The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica

Asymmetric physiological response of a reef-building coral to pulsed versus continuous addition of inorganic nutrients

Coral reefs, especially those located near-shore, are increasingly exposed to anthropogenic, eutrophic conditions that are often chronic. Yet, corals under unperturbed conditions may frequently receive natural and usually temporary nutrient supplementation through biological sources such as fishes. We compared physiological parameters indicative of long- and short-term coral health (day and night calcification, fragment surface area, productivity, energy reserves, and tissue stoichiometry) under continuous and temporary nutrient enrichment. The symbiotic coral Acropora intermedia was grown for 7 weeks under continuously elevated (press) levels of ammonium (14 µmol L^-1) and phosphate (10 µmol L^-1) as separate and combined treatments, to discern the individual and interactive nutrient effects. Another treatment exposed A. intermedia twice-daily to an ammonium and phosphate pulse of the same concentrations as the press treatments to simulate natural biotic supplementation. Press exposure to elevated ammonium or phosphate produced mixed effects on physiological responses, with little interaction between the nutrients in the combined treatment. Overall, corals under press exposure transitioned resources away from calcification. However, exposure to nutrient pulses often enhanced physiological responses. Our findings indicate that while continuous nutrient enrichment may pose a threat to coral health, episodic nutrient pulses that resemble natural nutrient supplementation may significantly benefit coral health and physiology.

Complex Stability and an Irrevertible Transition Reverted by Peptide and Fibroblasts in a Dynamic Model of Innate Immunity

We here apply a control analysis and various types of stability analysis to an in silico model of innate immunity that addresses the management of inflammation by a therapeutic peptide. Motivation is the observation, both in silico and in experiments, that this therapy is not robust. Our modeling results demonstrate how (1) the biological phenomena of acute and chronic modes of inflammation may reflect an inherently complex bistability with an irrevertible flip between the two modes, (2) the chronic mode of the model has stable, sometimes unique, steady states, while its acute-mode steady states are stable but not unique, (3) as witnessed by TNF levels, acute inflammation is controlled by multiple processes, whereas its chronic-mode inflammation is only controlled by TNF synthesis and washout, (4) only when the antigen load is close to the acute mode's flipping point, many processes impact very strongly on cells and cytokines, (5) there is no antigen exposure level below which reduction of the antigen load alone initiates a flip back to the acute mode, and (6) adding healthy fibroblasts makes the transition from acute to chronic inflammation revertible, although (7) there is a window of antigen load where such a therapy cannot be effective. This suggests that triple therapies may be essential to overcome chronic inflammation. These may comprise (1) anti-immunoglobulin light chain peptides, (2) a temporarily reduced antigen load, and (3a) fibroblast repopulation or (3b) stem cell strategies.

Self-replenishment cycles generate a threshold response

Many metabolic cycles, including the tricarboxylic acid cycle, glyoxylate cycle, Calvin cycle, urea cycle, coenzyme recycling, and substrate cycles, are well known to catabolize and anabolize different metabolites for efficient energy and mass conversion. In terms of stoichiometric structure, this study explicitly identifies two types of metabolic cycles. One is the well-known, elementary cycle that converts multiple substrates into different products and recycles one of the products as a substrate, where the recycled substrate is supplied from the outside to run the cycle. The other is the self-replenishment cycle that merges multiple substrates into two or multiple identical products and reuses one of the products as a substrate. The substrates are autonomously supplied within the cycle. This study first defines the self-replenishment cycles that many scientists have overlooked despite its functional importance. Theoretical analysis has revealed the design principle of the self-replenishment cycle that presents a threshold response without any bistability nor cooperativity. To verify the principle, three detailed kinetic models of self-replenishment cycles embedded in an E. coli metabolic system were simulated. They presented the threshold response or digital switch-like function that steeply shift metabolic status.

Quantifying Dynamic Regulation in Metabolic Pathways with Nonparametric Flux Inference

One of the central tasks in systems biology is to understand how cells regulate their metabolism. Hierarchical regulation analysis is a powerful tool to study this regulation at the metabolic, gene-expression, and signaling levels. It has been widely applied to study steady-state regulation, but analysis of the metabolic dynamics remains challenging because it is difficult to measure time-dependent metabolic flux. Here, we develop a nonparametric method that uses Gaussian processes to accurately infer the dynamics of a metabolic pathway based only on metabolite measurements; from this, we then go on to obtain a dynamical view of the hierarchical regulation processes invoked over time to control the activity in a pathway. Our approach allows us to use hierarchical regulation analysis in a dynamic setting but without the need for explicitly time-dependent flux measurements.

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a 'rubber band method' to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli's AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.

GlnK Facilitates the Dynamic Regulation of Bacterial Nitrogen Assimilation

Ammonium assimilation in Escherichia coli is regulated by two paralogous proteins (GlnB and GlnK), which orchestrate interactions with regulators of gene expression, transport proteins, and metabolic pathways. Yet how they conjointly modulate the activity of glutamine synthetase, the key enzyme for nitrogen assimilation, is poorly understood. We combine experiments and theory to study the dynamic roles of GlnB and GlnK during nitrogen starvation and upshift. We measure time-resolved in vivo concentrations of metabolites, total and posttranslationally modified proteins, and develop a concise biochemical model of GlnB and GlnK that incorporates competition for active and allosteric sites, as well as functional sequestration of GlnK. The model predicts the responses of glutamine synthetase, GlnB, and GlnK under time-varying external ammonium level in the wild-type and two genetic knock-outs. Our results show that GlnK is tightly regulated under nitrogen-rich conditions, yet it is expressed during ammonium run-out and starvation. This suggests a role for GlnK as a buffer of nitrogen shock after starvation, and provides a further functional link between nitrogen and carbon metabolisms.

The Peculiar Glycolytic Pathway in Hyperthermophylic Archaea: Understanding Its Whims by Experimentation In Silico

Mathematical models are key to systems biology where they typically describe the topology and dynamics of biological networks, listing biochemical entities and their relationships with one another. Some (hyper)thermophilic Archaea contain an enzyme, called non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GAPN), which catalyzes the direct oxidation of glyceraldehyde-3-phosphate to 3-phosphoglycerate omitting adenosine 5′-triphosphate (ATP) formation by substrate-level-phosphorylation via phosphoglycerate kinase. In this study we formulate three hypotheses that could explain functionally why GAPN exists in these Archaea, and then construct and use mathematical models to test these three hypotheses. We used kinetic parameters of enzymes of Sulfolobus solfataricus (S. solfataricus) which is a thermo-acidophilic archaeon that grows optimally between 60 and 90 °C and between pH 2 and 4. For comparison, we used a model of Saccharomyces cerevisiae (S. cerevisiae), an organism that can live at moderate temperatures. We find that both the first hypothesis, i.e., that the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plus phosphoglycerate kinase (PGK) route (the alternative to GAPN) is thermodynamically too much uphill and the third hypothesis, i.e., that GAPDH plus PGK are required to carry the flux in the gluconeogenic direction, are correct. The second hypothesis, i.e., that the GAPDH plus PGK route delivers less than the 1 ATP per pyruvate that is delivered by the GAPN route, is only correct when GAPDH reaction has a high rate and 1,3-bis-phosphoglycerate (BPG) spontaneously degrades to 3PG at a high rate.

The promiscuous enzyme medium-chain 3-keto-acyl-CoA thiolase triggers a vicious cycle in fatty-acid beta-oxidation

Mitochondrial fatty-acid beta-oxidation (mFAO) plays a central role in mammalian energy metabolism. Multiple severe diseases are associated with defects in this pathway. Its kinetic structure is characterized by a complex wiring of which the functional implications have hardly been explored. Repetitive cycles of reversible reactions, each cycle shortening the fatty acid by two carbon atoms, evoke competition between intermediates of different chain lengths for a common set of 'promiscuous' enzymes (enzymes with activity towards multiple substrates). In our validated kinetic model of the pathway, substrate overload causes a steep and detrimental flux decline. Here, we unravel the underlying mechanism and the role of enzyme promiscuity in it. Comparison of alternative model versions elucidated the role of promiscuity of individual enzymes. Promiscuity of the last enzyme of the pathway, medium-chain ketoacyl-CoA thiolase (MCKAT), was both necessary and sufficient to elicit the flux decline. Subsequently, Metabolic Control Analysis revealed that MCKAT had insufficient capacity to cope with high substrate influx. Next, we quantified the internal metabolic regulation, revealing a vicious cycle around MCKAT. Upon substrate overload, MCKAT's ketoacyl-CoA substrates started to accumulate. The unfavourable equilibrium constant of the preceding enzyme, medium/short-chain hydroxyacyl-CoA dehydrogenase, worked as an amplifier, leading to accumulation of upstream CoA esters, including acyl-CoA esters. These acyl-CoA esters are at the same time products of MCKAT and inhibited its already low activity further. Finally, the accumulation of CoA esters led to a sequestration of free CoA. CoA being a cofactor for MCKAT, its sequestration limited the MCKAT activity even further, thus completing the vicious cycle. Since CoA is also a substrate for distant enzymes, it efficiently communicated the 'traffic jam' at MCKAT to the entire pathway. This novel mechanism provides a basis to explore the role of mFAO in disease and elucidate similar principles in other pathways of lipid metabolism.

Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli

Background

A kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters.

Results

In this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture.

Conclusions

This model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0511-x) contains supplementary material, which is available to authorized users.

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.

Macromolecular networks and intelligence in microorganisms

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity - particularly activity of the human brain - with a phenomenon we call "intelligence." Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as "human" and "brain" out of the defining features of "intelligence," all forms of life - from microbes to humans - exhibit some or all characteristics consistent with "intelligence." We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

Nitrogen assimilation in Escherichia coli: putting molecular data into a systems perspective

We present a comprehensive overview of the hierarchical network of intracellular processes revolving around central nitrogen metabolism in Escherichia coli. The hierarchy intertwines transport, metabolism, signaling leading to posttranslational modification, and transcription. The protein components of the network include an ammonium transporter (AmtB), a glutamine transporter (GlnHPQ), two ammonium assimilation pathways (glutamine synthetase [GS]-glutamate synthase [glutamine 2-oxoglutarate amidotransferase {GOGAT}] and glutamate dehydrogenase [GDH]), the two bifunctional enzymes adenylyl transferase/adenylyl-removing enzyme (ATase) and uridylyl transferase/uridylyl-removing enzyme (UTase), the two trimeric signal transduction proteins (GlnB and GlnK), the two-component regulatory system composed of the histidine protein kinase nitrogen regulator II (NRII) and the response nitrogen regulator I (NRI), three global transcriptional regulators called nitrogen assimilation control (Nac) protein, leucine-responsive regulatory protein (Lrp), and cyclic AMP (cAMP) receptor protein (Crp), the glutaminases, and the nitrogen-phosphotransferase system. First, the structural and molecular knowledge on these proteins is reviewed. Thereafter, the activities of the components as they engage together in transport, metabolism, signal transduction, and transcription and their regulation are discussed. Next, old and new molecular data and physiological data are put into a common perspective on integral cellular functioning, especially with the aim of resolving counterintuitive or paradoxical processes featured in nitrogen assimilation. Finally, we articulate what still remains to be discovered and what general lessons can be learned from the vast amounts of data that are available now.

Mathematical model of the binding of allosteric effectors to the Escherichia coli PII signal transduction protein GlnB

PII proteins are important regulators of nitrogen metabolism in a wide variety of organisms: the binding of the allosteric effectors ATP, ADP, and 2-oxoglutarate (2-OG) to PII proteins affects their ability to interact with target proteins. We modeled the simultaneous binding of ATP, ADP, and 2-OG to one PII protein, namely GlnB of Escherichia coli, using a modeling approach that allows the prediction of the proportions of individual binding states. Four models with different binding rules were compared. We selected one of these models (that assumes that the binding of the first nucleotide to GlnB makes it harder for subsequent nucleotides to bind) and used it to explore how physiological concentrations of ATP, ADP, and 2-OG would affect the proportions of those states of GlnB that interact with the target proteins ATase and NtrB. Our simulations indicate that GlnB can, as suggested by previous researchers, act as a sensor of both 2-OG and the ATP:ADP ratio. We conclude that our modeling approach will be an important tool in future studies concerning the PII binding states and their interactions with target proteins.

Why in vivo may not equal in vitro - new effectors revealed by measurement of enzymatic activities under the same in vivo-like assay conditions

Does the understanding of the dynamics of biochemical networks in vivo, in terms of the properties of their components determined in vitro, require the latter to be determined all under the same conditions? An in vivo-like assay medium for enzyme activity determination was designed based on the concentrations of the major ionic constituents of the Escherichia coli cytosol: K(+), Na(+), Mg(2+), phosphate, glutamate, sulfate and Cl(-). The maximum capacities (V(max)) of the extracted enzymes of two pathways were determined using both this in vivo-like assay medium and the assay medium specific for each enzyme. The enzyme activities differed between the two assay conditions. Most of the differences could be attributed to unsuspected, pleiotropic effects of K(+) and phosphate. K(+) activated some enzymes (aldolase, enolase and glutamate dehydrogenase) and inhibited others (phosphoglucose isomerase, phosphofructokinase, triosephosphate isomerase, glyceraldehyde 3-phosphate dehydrogenase, phosphoglycerate kinase, phosphoglycerate mutase), whereas phosphate inhibited all glycolytic enzymes and glutamine synthetase but only activated glutamine 2-oxoglutarate amidotransferase. Neither a high glutamate concentration, nor macromolecular crowding affected the glycolytic or nitrogen assimilation enzymes, other than through the product inhibition of glutamate dehydrogenase by glutamate. This strategy of assessing all pathway enzymes kinetically under the same conditions may be necessary to avoid inadvertent differences between in vivo and in vitro biochemistry. It may also serve to reveal otherwise unnoticed pleiotropic regulation, such as that demonstrated in the present study by K(+) and phosphate.

Glycogen is the primary source of glucose during the lag phase of E. coli proliferation

In the studies of Escherichia coli (E. coli), metabolomics analyses have mainly been performed using steady state culture. However, to analyze the dynamic changes in cellular metabolism, we performed a profiling of concentration of metabolites by using batch culture. As a first step, we focused on glucose uptake and the behavior of the first metabolite, G6P (glucose-6-phosphate). A computational formula was derived to express the glucose uptake rate by a single cell from two kinds of experimental data, extracellular glucose concentration and cell growth, being simulated by Cell Illustrator. In addition, average concentration of G6P has been measured by CE-MS. The existence of another carbon source was suggested from the computational result. After careful comparison between cell growth, G6P concentration, and the computationally obtained curve of glucose uptake rate, we predicted the consumption of glycogen in lag phase and its accumulation as an energy source in an E. coli cell for the next proliferation. We confirmed our prediction experimentally. This behavior indicates the importance of glycogen participation in the lag phase for the growth of E. coli. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

Biological design principles of complex feedback modules in the E. coli ammonia assimilation system

To synthesize natural or artificial life, it is critically important to understand the design principles of how biochemical networks generate particular cellular functions and evolve complex systems in comparison with engineering systems. Cellular systems maintain their robustness in the face of perturbations arising from environmental and genetic variations. In analogy to control engineering architectures, the complexity of modular structures within a cell can be attributed to the necessity of achieving robustness. To reveal such biological design, the E. coli ammonia assimilation system is analyzed, which consists of complex but highly structured modules: the glutamine synthetase (GS) activity feedback control module with bifunctional enzyme cascades for catalyzing reversible reactions, and the GS synthesis feedback control module with positive and negative feedback loops. We develop a full-scale dynamic model that unifies the two modules, and we analyze its robustness and fine tuning with respect to internal and external perturbations. The GS activity control is added to the GS synthesis module to improve its transient response to ammonia depletion, compensating the tradeoffs of each module, but its robustness to internal perturbations is lost. These findings suggest some design principles necessary for the synthesis of life.

Systems biology of the metabolic network regulated by the Akt pathway

Cancer has been proposed as an example of systems biology disease or network disease. Accordingly, tumor cells differ from their normal counterparts more in terms of intracellular network dynamics than single markers. Here we shall focus on a recently recognized hallmark of cancer, the deregulation of cellular energetics. The constitutive activation of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway has been confirmed as an essential step toward cell transformation. We will consider how the effects of Akt activation are connected with cell metabolism; more precisely, we will review existing metabolic models and discuss the current knowledge available to construct a kinetic model of the most relevant metabolic processes regulated by the PI3K/Akt pathway. The model will enable a systems biology approach to predict the metabolic targets that may inhibit cell growth under hyper activation of Akt.

Flux balance analysis of ammonia assimilation network in E. coli predicts preferred regulation point

Nitrogen assimilation is a critical biological process for the synthesis of biomolecules in Escherichia coli. The central ammonium assimilation network in E. coli converts carbon skeleton α-ketoglutarate and ammonium into glutamate and glutamine, which further serve as nitrogen donors for nitrogen metabolism in the cell. This reaction network involves three enzymes: glutamate dehydrogenase (GDH), glutamine synthetase (GS) and glutamate synthase (GOGAT). In minimal media, E. coli tries to maintain an optimal growth rate by regulating the activity of the enzymes to match the availability of the external ammonia. The molecular mechanism and the strategy of the regulation in this network have been the research topics for many investigators. In this paper, we develop a flux balance model for the nitrogen metabolism, taking into account of the cellular composition and biosynthetic requirements for nitrogen. The model agrees well with known experimental results. Specifically, it reproduces all the (15)N isotope labeling experiments in the wild type and the two mutant (ΔGDH and ΔGOGAT) strains of E. coli. Furthermore, the predicted catalytic activities of GDH, GS and GOGAT in different ammonium concentrations and growth rates for the wild type, ΔGDH and ΔGOGAT strains agree well with the enzyme concentrations obtained from western blots. Based on this flux balance model, we show that GS is the preferred regulation point among the three enzymes in the nitrogen assimilation network. Our analysis reveals the pattern of regulation in this central and highly regulated network, thus providing insights into the regulation strategy adopted by the bacteria. Our model and methods may also be useful in future investigations in this and other networks.

Systems biochemistry in practice: experimenting with modelling and understanding, with regulation and control

Biology and medicine have become ‘big science’, even though we may not always like this: genomics and the subsequent analysis of what the genomes encode has shown that interesting living organisms require many more than 300 gene products to interact. We once thought that somewhere in this jungle of interacting macromolecules was hidden the molecule that constitutes the secret of Life, and therewith of health and disease. Now we know that, somehow, the secret of Life is the jungle of interactions. Consequently, we need to find the Rosetta Stones, i.e. interpretations of this jungle of systems biology. We need to find, perhaps convoluted, paths of understanding and intervention. Systems biochemistry is a good place to start, as it has the foothold that what goes in must come out. In the present paper, we review two strategies, which look at control and regulation. We discuss the difference between control and regulation and prove a relationship between them.

Signaling properties of a covalent modification cycle are altered by a downstream target

We used a model system of purified components to explore the effects of a downstream target on the signaling properties of a covalent modification cycle, an example of retroactivity. In the experimental system used, a bifunctional enzyme catalyzed the modification and demodification of its substrate protein, with both activities regulated by a small molecule stimulus. Here we examined how a downstream target for one or both forms of the substrate of the covalent modification cycle affected the steady-state output of the system, the sensitivity of the response to the stimulus, and the concentration of the stimulus required to provide the half-maximal response (S(50)). When both the modified and unmodified forms of the substrate protein were sequestered by the downstream target, the sensitivity of the response was dramatically decreased, but the S(50) was only modestly affected. Conversely, when the downstream target only sequestered the unmodified form of the substrate protein, significant effects were observed on both system sensitivity and S(50). Behaviors of the experimental systems were well approximated both by simple models allowing analytical solutions and by a detailed model based on the known interactions and enzymatic activities. Modeling and experimentation indicated that retroactivity may result in subsensitive responses, even if the covalent modification cycle displays significant ultrasensitivity in the absence of retroactivity. Thus, we provide examples of how a downstream target can alter the signaling properties of an upstream signal transduction covalent modification cycle.

Modeling the role of covalent enzyme modification in Escherichia coli nitrogen metabolism

In the bacterium Escherichia coli, the enzyme glutamine synthetase (GS) converts ammonium into the amino acid glutamine. GS is principally active when the cell is experiencing nitrogen limitation, and its activity is regulated by a bicyclic covalent modification cascade. The advantages of this bicyclic-cascade architecture are poorly understood. We analyze a simple model of the GS cascade in comparison to other regulatory schemes and conclude that the bicyclic cascade is suboptimal for maintaining metabolic homeostasis of the free glutamine pool. Instead, we argue that the lag inherent in the covalent modification of GS slows the response to an ammonium shock and thereby allows GS to transiently detoxify the cell, while maintaining homeostasis over longer times.

Metabolomics-driven quantitative analysis of ammonia assimilation in E. coli

Despite extensive study of individual enzymes and their organization into pathways, the means by which enzyme networks control metabolite concentrations and fluxes in cells remains incompletely understood. Here, we examine the integrated regulation of central nitrogen metabolism in Escherichia coli through metabolomics and ordinary-differential-equation-based modeling. Metabolome changes triggered by modulating extracellular ammonium centered around two key intermediates in nitrogen assimilation, α-ketoglutarate and glutamine. Many other compounds retained concentration homeostasis, indicating isolation of concentration changes within a subset of the metabolome closely linked to the nutrient perturbation. In contrast to the view that saturated enzymes are insensitive to substrate concentration, competition for the active sites of saturated enzymes was found to be a key determinant of enzyme fluxes. Combined with covalent modification reactions controlling glutamine synthetase activity, such active-site competition was sufficient to explain and predict the complex dynamic response patterns of central nitrogen metabolites.

Robustness in Escherichia coli glutamate and glutamine synthesis studied by a kinetic model

Metabolic control of glutamine and glutamate synthesis from ammonia and oxoglutarate in Escherichia coli is tight and complex. In this work, the role of glutamine synthetase (GS) and glutamate dehydrogenase (GDH) regulation in this control was studied. Both enzymes form a linear pathway, which can also have a cyclic topology if glutamate-oxoglutarate amino transferase (GOGAT) activity is included. We modelled the metabolic pathways in the linear or cyclic topologies using a coupled nonlinear differential equations system. To simulate GS regulation by covalent modification, we introduced a relationship that took into account the levels of oxoglutarate and glutamine as signal inputs, as well as the ultrasensitive response of enzyme adenylylation. Thus, by including this relationship or not, we were able to model the system with or without GS regulation. In addition, GS and GDH activities were changed manually. The response of the model in different stationary states, or under the influence of N-input exhaustion or oscillation, was analyzed in both pathway topologies. Our results indicate a metabolic control coefficient for GDH ranging from 0.94 in the linear pathway with GS regulation to 0.24 in the cyclic pathway without regulation, employing a default GDH concentration of 8 microM. Thus, in these conditions, GDH seemed to have a high degree of control in the linear pathway while having limited influence in the cyclic one. When GS was regulated, system responses to N-input perturbations were more sensitive, especially in the cyclic pathway. Furthermore, we found that effects of regulation against perturbations depended on the relative values of the glutamine and glutamate output first-order kinetic constants, which we named k(6) and k(7), respectively. Effects of regulation grew exponentially with a factor around 2, with linear increases of (k(7) - k(6)). These trends were sustained but with lower differences at higher GS concentration. Hence, GS regulation seemed important for metabolic stability in a changing environment, depending on the cell's metabolic status.

Identifying and characterising regulatory metabolites with generalised supply-demand analysis

We present the framework of generalised supply-demand analysis (SDA) of a kinetic model of a cellular system, which can be applied to networks of arbitrary complexity. By fixing the concentrations of each of the variable species in turn and varying them in a parameter scan, rate characteristics of supply-demand are constructed around each of these species. By inspecting the shapes of the rate characteristic patterns and comparing the flux-response coefficients of the supply and demand blocks with the elasticities of the enzymes that interact directly with the fixed metabolite, regulatory metabolites in the system can be identified and characterised. The analysis provides information on whether and where the system is functionally differentiated and which of its species are homeostatically buffered. The novelty in our proposed method lies in the fact that all metabolites are considered for SDA (hence the term "generalised"), which removes investigator bias. It supplies an entry point for the further analysis and detailed characterisation of large models of cellular systems, in which the choice of metabolite around which to perform a SDA is not always obvious.

A theoretical steady state analysis indicates that induction of Escherichia coli glnALG operon can display all-or-none behavior

The nitrogen starvation response in Escherichia coli is characterized by the enhanced expression of Ntr regulon, comprising hundreds of genes including the one coding for nitrogen-assimilating glutamine synthetase (GS) enzyme. The biosynthesis and activity of GS is regulated mainly by nitrogen and carbon levels in the cell and monitored by three functionally separable interconnected modules. Here, we present the steady-state modular analysis of this intricate network made up of a GS bicyclic closed-loop cascade, a NRII-NRI two-component system, and an autoregulated glnALG operon encoding genes for GS, NRII, and NRI. Our simulation results indicate that the transcriptional output of glnALG operon is discrete and switch-like, whereas the activation of transcription factor NRI is graded, and the inactivation of GS is moderately ultrasensitive to input stimulus glutamine. The autoregulation of the NRII-NRI two-component system was found to be essential for the all-or-none induction of the glnALG operon. Furthermore, we show that the autoregulated two-component system modulates the total active GS by delineating the GS activity from its biosynthetic regulation. Our analysis indicates that the exclusive relationship between GS activity and its synthesis is brought about by the autoregulated two-component system. The modularity of the network endows the system to respond differently to nitrogen depending on the carbon status of the cell. Through a system-level quantification, we conclude that the discrete switch-like transcriptional response of the E. coli glnALG operon to nutrient starvation prevents the premature initiation of transcription and may represent the desperate attempt by the cell to survive in limiting conditions.

Introduction to systems biology

The developments in the molecular biosciences have made possible a shift to combined molecular and system-level approaches to biological research under the name of Systems Biology. It integrates many types of molecular knowledge, which can best be achieved by the synergistic use of models and experimental data. Many different types of modeling approaches are useful depending on the amount and quality of the molecular data available and the purpose of the model. Analysis of such models and the structure of molecular networks have led to the discovery of principles of cell functioning overarching single species. Two main approaches of systems biology can be distinguished. Top-down systems biology is a method to characterize cells using system-wide data originating from the Omics in combination with modeling. Those models are often phenomenological but serve to discover new insights into the molecular network under study. Bottom-up systems biology does not start with data but with a detailed model of a molecular network on the basis of its molecular properties. In this approach, molecular networks can be quantitatively studied leading to predictive models that can be applied in drug design and optimization of product formation in bioengineering. In this chapter we introduce analysis of molecular network by use of models, the two approaches to systems biology, and we shall discuss a number of examples of recent successes in systems biology.

Integration of regulatory signals through involvement of multiple global regulators: control of the Escherichia coli gltBDF operon by Lrp, IHF, Crp, and ArgR

Background

The glutamate synthase operon (gltBDF) contributes to one of the two main pathways of ammonia assimilation in Escherichia coli. Of the seven most-global regulators, together affecting expression of about half of all E. coli genes, two were previously shown to exert direct, positive control on gltBDF transcription: Lrp and IHF. The involvement of Lrp is unusual in two respects: first, it is insensitive to the usual coregulator leucine, and second, Lrp binds more than 150 bp upstream of the transcription starting point. There was indirect evidence for involvement of a third global regulator, Crp. Given the physiological importance of gltBDF, and the potential opportunity to learn about integration of global regulatory signals, a combination of in vivo and in vitro approaches was used to investigate the involvement of additional regulatory proteins, and to determine their relative binding positions and potential interactions with one another and with RNA polymerase (RNAP).

Results

Crp and a more local regulator, ArgR, directly control gltBDF transcription, both acting negatively. Crp-cAMP binds a sequence centered at -65.5 relative to the transcript start. Mutation of conserved nucleotides in the Crp binding site abolishes the Crp-dependent repression. ArgR also binds to the gltBDF promoter region, upstream of the Lrp binding sites, and decreases transcription. RNAP only yields a defined DNAse I footprint under two tested conditions: in the presence of both Lrp and IHF, or in the presence of Crp-cAMP. The DNAse I footprint of RNAP in the presence of Lrp and IHF is altered by ArgR.

Conclusion

The involvement of nearly half of E. coli's most-global regulatory proteins in the control of gltBDF transcription is striking, but seems consistent with the central metabolic role of this operon. Determining the mechanisms of activation and repression for gltBDF was beyond the scope of this study. However the results are consistent with a model in which IHF bends the DNA to allow stabilizing contacts between Lrp and RNAP, ArgR interferes with such contacts, and Crp introduces an interfering bend in the DNA and/or stabilizes RNAP in a poised but inactive state.

Metabolic control analysis to identify optimal drug targets

This chapter describes the basic principles of Metabolic Control Analysis (MCA) which is a quantitative methodology to evaluate the importance and relative contribution of individual metabolic steps in the overall functioning of a particular system. The control on the flux through a metabolic pathway or subsystem can be quantified by the control coefficients of the individual enzymes or components which reflects the extent to which the component is rate-limiting. The perturbation of an individual step is measured by its elasticity coefficient. The effect of perturbation of a single step on the entire pathway or subsystem is, in turn, measured by the response coefficient. Differential control analysis can be used to compare flux through a single metabolic pathway in a pathogen with the same pathway in its host to identify uniquely vulnerable steps with the greatest potential for specifically inhibiting flux through the pathogen metabolic pathway. The utility of this methodology is illustrated with the glycolysis in Trypanosomes and with oncogenic signaling.

The nature of systems biology

The advent of functional genomics has enabled the molecular biosciences to come a long way towards characterizing the molecular constituents of life. Yet, the challenge for biology overall is to understand how organisms function. By discovering how function arises in dynamic interactions, systems biology addresses the missing links between molecules and physiology. Top-down systems biology identifies molecular interaction networks on the basis of correlated molecular behavior observed in genome-wide "omics" studies. Bottom-up systems biology examines the mechanisms through which functional properties arise in the interactions of known components. Here, we outline the challenges faced by systems biology and discuss limitations of the top-down and bottom-up approaches, which, despite these limitations, have already led to the discovery of mechanisms and principles that underlie cell function.

Approaches to biosimulation of cellular processes

Modelling and simulation are at the heart of the rapidly developing field of systems biology. This paper reviews various types of models, simulation methods, and theoretical approaches that are presently being used in the quantitative description of cellular processes. We first describe how molecular interaction networks can be represented by means of stoichiometric, topological and kinetic models. We briefly discuss the formulation of kinetic models using mesoscopic (stochastic) or macroscopic (continuous) approaches, and we go on to describe how detailed models of molecular interaction networks (silicon cells) can be constructed on the basis of experimentally determined kinetic parameters for cellular processes. We show how theory can help in analyzing models by applying control analysis to a recently published silicon cell model. Finally, we review some of the theoretical approaches available to analyse kinetic models and experimental data, respectively.

The Silicon Cell initiative: working towards a detailed kinetic description at the cellular level

The Silicon Cell initiative aims to understand cellular systems on the basis of the characteristics of their components. As a tool to achieve this, detailed kinetic models at the network reaction level are being constructed. Such detailed kinetic models are extremely useful for medical and biotechnological applications and form strong tools for fundamental studies. Several recently constructed detailed kinetic models on metabolism (glycolysis), signal transduction (EGF receptor), and the eukaryotic cell cycle (Saccharomyces cerevisiae) have been used to exemplify the Silicon Cell project. These models are stored and made accessible via the JWS Online Cellular Systems Modeling project, a web-based repository of kinetic models. Using a web-browser the models can be interrogated via a user-friendly graphical interface. The goal of the two projects is to combine models on parts of cellular systems and ultimately to construct detailed kinetic models at the cellular level.