Combinatorics of feedback in cellular uptake and metabolism of small molecules

Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis

The genetic regulatory network (GRN) plays a key role in controlling the response of the cell to changes in the environment. Although the structure of GRNs has been the subject of many studies, their large scale structure in the light of feedbacks from the metabolic network (MN) has received relatively little attention. Here we study the causal structure of the GRNs, namely the chain of influence of one component on the other, taking into account feedback from the MN. First we consider the GRNs of E. coli and B. subtilis without feedback from MN and illustrate their causal structure. Next we augment the GRNs with feedback from their respective MNs by including (a) links from genes coding for enzymes to metabolites produced or consumed in reactions catalyzed by those enzymes and (b) links from metabolites to genes coding for transcription factors whose transcriptional activity the metabolites alter by binding to them. We find that the inclusion of feedback from MN into GRN significantly affects its causal structure, in particular the number of levels and relative positions of nodes in the hierarchy, and the number and size of the strongly connected components (SCCs). We then study the functional significance of the SCCs. For this we identify condition specific feedbacks from the MN into the GRN by retaining only those enzymes that are essential for growth in specific environmental conditions simulated via the technique of flux balance analysis (FBA). We find that the SCCs of the GRN augmented by these feedbacks can be ascribed specific functional roles in the organism. Our algorithmic approach thus reveals relatively autonomous subsystems with specific functionality, or regulatory modules in the organism. This automated approach could be useful in identifying biologically relevant modules in other organisms for which network data is available, but whose biology is less well studied.

Engineering Microbial Metabolite Dynamics and Heterogeneity

As yields for biological chemical production in microorganisms approach their theoretical maximum, metabolic engineering requires new tools, and approaches for improvements beyond what traditional strategies can achieve. Engineering metabolite dynamics and metabolite heterogeneity is necessary to achieve further improvements in product titers, productivities, and yields. Metabolite dynamics, the ensemble change in metabolite concentration over time, arise from the need for microbes to adapt their metabolism in response to the extracellular environment and are important for controlling growth and productivity in industrial fermentations. Metabolite heterogeneity, the cell-to-cell variation in a metabolite concentration in an isoclonal population, has a significant impact on ensemble productivity. Recent advances in single cell analysis enable a more complete understanding of the processes driving metabolite heterogeneity and reveal metabolic engineering targets. The authors present an overview of the mechanistic origins of metabolite dynamics and heterogeneity, why they are important, their potential effects in chemical production processes, and tools and strategies for engineering metabolite dynamics and heterogeneity. The authors emphasize that the ability to control metabolite dynamics and heterogeneity will bring new avenues of engineering to increase productivity of microbial strains.

The role of mRNA and protein stability in the function of coupled positive and negative feedback systems in eukaryotic cells

Oscillators and switches are important elements of regulation in biological systems. These are composed of coupling negative feedback loops, which cause oscillations when delayed, and positive feedback loops, which lead to memory formation. Here, we examine the behavior of a coupled feedback system, the Negative Autoregulated Frustrated bistability motif (NAF). This motif is a combination of two previously explored motifs, the frustrated bistability motif (FBM) and the negative auto regulation motif (NAR), which both can produce oscillations. The NAF motif was previously suggested to govern long term memory formation in animals, and was used as a synthetic oscillator in bacteria. We build a mathematical model to analyze the dynamics of the NAF motif. We show analytically that the NAF motif requires an asymmetry in the strengths of activation and repression links in order to produce oscillations. We show that the effect of time delays in eukaryotic cells, originating from mRNA export and protein import, are negligible in this system. Based on the reported protein and mRNA half-lives in eukaryotic cells, we find that even though the NAF motif possesses the ability for oscillations, it mostly promotes constant protein expression at the biologically relevant parameter regimes.

Coupled positive and negative feedbacks produce diverse gene expression patterns in colonies

Formation of patterns is a common feature in the development of multicellular organism as well as of microbial communities. To investigate the formation of gene expression patterns in colonies, we build a mathematical model of two-dimensional colony growth, where cells carry a coupled positive-and-negative-feedback circuit. We demonstrate that the model can produce sectored, target (concentric), uniform, and scattered expression patterns of regulators, depending on gene expression dynamics and nutrient diffusion. We reconstructed the same regulatory structure in Escherichia coli cells and found gene expression patterns on the surface of colonies similar to the ones produced by the computer simulations. By comparing computer simulations and experimental results, we observed that very simple rules of gene expression can yield a spectrum of well-defined patterns in a growing colony. Our results suggest that variations of the protein content among cells lead to a high level of heterogeneity in colonies.

Importance Formation of patterns is a common feature in the development of microbial communities. In this work, we show that a simple genetic circuit composed of a positive-feedback loop and a negative-feedback loop can produce diverse expression patterns in colonies. We obtained similar sets of gene expression patterns in the simulations and in the experiments. Because the combination of positive feedback and negative feedback is common in intracellular molecular networks, our results suggest that the protein content of cells is highly diversified in colonies.

Formation of patterns is a common feature in the development of microbial communities. In this work, we show that a simple genetic circuit composed of a positive-feedback loop and a negative-feedback loop can produce diverse expression patterns in colonies. We obtained similar sets of gene expression patterns in the simulations and in the experiments. Because the combination of positive feedback and negative feedback is common in intracellular molecular networks, our results suggest that the protein content of cells is highly diversified in colonies.

Trade-offs in engineering sugar utilization pathways for titratable control

Titratable systems are common tools in metabolic engineering to tune the levels of enzymes and cellular components as part of pathway optimization. For nonmodel microorganisms with limited genetic tools, inducible sugar utilization pathways offer built-in titratable systems. However, these pathways can exhibit undesirable single-cell behaviors that hamper the uniform and tunable control of gene expression. Here, we applied mathematical modeling and single-cell measurements of l-arabinose utilization in Escherichia coli to systematically explore how sugar utilization pathways can be altered to achieve desirable inducible properties. We found that different pathway alterations, such as the removal of catabolism, constitutive expression of high-affinity or low-affinity transporters, or further deletion of the other transporters, came with trade-offs specific to each alteration. For instance, sugar catabolism improved the uniformity and linearity of the response at the cost of requiring higher sugar concentrations to induce the pathway. Within these alterations, we also found that a uniform and linear response could be achieved with a single alteration: constitutively expressing the high-affinity transporter. Equivalent modifications to the d-xylose utilization pathway yielded similar responses, demonstrating the applicability of our observations. Overall, our findings indicate that there is no ideal set of typical alterations when co-opting natural utilization pathways for titratable control and suggest design rules for manipulating these pathways to advance basic genetic studies and the metabolic engineering of microorganisms for optimized chemical production.

Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli

The ferric uptake regulator (Fur) plays a critical role in the transcriptional regulation of iron metabolism. However, the full regulatory potential of Fur remains undefined. Here we comprehensively reconstruct the Fur transcriptional regulatory network in Escherichia coli K-12 MG1655 in response to iron availability using genome-wide measurements (ChIP-exo and RNA-seq). Integrative data analysis reveals that a total of 81 genes in 42 transcription units are directly regulated by three different modes of Fur regulation, including apo- and holo-Fur activation and holo-Fur repression. We show that Fur connects iron transport and utilization enzymes with negative-feedback loop pairs for iron homeostasis. In addition, direct involvement of Fur in the regulation of DNA synthesis, energy metabolism, and biofilm development is found. These results show how Fur exhibits a comprehensive regulatory role affecting many fundamental cellular processes linked to iron metabolism in order to coordinate the overall response of E. coli to iron availability.

Bacterial sugar utilization gives rise to distinct single-cell behaviours

Inducible utilization pathways reflect widespread microbial strategies to uptake and consume sugars from the environment. Despite their broad importance and extensive characterization, little is known how these pathways naturally respond to their inducing sugar in individual cells. Here, we performed single-cell analyses to probe the behaviour of representative pathways in the model bacterium Escherichia coli. We observed diverse single-cell behaviours, including uniform responses (d-lactose, d-galactose, N-acetylglucosamine, N-acetylneuraminic acid), 'all-or-none' responses (d-xylose, l-rhamnose) and complex combinations thereof (l-arabinose, d-gluconate). Mathematical modelling and probing of genetically modified pathways revealed that the simple framework underlying these pathways - inducible transport and inducible catabolism - could give rise to most of these behaviours. Sugar catabolism was also an important feature, as disruption of catabolism eliminated tunable induction as well as enhanced memory of previous conditions. For instance, disruption of catabolism in pathways that respond to endogenously synthesized sugars led to full pathway induction even in the absence of exogenous sugar. Our findings demonstrate the remarkable flexibility of this simple biological framework, with direct implications for environmental adaptation and the engineering of synthetic utilization pathways as titratable expression systems and for metabolic engineering.

BioFNet: biological functional network database for analysis and synthesis of biological systems

In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures.

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.

Specific contacts of the -35 region of the galP1 promoter by RNA polymerase inhibit GalR-mediated DNA looping repression

The P1 promoter of the galactose operon in Escherichia coli is one of the best studied examples of ‘extended −10’ promoters. Recognition of the P1 promoter does not require specific contacts between RNA polymerase and its poor −35 element. To investigate whether specific recognition of the −35 element would affect the regulation of P1 by GalR, we mutagenized the −35 element of P1, isolated variants of the −35 element and studied the regulation of the mutant promoters by in vitro transcription assays and by mathematical modeling. The results show that the GalR-mediated DNA loop is less efficient in repressing P1 transcription when RNA polymerase binds to the −10 and −35 elements concomitantly. Our results suggest that promoters that lack specific −35 element recognition allow decoupling of local chromosome structure from transcription initiation.

Deciphering the transcriptional regulatory logic of amino acid metabolism

Although metabolic networks have been reconstructed on a genome scale, the corresponding reconstruction and integration of governing transcriptional regulatory networks has not been fully achieved. Here we reconstruct such an integrated network for amino acid metabolism in Escherichia coli. Analysis of ChIP-chip and gene expression data for the transcription factors ArgR, Lrp and TrpR showed that 19 out of 20 amino acid biosynthetic pathways are either directly or indirectly controlled by these regulators. Classifying the regulated genes into three functional categories of transport, biosynthesis and metabolism leads to the elucidation of regulatory motifs that constitute the integrated network's basic building blocks. The regulatory logic of these motifs was determined on the basis of relationships between transcription factor binding and changes in the amount of transcript in response to exogenous amino acids. Remarkably, the resulting logic shows how amino acids are differentiated as signaling and nutrient molecules, revealing the overarching regulatory principles of the amino acid stimulon.

The PurR regulon in Escherichia coli K-12 MG1655

The PurR transcription factor plays a critical role in transcriptional regulation of purine metabolism in enterobacteria. Here, we elucidate the role of PurR under exogenous adenine stimulation at the genome-scale using high-resolution chromatin immunoprecipitation (ChIP)–chip and gene expression data obtained under in vivo conditions. Analysis of microarray data revealed that adenine stimulation led to changes in transcript level of about 10% of Escherichia coli genes, including the purine biosynthesis pathway. The E. coli strain lacking the purR gene showed that a total of 56 genes are affected by the deletion. From the ChIP–chip analysis, we determined that over 73% of genes directly regulated by PurR were enriched in the biosynthesis, utilization and transport of purine and pyrimidine nucleotides, and 20% of them were functionally unknown. Compared to the functional diversity of the regulon of the other general transcription factors in E. coli, the functions and size of the PurR regulon are limited.

Timing of gene transcription in the galactose utilization system of Escherichia coli

In the natural environment, bacterial cells have to adjust their metabolism to alterations in the availability of food sources. The order and timing of gene expression are crucial in these situations to produce an appropriate response. We used the galactose regulation in Escherichia coli as a model system for understanding how cells integrate information about food availability and cAMP levels to adjust the timing and intensity of gene expression. We simulated the feast-famine cycle of bacterial growth by diluting stationary phase cells in fresh medium containing galactose as the sole carbon source. We followed the activities of six promoters of the galactose system as cells grew on and ran out of galactose. We found that the cell responds to a decreasing external galactose level by increasing the internal galactose level, which is achieved by limiting galactose metabolism and increasing the expression of transporters. We show that the cell alters gene expression based primarily on the current state of the cell and not on monitoring the level of extracellular galactose in real time. Some decisions have longer term effects; therefore, the current state does subtly encode the history of food availability. In summary, our measurements of timing of gene expression in the galactose system suggest that the system has evolved to respond to environments where future galactose levels are unpredictable rather than regular feast and famine cycles.

Simplified models of biological networks

The function of living cells is controlled by complex regulatory networks that are built of a wide diversity of interacting molecular components. The sheer size and intricacy of molecular networks of even the simplest organisms are obstacles toward understanding network functionality. This review discusses the achievements and promise of a bottom-up approach that uses well-characterized subnetworks as model systems for understanding larger networks. It highlights the interplay between the structure, logic, and function of various types of small regulatory circuits. The bottom-up approach advocates understanding regulatory networks as a collection of entangled motifs. We therefore emphasize the potential of negative and positive feedback, as well as their combinations, to generate robust homeostasis, epigenetics, and oscillations.

Bacterial adaptation through distributed sensing of metabolic fluxes

We present a large-scale differential equation model of E. coli's central metabolism and its enzymatic, transcriptional, and posttranslational regulation. This model reproduces E. coli's known physiological behavior.

We found that the interplay of known interactions in E. coli's central metabolism can indirectly recognize the presence of extracellular carbon sources through measuring intracellular metabolic flux patterns.

We found that E. coli's system-level adaptations between glycolytic and gluconeogenic carbon sources are realized on the molecular level by global feedback architectures that overarch the enzymatic and transcriptional regulatory layers.

We found that the capability for closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously to changing carbon sources (not requiring upstream sensing and signaling).

Adaptations to fluctuating carbon source availability are of particular importance for bacteria. To understand these adaptations, it needs to be understood how a system's behavior emerges from the interactions between the characterized molecules (Kitano, 2002b). To attain such a system understanding of bacterial metabolic adaptations to carbon source availability, the coupling between the recognition and adjustment aspects and between the enzymatic and genetic regulatory layers must be understood. For many carbon sources, neither transmembrane sensors nor regulatory proteins with sensing function have been identified. Also, it remains unclear how multiple local regulations work together to accomplish a coherent adjustment on the systems level. In this paper, we show that (1) the interplay of the known interactions in E. coli's central metabolism is capable of recognizing carbon sources indirectly, and that (2) these molecular interactions can adjust E. coli's metabolic operation between growth on glycolytic and gluconeogenic carbon sources, and that (3) this adaptation is governed by general principles.

We hypothesized that the system-level adaptations between growth on glycolytic and gluconeogenic carbon sources are accomplished by a system-wide regulation architecture that emerges when the known enzymatic and transcriptional regulations become coupled through five transcription factor (TF)–metabolite interactions. To (1) assess whether such coupled molecular interactions can indeed work together to adapt metabolic operation, and if yes, (2) to understand this system-level adaptation in molecular-level detail, we constructed a large-scale differential equation model. The model topology comprises the Embden–Meyerhoff pathway, the tricarboxylic acid (TCA) cycle, the glyoxylate (GLX) shunt, the anaplerotic reactions, the diversion of carbon flux to the GLX shunt, the uptake of glucose, the uptake and excretion of acetate, enzymatic regulation, transcriptional regulation by four TFs, and the regulation of these TFs' activities through TF–metabolite interactions. We translated the topology into differential equations by assigning the most appropriate rate law to each interaction. The kinetic model comprises 47 ordinary differential equations and 193 parameters. Parameter values were estimated through application of the ‘divide-and-conquer approach' (Kotte and Heinemann, 2009) on published experimental steady state-omics data sets.

Model simulations reproduce E. coli's known physiological behavior in an environment with fluctuating carbon source availability. But how does the in silico cell recognize acetate without a transmembrane sensor for extracellular acetate or a TF binding to intracellular acetate? Similarly, it is unclear whether the glucose sensing function of the phosphotransferase system is the exclusive mechanism to recognize glucose, or whether this sensing function is integrated into a larger sensing architecture. The model suggests that the recognition is performed indirectly through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two distinct motifs, which we termed pathway usage and flux direction, to establish defined correlations between metabolic fluxes and the levels of certain, here termed flux-signaling metabolites. The binding of these metabolites to TFs propagates the flux information to the transcriptional regulatory layer. A molecular sensor for intracellular metabolic flux is thus defined as a system of regulations and enzyme kinetics, comprising (1) either of the two motifs pathway usage or flux direction and (2) the binding of the thus established flux-signaling metabolites to TF(s).

As the in silico cell establishes and uses sensors for several intracellular metabolic fluxes, the overall sensing architecture infers the present carbon sources from a pattern of metabolic fluxes and is as such of a distributed nature. The core of this sensing architecture is formed not by transmembrane sensors but by four flux sensors, which establish flux-signaling metabolites according to the two proposed general motifs. These flux sensors use intracellular metabolic flux as a means to correlate the presence of extracellular carbon sources with the levels of intracellular metabolites. The recognition of glucose through the PTS transmembrane complex is embedded as one flux sensor in this distributed sensing architecture; the other three flux sensors function without the help of transmembrane complexes.

The in silico cell achieves the coupling between recognition and adjustment through its TFs, whose activities respond to the available carbon sources and at the same time regulate the expression of target genes. This combined recognition and adjustment, centered on the four TFs, closes four global feedback loops that overarch the metabolic and genetic layers as illustrated in Figure 6. The adaptation of the in silico cell arises from the global feedback loop-embedded, flux sensor-adjusted transcriptional regulation of the four TFs, with each TF performing one part of the overall adaptation. This adaptation incorporates both the influence of the metabolic on the genetic layer, achieved through TF–metabolite interactions, and of the genetic on the metabolic layer, achieved through the impact of adjusted enzyme levels on metabolic fluxes.

The existence of the global feedback architectures challenges the conventional view that top-level regulatory proteins recognize environmental conditions and adjust downstream metabolic operation. It suggests that the capability for closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and regulation) to changing carbon sources.

To conclude, the presented differential equation model of E. coli's central metabolism offers a consistent explanation of how a multitude of known molecular interactions fit into a coherent systems picture; the interactions work together like gear wheels that mesh with one another to adapt central metabolism between growth on the glycolytic substrate glucose and the gluconeogenic substrate acetate. The deduced general functional principles provide the missing link to understand system-level adaptations to carbon sources in molecular-level detail. The proposed principles fall under the umbrella of distributed flux sensing. The flux sensing mechanism entails the binding of TFs to flux-signaling metabolites, which are established through the motifs signaling of pathway usage and signaling of flux direction, and are embedded in global feedback loop architectures. These principles allow an autonomous adaptation of metabolic operation to growth in fluctuating environments.

The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.

Growth landscape formed by perception and import of glucose in yeast

An important challenge in systems biology is to quantitatively describe microbial growth using a few measurable parameters that capture the essence of this complex phenomenon. Two key events at the cell membrane – extracellular glucose sensing and uptake – initiate the budding yeast’s growth on glucose. However, conventional growth models focus almost exclusively on glucose uptake. Here we present results from growth-rate experiments that cannot be explained by focusing on glucose uptake alone. By imposing a glucose uptake rate independent of the sensed extracellular glucose level, we show that despite increasing both the sensed glucose concentration and uptake rate, the cell’s growth rate can decrease or even approach zero. We resolve this puzzle by showing that the interaction between glucose perception and import, not their individual actions, determines the central features of growth and characterize this interaction using a quantitative model. Disrupting this interaction by knocking out two key glucose sensors significantly changes the cell’s growth rate, yet uptake rates are unchanged. This is due to a decrease in burden that glucose perception places on the cells. Our work shows that glucose perception and import are separate and pivotal modules of yeast growth whose interplay can be precisely tuned and measured.

The combination of positive and negative feedback loops confers exquisite flexibility to biochemical switches

A wide range of cellular processes require molecular regulatory pathways to convert a graded signal into a discrete response. One prevalent switching mechanism relies on the coexistence of two stable states (bistability) caused by positive feedback regulations. Intriguingly, positive feedback is often supplemented with negative feedback, raising the question of whether and how these two types of feedback can cooperate to control discrete cellular responses. To address this issue, we formulate a canonical model of a protein-protein interaction network and analyze the dynamics of a prototypical two-component circuit. The appropriate combination of negative and positive feedback loops can bring a bistable circuit close to the oscillatory regime. Notably, sharply activated negative feedback can give rise to a bistable regime wherein two stable fixed points coexist and may collide pairwise with two saddle points. This specific type of bistability is found to allow for separate and flexible control of switch-on and switch-off events, for example (i) to combine fast and reversible transitions, (ii) to enable transient switching responses and (iii) to display tunable noise-induced transition rates. Finally, we discuss the relevance of such bistable switching behavior, and the circuit topologies considered, to specific biological processes such as adaptive metabolic responses, stochastic fate decisions and cell-cycle transitions. Taken together, our results suggest an efficient mechanism by which positive and negative feedback loops cooperate to drive the flexible and multifaceted switching behaviors arising in biological systems.

Relation of intracellular signal levels and promoter activities in the gal regulon of Escherichia coli

Transcription of many genes is regulated by combinations of multiple signals. In Escherichia coli, combinatorial control is typical in the case of operons related to utilization of different sugars in the absence of glucose. To understand regulation of the transport and metabolic pathways in the galactose system, we measured activities of the six gal regulon promoters simultaneously, using an in vitro transcription system containing purified components. Input functions were computed on the basis of the experimental measurements. We observed four different shapes of input functions. From the results, we can conclude that the structure of the regulatory network is insufficient for the determination of signal integration. It is the actual structure of the promoter and regulatory region, the mechanism of transcription regulation, and the interplay between transcription factors that shape the input function to be suitable for adaptation.

Dynamics of uptake and metabolism of small molecules in cellular response systems

BACKGROUND

Proper cellular function requires uptake of small molecules from the environment. In response to changes in extracellular conditions cells alter the import and utilization of small molecules. For a wide variety of small molecules the cellular response is regulated by a network motif that combines two feedback loops, one which regulates the transport and the other which regulates the subsequent metabolism.

RESULTS

We analyze the dynamic behavior of two widespread but logically distinct two-loop motifs. These motifs differ in the logic of the feedback loop regulating the uptake of the small molecule. Our aim is to examine the qualitative features of the dynamics of these two classes of feedback motifs. We find that the negative feedback to transport is accompanied by overshoot in the intracellular amount of small molecules, whereas a positive feedback to transport removes overshoot by boosting the final steady state level. On the other hand, the negative feedback allows for a rapid initial response, whereas the positive feedback is slower. We also illustrate how the dynamical deficiencies of one feedback motif can be mitigated by an additional loop, while maintaining the original steady-state properties.

CONCLUSIONS

Our analysis emphasizes the core of the regulation found in many motifs at the interface between the metabolic network and the environment of the cell. By simplifying the regulation into uptake and the first metabolic step, we provide a basis for elaborate studies of more realistic network structures. Particularly, this theoretical analysis predicts that FeS cluster formation plays an important role in the dynamics of iron homeostasis.

Genome-scale reconstruction of the Lrp regulatory network in Escherichia coli

Broad-acting transcription factors (TFs) in bacteria form regulons. Here, we present a 4-step method to fully reconstruct the leucine-responsive protein (Lrp) regulon in Escherichia coli K-12 MG 1655 that regulates nitrogen metabolism. Step 1 is composed of obtaining high-resolution ChIP-chip data for Lrp, the RNA polymerase and expression profiles under multiple environmental conditions. We identified 138 unique and reproducible Lrp-binding regions and classified their binding state under different conditions. In the second step, the analysis of these data revealed 6 distinct regulatory modes for individual ORFs. In the third step, we used the functional assignment of the regulated ORFs to reconstruct 4 types of regulatory network motifs around the metabolites that are affected by the corresponding gene products. In the fourth step, we determined how leucine, as a signaling molecule, shifts the regulatory motifs for particular metabolites. The physiological structure that emerges shows the regulatory motifs for different amino acid fall into the traditional classification of amino acid families, thus elucidating the structure and physiological functions of the Lrp-regulon. The same procedure can be applied to other broad-acting TFs, opening the way to full bottom-up reconstruction of the transcriptional regulatory network in bacterial cells.