements. Consequently, our model is able to capture phenomena across multiple spatial scales, from changing concentration gradients to one-on-one molecular interactions.

We use the classic gene regulatory mechanism of the lactose operon to demonstrate our model’s resolution, visual presentation, and real-time interactivity. Our agent-based model expands on a sophisticated mathematical E. coli metabolism model, through which we highlight our model’s scientific validity.

Conclusion

We believe that through illustration and interactive exploratory learning a model system like Prokaryo can enhance the general understanding and perception of biomolecular processes. Our agent-DEQ hybrid modeling approach can also be of value to conceptualize, illustrate, and—eventually—validate cell experiments in the wet lab.

Effect of Intrinsic Noise on the Phenotype of Cell Populations Featuring Solution Multiplicity: An Artificial lac Operon Network Paradigm

Heterogeneity in cell populations originates from two fundamentally different sources: the uneven distribution of intracellular content during cell division, and the stochastic fluctuations of regulatory molecules existing in small amounts. Discrete stochastic models can incorporate both sources of cell heterogeneity with sufficient accuracy in the description of an isogenic cell population; however, they lack efficiency when a systems level analysis is required, due to substantial computational requirements. In this work, we study the effect of cell heterogeneity in the behaviour of isogenic cell populations carrying the genetic network of lac operon, which exhibits solution multiplicity over a wide range of extracellular conditions. For such systems, the strategy of performing solely direct temporal solutions is a prohibitive task, since a large ensemble of initial states needs to be tested in order to drive the system--through long time simulations--to possible co-existing steady state solutions. We implement a multiscale computational framework, the so-called "equation-free" methodology, which enables the performance of numerical tasks, such as the computation of coarse steady state solutions and coarse bifurcation analysis. Dynamically stable and unstable solutions are computed and the effect of intrinsic noise on the range of bistability is efficiently investigated. The results are compared with the homogeneous model, which neglects all sources of heterogeneity, with the deterministic cell population balance model, as well as with a stochastic model neglecting the heterogeneity originating from intrinsic noise effects. We show that when the effect of intrinsic source of heterogeneity is intensified, the bistability range shifts towards higher extracellular inducer concentration values.

Global sensitivity analysis of a dynamic model for gene expression in Drosophila embryos

It is well known that gene regulation is a tightly controlled process in early organismal development. However, the roles of key processes involved in this regulation, such as transcription and translation, are less well understood, and mathematical modeling approaches in this field are still in their infancy. In recent studies, biologists have taken precise measurements of protein and mRNA abundance to determine the relative contributions of key factors involved in regulating protein levels in mammalian cells. We now approach this question from a mathematical modeling perspective. In this study, we use a simple dynamic mathematical model that incorporates terms representing transcription, translation, mRNA and protein decay, and diffusion in an early Drosophila embryo. We perform global sensitivity analyses on this model using various different initial conditions and spatial and temporal outputs. Our results indicate that transcription and translation are often the key parameters to determine protein abundance. This observation is in close agreement with the experimental results from mammalian cells for various initial conditions at particular time points, suggesting that a simple dynamic model can capture the qualitative behavior of a gene. Additionally, we find that parameter sensitivites are temporally dynamic, illustrating the importance of conducting a thorough global sensitivity analysis across multiple time points when analyzing mathematical models of gene regulation.

Determining the bistability parameter ranges of artificially induced lac operon using the root locus method

This paper employs the root locus method to conduct a detailed investigation of the parameter regions that ensure bistability in a well-studied gene regulatory network namely, lac operon of Escherichia coli (E. coli). In contrast to previous works, the parametric bistability conditions observed in this study constitute a complete set of necessary and sufficient conditions. These conditions were derived by applying the root locus method to the polynomial equilibrium equation of the lac operon model to determine the parameter values yielding the multiple real roots necessary for bistability. The lac operon model used was defined as an ordinary differential equation system in a state equation form with a rational right hand side, and it was compatible with the Hill and Michaelis-Menten approaches of enzyme kinetics used to describe biochemical reactions that govern lactose metabolism. The developed root locus method can be used to study the steady-state behavior of any type of convergent biological system model based on mass action kinetics. This method provides a solution to the problem of analyzing gene regulatory networks under parameter uncertainties because the root locus method considers the model parameters as variable, rather than fixed. The obtained bistability ranges for the lac operon model parameters have the potential to elucidate the appearance of bistability for E. coli cells in in vivo experiments, and they could also be used to design robust hysteretic switches in synthetic biology.

The utility of simple mathematical models in understanding gene regulatory dynamics

In this review, we survey work that has been carried out in the attempts of biomathematicians to understand the dynamic behaviour of simple bacterial operons starting with the initial work of the 1960's. We concentrate on the simplest of situations, discussing both repressible and inducible systems and then turning to concrete examples related to the biology of the lactose and tryptophan operons. We conclude with a brief discussion of the role of both extrinsic noise and so-called intrinsic noise in the form of translational and/or transcriptional bursting.

The influence of transcription factor competition on the relationship between occupancy and affinity

Transcription factors (TFs) are proteins that bind to specific sites on the DNA and regulate gene activity. Identifying where TF molecules bind and how much time they spend on their target sites is key to understanding transcriptional regulation. It is usually assumed that the free energy of binding of a TF to the DNA (the affinity of the site) is highly correlated to the amount of time the TF remains bound (the occupancy of the site). However, knowing the binding energy is not sufficient to infer actual binding site occupancy. This mismatch between the occupancy predicted by the affinity and the observed occupancy may be caused by various factors, such as TF abundance, competition between TFs or the arrangement of the sites on the DNA. We investigated the relationship between the affinity of a TF for a set of binding sites and their occupancy. In particular, we considered the case of the transcription factor lac repressor (lacI) in E.coli, and performed stochastic simulations of the TF dynamics on the DNA for various combinations of lacI abundance and competing TFs that contribute to macromolecular crowding. We also investigated the relationship of site occupancy and the information content of position weight matrices (PWMs) used to represent binding sites. Our results showed that for medium and high affinity sites, TF competition does not play a significant role for genomic occupancy except in cases when the abundance of the TF is significantly increased, or when the PWM displays relatively low information content. Nevertheless, for medium and low affinity sites, an increase in TF abundance (for both cognate and non-cognate molecules) leads to an increase in occupancy at several sites.

Global parameter estimation for thermodynamic models of transcriptional regulation

Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort.

The effect of LacI autoregulation on the performance of the lactose utilization system in Escherichia coli

The lactose operon of Escherichia coli is a paradigm system for quantitative understanding of gene regulation in prokaryotes. Yet, none of the many mathematical models built so far to study the dynamics of this system considered the fact that the Lac repressor regulates its own transcription by forming a transcriptional roadblock at the O3 operator site. Here we study the effect of autoregulation on intracellular LacI levels and also show that cAMP-CRP binding does not affect the efficiency of autoregulation. We built a mathematical model to study the role of LacI autoregulation in the lactose utilization system. Previously, it has been argued that negative autoregulation can significantly reduce noise as well as increase the speed of response. We show that the particular molecular mechanism, a transcriptional roadblock, used to achieve self-repression in the lac system does neither. Instead, LacI autoregulation balances two opposing states, one that allows quicker response to smaller pulses of external lactose, and the other that minimizes production costs in the absence of lactose.

TWO-LAYER MATHEMATICAL MODELING OF GENE EXPRESSION: INCORPORATING DNA-LEVEL INFORMATION AND SYSTEM DYNAMICS

High-throughput genome sequencing and transcriptome analysis have provided researchers with a quantitative basis for detailed modeling of gene expression using a wide variety of mathematical models. Two of the most commonly employed approaches used to model eukaryotic gene regulation are systems of differential equations, which describe time-dependent interactions of gene networks, and thermodynamic equilibrium approaches that can explore DNA-level transcriptional regulation. To combine the strengths of these approaches, we have constructed a new two-layer mathematical model that provides a dynamical description of gene regulatory systems, using detailed DNA-based information, as well as spatial and temporal transcription factor concentration data. We also developed a semi-implicit numerical algorithm for solving the model equations and demonstrate here the efficiency of this algorithm through stability and convergence analyses. To test the model, we used it together with the semi-implicit algorithm to simulate a Drosophila gene regulatory circuit that drives development in the dorsal-ventral axis of the blastoderm-stage embryo, involving three genes. For model validation, we have done both mathematical and statistical comparisons between the experimental data and the model's simulated data. Where protein and cis-regulatory information is available, our two-layer model provides a method for recapitulating and predicting dynamic aspects of eukaryotic transcriptional systems that will greatly improve our understanding of gene regulation at a global level.

A genetic bistable switch utilizing nonlinear protein degradation

Background

Bistability is a fundamental property in engineered and natural systems, conferring the ability to switch and retain states. Synthetic bistable switches in prokaryotes have mainly utilized transcriptional components in their construction. Using both transcriptional and enzymatic components, creating a hybrid system, allows for wider bistable parameter ranges in a circuit.

Results

In this paper, we demonstrate a tunable family of hybrid bistable switches in E. coli using both transcriptional components and an enzymatic component. The design contains two linked positive feedback loops. The first loop utilizes the lambda repressor, CI, and the second positive feedback loop incorporates the Lon protease found in Mesoplasma florum (mf-Lon). We experimentally tested for bistable behavior in exponential growth phase, and found that our hybrid bistable switch was able to retain its state in the absence of an input signal throughout 40 cycles of cell division. We also tested the transient behavior of our switch and found that switching speeds can be tuned by changing the expression rate of mf-Lon.

Conclusions

To our knowledge, this work demonstrates the first use of dynamic expression of an orthogonal and heterologous protease to tune a nonlinear protein degradation circuit. The hybrid switch is potentially a more robust and tunable topology for use in prokaryotic systems.

Optimal control of gene mutation in DNA replication

We propose a molecular-level control system view of the gene mutations in DNA replication from the finite field concept. By treating DNA sequences as state variables, chemical mutagens and radiation as control inputs, one cell cycle as a step increment, and the measurements of the resulting DNA sequence as outputs, we derive system equations for both deterministic and stochastic discrete-time, finite-state systems of different scales. Defining the cost function as a summation of the costs of applying mutagens and the off-trajectory penalty, we solve the deterministic and stochastic optimal control problems by dynamic programming algorithm. In addition, given that the system is completely controllable, we find that the global optimum of both base-to-base and codon-to-codon deterministic mutations can always be achieved within a finite number of steps.

Mathematical modeling of gene expression: a guide for the perplexed biologist

The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project, we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean, and differential equation models, we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems.

A survey on methods for modeling and analyzing integrated biological networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics" technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.

Exact time-dependent solutions for a self-regulating gene

The exact time-dependent solution for the stochastic equations governing the behavior of a binary self-regulating gene is presented. Using the generating function technique to rephrase the master equations in terms of partial differential equations, we show that the model is totally integrable and the analytical solutions are the celebrated confluent Heun functions. Self-regulation plays a major role in the control of gene expression, and it is remarkable that such a microscopic model is completely integrable in terms of well-known complex functions.

Boolean models can explain bistability in the lac operon

The lac operon in Escherichia coli has been studied extensively and is one of the earliest gene systems found to undergo both positive and negative control. The lac operon is known to exhibit bistability, in the sense that the operon is either induced or uninduced. Many dynamical models have been proposed to capture this phenomenon. While most are based on complex mathematical formulations, it has been suggested that for other gene systems network topology is sufficient to produce the desired dynamical behavior. We present a Boolean network as a discrete model for the lac operon. Our model includes the two main glucose control mechanisms of catabolite repression and inducer exclusion. We show that this Boolean model is capable of predicting the ON and OFF steady states and bistability. Further, we present a reduced model which shows that lac mRNA and lactose form the core of the lac operon, and that this reduced model exhibits the same dynamics. This work suggests that the key to model qualitative dynamics of gene systems is the topology of the network and Boolean models are well suited for this purpose.

Mathematical modelling of stem cell differentiation: the PU.1-GATA-1 interaction

The transcription factors PU.1 and GATA-1 are known to be important in the development of blood progenitor cells. Specifically they are thought to regulate the differentiation of progenitor cells into the granulocyte/macrophage lineage and the erythrocyte/megakaryocite lineage. While several mathematical models have been proposed to investigate the interaction between the transcription factors in recent years, there is still debate about the nature of the progenitor state in the dynamical system, and whether the existing models adequately capture new knowledge about the interactions gleaned from experimental data. Further, the models utilise different formalisms to represent the genetic regulation, and it appears that the resulting dynamical system depends upon which formalism is adopted. In this paper we analyse the four existing models, and propose an alternative model which is shown to demonstrate a rich variety of dynamical systems behaviours found across the existing models, including both bistability and tristability required for modelling the undifferentiated progenitors.

Biological physics in México: Review and new challenges

Biological and physical sciences possess a long-standing tradition of cooperativity as separate but related subfields of science. For some time, this cooperativity has been limited by their obvious differences in methods and views. Biological physics has recently experienced a kind of revival (or better a rebirth) due to the growth of molecular research on animate matter. New avenues for research have been opened for both theoretical and experimental physicists. Nevertheless, in order to better travel for such paths, the contemporary biological physicist should be armed with a set of specialized tools and methods but also with a new attitude toward multidisciplinarity. In this review article, we intend to somehow summarize what has been done in the past (in particular, as an example we will take a closer look at the Mexican case), to show some examples of fruitful investigations in the biological physics area and also to set a proposal of new curricula for physics students and professionals interested in applying their science to get a better understanding of the physical basis of biological function.

Transcription by the numbers redux: experiments and calculations that surprise

The study of transcription has witnessed an explosion of quantitative effort both experimentally and theoretically. In this article we highlight some of the exciting recent experimental efforts in the study of transcription with an eye to the demands that such experiments put on theoretical models of transcription. From a modeling perspective, we focus on two broad classes of models: the so-called thermodynamic models that use statistical mechanics to reckon the level of gene expression as probabilities of promoter occupancy, and rate-equation treatments that focus on the temporal evolution of the activity of a given promoter and that make it possible to compute the distributions of messenger RNA and proteins. We consider several appealing case studies to illustrate how quantitative models have been used to dissect transcriptional regulation.

Rational design of modular circuits for gene transcription: A test of the bottom-up approach

BACKGROUND

Most of synthetic circuits developed so far have been designed by an ad hoc approach, using a small number of components (i.e. LacI, TetR) and a trial and error strategy. We are at the point where an increasing number of modular, inter-changeable and well-characterized components is needed to expand the construction of synthetic devices and to allow a rational approach to the design.

RESULTS

We used interchangeable modular biological parts to create a set of novel synthetic devices for controlling gene transcription, and we developed a mathematical model of the modular circuits. Model parameters were identified by experimental measurements from a subset of modular combinations. The model revealed an unexpected feature of the lactose repressor system, i.e. a residual binding affinity for the operator site by induced lactose repressor molecules. Once this residual affinity was taken into account, the model properly reproduced the experimental data from the training set. The parameters identified in the training set allowed the prediction of the behavior of networks not included in the identification procedure.

CONCLUSIONS

This study provides new quantitative evidences that the use of independent and well-characterized biological parts and mathematical modeling, what is called a bottom-up approach to the construction of gene networks, can allow the design of new and different devices re-using the same modular parts.

Building biological memory by linking positive feedback loops

A common topology found in many bistable genetic systems is two interacting positive feedback loops. Here we explore how this relatively simple topology can allow bistability over a large range of cellular conditions. On the basis of theoretical arguments, we predict that nonlinear interactions between two positive feedback loops can produce an ultrasensitive response that increases the range of cellular conditions at which bistability is observed. This prediction was experimentally tested by constructing a synthetic genetic circuit in Escherichia coli containing two well-characterized positive feedback loops, linked in a coherent fashion. The concerted action of both positive feedback loops resulted in bistable behavior over a broad range of inducer concentrations; when either of the feedback loops was removed, the range of inducer concentrations at which the system exhibited bistability was decreased by an order of magnitude. Furthermore, bistability of the system could be tuned by altering growth conditions that regulate the contribution of one of the feedback loops. Our theoretical and experimental work shows how linked positive feedback loops may produce the robust bistable responses required in cellular networks that regulate development, the cell cycle, and many other cellular responses.

Computational limits to binary genes

We analyse the trade-off between the speed with which a gene can propagate information, the noise of its output and its metabolic cost. Our main finding is that for any given level of metabolic cost there is an optimal trade-off between noise and processing speed. Any system with a non-vanishing leak expression rate is suboptimal, i.e. it will exhibit higher noise and/or slower speed than leak-free systems with the same metabolic cost. We also show that there is an optimal Hill coefficient h which minimizes noise and metabolic cost at fixed speeds, and an optimal threshold K which minimizes noise.

Comparison of deterministic and stochastic models of the lac operon genetic network

The lac operon has been a paradigm for genetic regulation with positive feedback, and several modeling studies have described its dynamics at various levels of detail. However, it has not yet been analyzed how stochasticity can enrich the system's behavior, creating effects that are not observed in the deterministic case. To address this problem we use a comparative approach. We develop a reaction network for the dynamics of the lac operon genetic switch and derive corresponding deterministic and stochastic models that incorporate biological details. We then analyze the effects of key biomolecular mechanisms, such as promoter strength and binding affinities, on the behavior of the models. No assumptions or approximations are made when building the models other than those utilized in the reaction network. Thus, we are able to carry out a meaningful comparison between the predictions of the two models to demonstrate genuine effects of stochasticity. Such a comparison reveals that in the presence of stochasticity, certain biomolecular mechanisms can profoundly influence the region where the system exhibits bistability, a key characteristic of the lac operon dynamics. For these cases, the temporal asymptotic behavior of the deterministic model remains unchanged, indicating a role of stochasticity in modulating the behavior of the system.

Kinetic modeling of ace operon genetic regulation in Escherichia coli

A family of kinetic models has been developed that takes into account available experimental information on the regulation of ace operon expression in Escherichia coli. This has allowed us to study and analyze possible versions of regulation of the ace operon and to test their possibilities. Based on literature analysis, we found that there is an ambiguity of properties of IclR (main repressor of ace operon). The main aspect of this ambiguity are two different forms of IclR purified from E. coli K strain and different coeffector sets for IclR purified from E. coli K and B strains. It has been shown that the full-length form of IclR is physiologically relevant and that IclR truncation is a result of purification of the protein from E. coli K strains. We also found that the IclR protein purified from E. coli B strain carries two coeffector binding sites. Using model-developed levels of steady state aceBAK expression against physiological ranges of coeffectors, concentration has been predicted.

Gene regulation in continuous cultures: a unified theory for bacteria and yeasts

During batch growth on mixtures of two growth-limiting substrates, microbes consume the substrates either sequentially (diauxie) or simultaneously. The ubiquity of these growth patterns suggests that they may be driven by a universal mechanism common to all microbial species. Recently, we showed that a minimal model accounting only for enzyme induction and dilution, the two processes that occur in all microbes, explains the phenotypes observed in batch cultures of various wild-type and mutant/recombinant cells (Narang and Pilyugin in J. Theor. Biol. 244:326-348, 2007). Here, we examine the extension of the minimal model to continuous cultures. We show that: (1) Several enzymatic trends, attributed entirely to cross-regulatory mechanisms, such as catabolite repression and inducer exclusion, can be quantitatively explained by enzyme dilution. (2) The bifurcation diagram of the minimal model for continuous cultures, which classifies the substrate consumption pattern at any given dilution rate and feed concentrations, provides a precise explanation for the empirically observed correlations between the growth patterns in batch and continuous cultures. (3) Numerical simulations of the model are in excellent agreement with the data. The model captures the variation of the steady state substrate concentrations, cell densities, and enzyme levels during the single- and mixed-substrate growth of bacteria and yeasts at various dilution rates and feed concentrations. This variation is well approximated by simple analytical expressions that furnish deep physical insights. (4) Since the minimal model describes the behavior of the cells in the absence of cross-regulatory mechanisms, it provides a rigorous framework for quantifying the effect of these mechanisms. We illustrate this by analyzing several data sets from the literature.

Quantitative approaches to the study of bistability in the lac operon of Escherichia coli

In this paper, the history and importance of the lac operon in the development of molecular and systems biology are briefly reviewed. We start by presenting a description of the regulatory mechanisms in this operon, taking into account the most recent discoveries. Then we offer a survey of the history of the lac operon, including the discovery of its main elements and the subsequent influence on the development of molecular and systems biology. Next the bistable behaviour of the operon is discussed, both with respect to its discovery and its molecular origin. A review of the literature in which this bistable phenomenon has been studied from a mathematical modelling viewpoint is then given. We conclude with some brief remarks.

When activators repress and repressors activate: a qualitative analysis of the Shea-Ackers model

The concept of activation in transcriptional regulation is based on the assumption that product mRNA increases monotonically as a function of regulator concentration. We analyze the Shea-Ackers model of transcription and find this assumption to be correct only for the simplest of promoters. We define a new regulatory constant that is a nonlinear combination of association and transcription initiation constants characterizing activation and repression for more complicated promoters. Our results can guide the synthesis of new promoters and lead to a deeper understanding of the constraints guiding the natural promoters evolution.

A bistable gene switch for antibiotic biosynthesis: the butyrolactone regulon in Streptomyces coelicolor

Many microorganisms, including bacteria of the class Streptomycetes, produce various secondary metabolites including antibiotics to gain a competitive advantage in their natural habitat. The production of these compounds is highly coordinated in a population to expedite accumulation to an effective concentration. Furthermore, as antibiotics are often toxic even to their producers, a coordinated production allows microbes to first arm themselves with a defense mechanism to resist their own antibiotics before production commences. One possible mechanism of coordination among individuals is through the production of signaling molecules. The gamma-butyrolactone system in Streptomyces coelicolor is a model of such a signaling system for secondary metabolite production. The accumulation of these signaling molecules triggers antibiotic production in the population. A pair of repressor-amplifier proteins encoded by scbA and scbR mediates the production and action of one particular gamma-butyrolactone, SCB1. Based on the proposed interactions of scbA and scbR, a mathematical model was constructed and used to explore the ability of this system to act as a robust genetic switch. Stability analysis shows that the butyrolactone system exhibits bistability and, in response to a threshold SCB1 concentration, can switch from an OFF state to an ON state corresponding to the activation of genes in the cryptic type I polyketide synthase gene cluster, which are responsible for production of the hypothetical polyketide. The switching time is inversely related to the inducer concentration above the threshold, such that short pulses of low inducer concentration cannot switch on the system, suggesting its possible role in noise filtering. In contrast, secondary metabolite production can be triggered rapidly in a population of cells producing the butyrolactone signal due to the presence of an amplification loop in the system. S. coelicolor was perturbed experimentally by varying concentrations of SCB1, and the model simulations match the experimental data well. Deciphering the complexity of this butyrolactone switch will provide valuable insights into how robust and efficient systems can be designed using "simple" two-protein networks.

Binding cooperativity in phage lambda is not sufficient to produce an effective switch

In the wild-type phage lambda, binding of CI to O(R)2 helps polymerase bound to P(RM) transition from a closed to open complex. Activators on other promoters increase the polymerase-DNA binding energy, or affect both the binding energy and the closed-open transition probability. Using a validated mathematical model, we show that these two modes of upregulation have very different effects on the promoter function. We predict that if CI(2) bound to O(R)2 produced equal increase in RNAP-DNA binding constant (compared to wild-type increase in the closed-open transition probability), the lysogen would be significantly less stable.

A feed-forward loop guarantees robust behavior in Escherichia coli carbohydrate uptake

MOTIVATION

In Escherichia coli, the phosphoenolpyruvate: carbohydrate phosphotransferase system acts like a sensory element which is able to measure the flux through glycolysis. Since the output of the sensor, the phosphorylated form of protein EIIA, is connected to the activity of the global transcription factor Crp, the kinetic and structural properties of the system are important for the understanding of the overall cellular behavior.

RESULTS

A family of mathematical models is presented, varying with respect to their degree of complexity (number of reactions that are taken into account, number of parameters) that show a structurally and quantitatively robust behavior. The models describe a set of experimental data that relates the output of the sensor to the specific growth rate. A central element that is responsible for the structural robustness is a feed-forward loop in the glycolysis, namely the activation of the pyruvate kinase reaction by a metabolite of the upper part of the glycolysis. The robustness is shown for variations of the measured data as well as for variations of the parameters.

AVAILABILITY

MATLAB files for model simulations are available on http://www.mpi-magdeburg.mpg.de/people/kre/robust/ A short description of the files provided on this site can be found in the Supporting information.

Modulation of glucose transport causes preferential utilization of aromatic compounds in Pseudomonas putida CSV86

Pseudomonas putida CSV86 utilizes aromatic compounds in preference to glucose and coutilizes aromatics and organic acids. Protein analysis of cells grown on different carbon sources, either alone or in combination, revealed that a 43-kDa periplasmic-space protein was induced by glucose and repressed by aromatics and succinate. Two-dimensional gel electrophoresis and liquid chromatography-tandem mass spectrometry analysis identified this protein as closely resembling the sugar ABC transporter of Pseudomonas putida KT2440. A partially purified 43-kDa protein showed glucose binding activity and was specific for glucose. The results demonstrate that the aromatic- and organic acid-mediated repression of a periplasmic-space glucose binding protein and consequent inhibition of glucose transport are responsible for this strain's ability to utilize aromatics and organic acids in preference to glucose.

Simultaneous catabolite repression between glucose and toluene metabolism in Pseudomonas putida is channeled through different signaling pathways

Pseudomonas putida KT2440(pWW0) can use toluene via the TOL plasmid-encoded catabolic pathways and can use glucose via a series of three peripheral chromosome-encoded routes that convert glucose into 6-phosphogluconate (6PG), namely, the glucokinase pathway, in which glucose is transformed to 6PG through the action of glucokinase and glucose-6-phosphate dehydrogenase. Alternatively, glucose can be oxidized to gluconate, which can be phosphorylated by gluconokinase to 6PG or oxidized to 2-ketogluconate, which, in turn, is converted into 6PG. Our results show that KT2440 metabolizes glucose and toluene simultaneously, as revealed by net flux analysis of [(13)C]glucose. Determination of glucokinase and gluconokinase activities in glucose metabolism, gene expression assays using a fusion of the promoter of the Pu TOL upper pathway to 'lacZ, and global transcriptomic assays revealed simultaneous catabolite repression in the use of these two carbon sources. The effect of toluene on glucose metabolism was directed to the glucokinase branch and did not affect gluconate metabolism. Catabolite repression of the glucokinase pathway and the TOL pathway was triggered by two different catabolite repression systems. Expression from Pu was repressed mainly via PtsN in response to high levels of 2-dehydro-3-deoxygluconate-6-phosphate, whereas repression of the glucokinase pathway was channeled through Crc.

Stochastic approach to molecular interactions and computational theory of metabolic and genetic regulations

The underlying molecular mechanisms of metabolic and genetic regulations are computationally identical and can be described by a finite state Markov process. We establish a common computational model for both regulations based on the stationary distribution of the Markov process with the aim of establishing a unified, quantitative model of general biological regulations. Various existing results regarding intracellular regulations are derived including the classical Michaelis-Menten equation and its generalization to more complex allosteric enzymes in a systematic way. The notion of probability flow is introduced to distinguish the equilibrium stationary distribution from the non-equilibrium one; it plays a crucial role in the analysis of stationary state equations. A graphical criterion to guarantee the existence of an equilibrium stationary distribution is derived, which turns out to be identical to the classical Wegscheider condition. Simple graphical methods to compute the equilibrium and non-equilibrium stationary distributions are derived based crucially on the probability flow, which dramatically simplifies the classical methods still used in enzymology.

Exact results for noise power spectra in linear biochemical reaction networks

We present a simple method for determining the exact noise power spectra and related statistical properties for linear chemical reaction networks. The method is applied to reaction networks which are representative of biochemical processes such as gene expression. We find, for example, that a post-translational modification reaction can reduce the noise associated with gene expression. Our results also indicate how to coarse grain networks by the elimination of fast reactions. In this context we have discovered a breakdown of the sum rule which relates the noise power spectrum to the total noise. The breakdown can be quantified by a sum rule deficit, which is found to be universal, and can be attributed to the high-frequency noise in the fast reactions.

Origin of bistability in the lac Operon

Multistability is an emergent dynamic property that has been invoked to explain multiple coexisting biological states. In this work, we investigate the origin of bistability in the lac operon. To do this, we develop a mathematical model for the regulatory pathway in this system and compare the model predictions with other experimental results in which a nonmetabolizable inducer was employed. We investigate the effect of lactose metabolism using this model, and show that it greatly modifies the bistable region in the external lactose (Le) versus external glucose (Ge) parameter space. The model also predicts that lactose metabolism can cause bistability to disappear for very low Ge. We have also carried out stochastic numerical simulations of the model for several values of Ge and Le. Our results indicate that bistability can help guarantee that Escherichia coli consumes glucose and lactose in the most efficient possible way. Namely, the lac operon is induced only when there is almost no glucose in the growing medium, but if Le is high, the operon induction level increases abruptly when the levels of glucose in the environment decrease to very low values. We demonstrate that this behavior could not be obtained without bistability if the stability of the induced and uninduced states is to be preserved. Finally, we point out that the present methods and results may be useful to study the emergence of multistability in biological systems other than the lac operon.

Analysis of lactose metabolism in E.coli using reachability analysis of hybrid systems

We propose an abstraction method for medium-scale biomolecular networks, based on hybrid dynamical systems with continuous multi-affine dynamics. This abstraction method follows naturally from the notion of approximating nonlinear rate laws with continuous piecewise linear functions and can be easily automated. An efficient reachability algorithm is possible for the resulting class of hybrid systems. An efficient reachability algorithm is possible for the resulting class of hybrid systems. An approximation for an ordinary differential equation model of the lac operon is constructed, and it is shown that the abstraction passes the same experimental tests as were used to validate the original model. The well studied biological system exhibits bistability and switching behaviour, arising from positive feedback in the expression mechanism of the lac operon. The switching property of the lac system is an example of the major qualitative features that are the building blocks of higher level, more coarse-grained descriptions. The present approach is useful in helping to correctly identify such properties and in connecting them to the underlying molecular dynamical details. Reachability analysis together with the knowledge of the steady-state structure are used to identify ranges of parameter values for which the system maintains the bistable switching property.