Sequence-based analysis of metabolic demands for protein synthesis in prokaryotes

Integrated Constraint-Based Modeling of E. coli Cell-Free Protein Synthesis

Cell-free protein expression has become a widely used research tool in systems and synthetic biology and a promising technology for protein biomanufacturing. Cell-free protein synthesis relies on in-vitro transcription and translation processes to produce a protein of interest. However, transcription and translation depend upon the operation of complex metabolic pathways for precursor and energy regeneration. Toward understanding the role of metabolism in a cell-free system, we developed a dynamic constraint-based simulation of protein production in the myTXTL E. coli cell-free system with and without electron transport chain inhibitors. Time-resolved absolute metabolite measurements for â"³ = 63 metabolites, along with absolute concentration measurements of the mRNA and protein abundance and measurements of enzyme activity, were integrated with kinetic and enzyme abundance information to simulate the time evolution of metabolic flux and protein production with and without inhibitors. The metabolic flux distribution estimated by the model, along with the experimental metabolite and enzyme activity data, suggested that the myTXTL cell-free system has an active central carbon metabolism with glutamate powering the TCA cycle. Further, the electron transport chain inhibitor studies suggested the presence of oxidative phosphorylation activity in the myTXTL cell-free system; the oxidative phosphorylation inhibitors provided biochemical evidence that myTXTL relied, at least partially, on oxidative phosphorylation to generate the energy required to sustain transcription and translation for a 16-hour batch reaction.

Modeling Cell-Free Protein Synthesis Systems-Approaches and Applications

In vitro systems are ideal setups to investigate the basic principles of biochemical reactions and subsequently the bricks of life. Cell-free protein synthesis (CFPS) systems mimic the transcription and translation processes of whole cells in a controlled environment and allow the detailed study of single components and reaction networks. In silico studies of CFPS systems help us to understand interactions and to identify limitations and bottlenecks in those systems. Black-box models laid the foundation for understanding the production and degradation dynamics of macromolecule components such as mRNA, ribosomes, and proteins. Subsequently, more sophisticated models revealed shortages in steps such as translation initiation and tRNA supply and helped to partially overcome these limitations. Currently, the scope of CFPS modeling has broadened to various applications, ranging from the screening of kinetic parameters to the stochastic analysis of liposome-encapsulated CFPS systems and the assessment of energy supply properties in combination with flux balance analysis (FBA).

The Evolution of Cell Free Biomanufacturing

Cell-free systems are a widely used research tool in systems and synthetic biology and a promising platform for manufacturing of proteins and chemicals. In the past, cell-free biology was primarily used to better understand fundamental biochemical processes. Notably, E. coli cell-free extracts were used in the 1960s to decipher the sequencing of the genetic code. Since then, the transcription and translation capabilities of cell-free systems have been repeatedly optimized to improve energy efficiency and product yield. Today, cell-free systems, in combination with the rise of synthetic biology, have taken on a new role as a promising technology for just-in-time manufacturing of therapeutically important biologics and high-value small molecules. They have also been implemented at an industrial scale for the production of antibodies and cytokines. In this review, we discuss the evolution of cell-free technologies, in particular advancements in extract preparation, cell-free protein synthesis, and cell-free metabolic engineering applications. We then conclude with a discussion of the mathematical modeling of cell-free systems. Mathematical modeling of cell-free processes could be critical to addressing performance bottlenecks and estimating the costs of cell-free manufactured products.

Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli

In this study, we developed a dynamic mathematical model of E. coli cell-free protein synthesis (CFPS). Model parameters were estimated from a dataset consisting of glucose, organic acids, energy species, amino acids, and protein product, chloramphenicol acetyltransferase (CAT) measurements. The model was successfully trained to simulate these measurements, especially those of the central carbon metabolism. We then used the trained model to evaluate the performance, e.g., the yield and rates of protein production. CAT was produced with an energy efficiency of 12%, suggesting that the process could be further optimized. Reaction group knockouts showed that protein productivity was most sensitive to the oxidative phosphorylation and glycolysis/gluconeogenesis pathways. Amino acid biosynthesis was also important for productivity, while overflow metabolism and TCA cycle affected the overall system state. In addition, translation was more important to productivity than transcription. Finally, CAT production was robust to allosteric control, as were most of the predicted metabolite concentrations; the exceptions to this were the concentrations of succinate and malate, and to a lesser extent pyruvate and acetate, which varied from the measured values when allosteric control was removed. This study is the first to use kinetic modeling to predict dynamic protein production in a cell-free E. coli system, and could provide a foundation for genome scale, dynamic modeling of cell-free E. coli protein synthesis.

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Protein production is biphasic, powered initially by glucose and later by pyruvate.

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Protein is produced with an energy efficiency of only 12%.

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Protein productivity is most sensitive to oxidative phosphorylation and glycolysis.

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Protein production is robust to allosteric control.

Sequence Specific Modeling of E. coli Cell-Free Protein Synthesis

Cell-free protein synthesis (CFPS) is a widely used research tool in systems and synthetic biology. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Toward this question, we used sequence specific constraint based modeling to evaluate the performance of E. coli cell-free protein synthesis. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis, and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Model parameters were largely taken from literature; thus the constraint based approach coupled the transcription and translation of the protein product, and the regulation of gene expression, with the availability of metabolic resources using only a limited number of adjustable model parameters. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have data sets; we then expanded the simulations to a range of additional proteins. Protein expression simulations were consistent with measurements for a variety of cases. The constraint based simulations confirmed that oxidative phosphorylation was active in the CAT cell-free extract, as without it there was no feasible solution within the experimental constraints of the system. We then compared the metabolism of theoretically optimal and experimentally constrained CFPS reactions, and developed parameter free correlations which could be used to estimate productivity as a function of carbon number and promoter type. Lastly, global sensitivity analysis identified the key metabolic processes that controlled CFPS productivity and energy efficiency. In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of adjustable parameters. While we modeled the production of a single protein in this study, the approach could easily be extended to multiprotein synthetic circuits, RNA circuits, or the cell-free production of small molecule products.

Intermolecular hydrogen bonds between 1,4-benzoquinones and HF molecule: Synergetic effects, reduction potentials and electron affinities

Some biological activities of quinones can be attributed to the H-bonding ability of acceptor oxygen atoms. According to the results obtained from the quantum mechanical calculations performed on a wide variety of complexes between the 1,4-benzoquinone (BQ) derivatives and HF molecules, the interplay between H-bonds and individual H-bond interaction energies (E_HB) can be affected by the substituents placed on the six-membered ring of BQ. The total binding energies of complexes become more negative by the electron donating substituents (EDSs) while the changes are reversed by the electron withdrawing substituents (EWSs). The mutual interplay between the X-BQ⋯(HF)_n (n=1-3) interactions has been investigated using the geometrical parameters, synergetic energies (SE) and the E_HB values. Hydrogen bonding decreases the reduction potentials (E⁰_red) and increases the electron affinities (EA) of X-BQ derivatives. Linear relationships have been observed between the E⁰_red (and EA) values and the Hammett constants of substituents.

Dynamic optimization of metabolic networks coupled with gene expression

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle.

Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli

The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype-phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy.

In silico method for modelling metabolism and gene product expression at genome scale

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis

Background

There is a growing realization that alterations in host-pathogen interactions (HPI) can generate disease phenotypes without pathogen invasion. The gut represents a prime region where such HPI can arise and manifest. Under normal conditions intestinal microbial communities maintain a stable, mutually beneficial ecosystem. However, host stress can lead to changes in environmental conditions that shift the nature of the host-microbe dialogue, resulting in escalation of virulence expression, immune activation and ultimately systemic disease. Effective modulation of these dynamics requires the ability to characterize the complexity of the HPI, and dynamic computational modeling can aid in this task. Agent-based modeling is a computational method that is suited to representing spatially diverse, dynamical systems. We propose that dynamic knowledge representation of gut HPI with agent-based modeling will aid in the investigation of the pathogenesis of gut-derived sepsis.

Methodology/Principal Findings

An agent-based model (ABM) of virulence regulation in Pseudomonas aeruginosa was developed by translating bacterial and host cell sense-and-response mechanisms into behavioral rules for computational agents and integrated into a virtual environment representing the host-microbe interface in the gut. The resulting gut milieu ABM (GMABM) was used to: 1) investigate a potential clinically relevant laboratory experimental condition not yet developed - i.e. non-lethal transient segmental intestinal ischemia, 2) examine the sufficiency of existing hypotheses to explain experimental data - i.e. lethality in a model of major surgical insult and stress, and 3) produce behavior to potentially guide future experimental design - i.e. suggested sample points for a potential laboratory model of non-lethal transient intestinal ischemia. Furthermore, hypotheses were generated to explain certain discrepancies between the behaviors of the GMABM and biological experiments, and new investigatory avenues proposed to test those hypotheses.

Conclusions/Significance

Agent-based modeling can account for the spatio-temporal dynamics of an HPI, and, even when carried out with a relatively high degree of abstraction, can be useful in the investigation of system-level consequences of putative mechanisms operating at the individual agent level. We suggest that an integrated and iterative heuristic relationship between computational modeling and more traditional laboratory and clinical investigations, with a focus on identifying useful and sufficient degrees of abstraction, will enhance the efficiency and translational productivity of biomedical research.

Bacterial growth rate reflects a bottleneck in resource allocation

BACKGROUND

Growth rate management in fast-growing bacteria is currently an active research area. In spite of the huge progress made in our understanding of the molecular mechanisms controlling the growth rate, fundamental questions concerning its intrinsic limitations are still relevant today. In parallel, systems biology claims that mathematical models could shed light on these questions.

METHODS

This review explores some possible reasons for the limitation of the growth rate in fast-growing bacteria, using a systems biology approach based on constraint-based modeling methods.

RESULTS

Recent experimental results and a new constraint-based modelling method named Resource Balance Analysis (RBA) reveal the existence of constraints on resource allocation between biological processes in bacterial cells. In this context, the distribution of a finite amount of resources between the metabolic network and the ribosomes limits the growth rate, which implies the existence of a bottleneck between these two processes. Any mechanism for saving resources increases the growth rate.

GENERAL SIGNIFICANCE

Consequently, the emergence of genetic regulation of metabolic pathways, e.g. catabolite repression, could then arise as a means to minimise the protein cost, i.e. maximising growth performance while minimising the resource allocation. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization

Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship.

Systems biology aims to understand the interactions of cellular components in a systemic manner. Mathematical modeling is critical to the integration and analysis of these components on a conceptual as well as mechanistic level. To date, detailed genome-scale reconstructions of metabolism have become available for a growing number of organisms. Although metabolism has an important role in cells, other cellular functions need to be considered as well, such as signaling, regulation, and macromolecular synthesis. For instance, the cellular machinery required for RNA and protein synthesis consists of a complex set of proteins. Here, we show that one can collect all of the necessary information for a prokaryotic organism to create a gene-specific, fine-grained representation of the macromolecular synthesis machinery. E. coli was chosen as a model organism because of the wealth of available information. The explicit representation of transcription and translation in terms of a mass-balanced network enables a detailed, quantitative accounting of the protein synthesis capabilities of E. coli in silico. Hence, this study demonstrates the feasibility of constructing very large networks and also represents a critical step toward building cellular models of growth that can account for gene-specific protein production in a stoichiometric fashion on the genome scale.

Reconstruction of biochemical networks in microorganisms

Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

Uropathogenic Escherichia coli CFT073 is adapted to acetatogenic growth but does not require acetate during murine urinary tract infection

In vivo accumulation of D-serine by Escherichia coli CFT073 leads to elevated expression of PAP fimbriae and hemolysin by an unknown mechanism. Loss of D-serine catabolism by CFT073 leads to a competitive advantage during murine urinary tract infection (UTI), but loss of both D- and L-serine catabolism results in attenuation. Serine is the first amino acid to be consumed in closed tryptone broth cultures and precedes the production of acetyl phosphate, a high-energy molecule involved in intracellular signaling, and the eventual secretion of acetate. We propose that the colonization defect associated with the loss of serine catabolism is due to perturbations of acetate metabolism. CFT073 grows more rapidly on acetogenic substrates than does E. coli K-12 isolate MG1655. As shown by transcription microarray results, D-serine is catabolized into acetate via the phosphotransacetylase (pta) and acetate kinase (ackA) genes while downregulating expression of acetyl coenzyme A synthase (acs). CFT073 acs, which is unable to reclaim secreted acetate, colonized mouse bladders and kidneys in the murine model of UTI indistinguishably from the wild type. Both pta and ackA are involved in the maintenance of intracellular acetyl phosphate. CFT073 pta and ackA mutants were screened to investigate the role of acetyl phosphate in UTI pathogenesis. Both single mutants are at a competitive disadvantage relative to the wild type in the kidneys but normally colonize the bladder. CFT073 ackA pta was attenuated in both the bladder and the kidneys. Thus, we demonstrate that CFT073 is adapted to acetate metabolism as a result of requiring a proper cycling of the acetyl phosphate pathway for colonization of the upper urinary tract.

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded over the past 15 years. In particular, Escherichia coli metabolic network reconstruction has reached the genome scale and been utilized to address a broad spectrum of basic and practical applications in five main categories: metabolic engineering, model-directed discovery, interpretations of phenotypic screens, analysis of network properties and studies of evolutionary processes. Spurred on by these accomplishments, the field is expected to move forward and further broaden the scope and content of network reconstructions, develop new and novel in silico analysis tools, and expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.

Predicting gene essentiality using genome-scale in silico models

Genome-scale metabolic models of organisms can be reconstructed using annotated genome sequence information, well-curated databases, and primary research literature. The metabolic reaction stoichiometry and other physicochemical factors are incorporated into the model, thus imposing constraints that represent restrictions on phenotypic behavior. Based on this premise, the theoretical capabilities of the metabolic network can be assessed by using a mathematical technique known as flux balance analysis (FBA). This modeling framework, also known as the constraint-based reconstruction and analysis approach, differs from other modeling strategies because it does not attempt to predict exact network behavior. Instead, this approach uses known constraints to separate the states that a system can achieve from those that it cannot. In recent years, this strategy has been employed to probe the metabolic capabilities of a number of organisms, to generate and test experimental hypotheses, and to predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the constraint-based modeling approach and focuses on its application to computationally predicting gene essentiality.

Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach

The increasing availability of various system-level, or so-called 'omics', datasets, in concert with existing data from the primary research literature, is facilitating the development of genome-scale metabolic models for many organisms. By incorporating the metabolic reaction stoichiometry as well as other physicochemical properties into systemic network reconstructions, these models account for the constraints that restrict an organism's phenotypic behavior. Accordingly, unlike many contemporary modeling strategies, this constraint-based modeling approach does not attempt to predict network behavior exactly; rather, it seeks to clearly distinguish those network states that a system can achieve from those that it cannot. A variety of analytical tools have been designed and developed to probe these models, thus enabling studies that investigate the metabolic capabilities of a number of organisms, that generate and test experimental hypotheses, and that predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the concepts that underlie the constraint-based modeling approach, and describes several of its applications with an emphasis on those potentially relevant to the drug development field. In addition, while this chapter focuses on the primary application of the constraint-based approach to date, namely in modeling metabolic networks, the latter sections of the chapter discuss its relatively recent application to modeling other cellular systems. Finally, the chapter concludes with an assessment of future directions focusing on the efforts that will be required to utilize the constraint-based approach in generating a holistic model of a viable organism.

In vivo gene expression analysis identifies genes required for enhanced colonization of the mouse urinary tract by uropathogenic Escherichia coli strain CFT073 dsdA

Deletional inactivation of the gene encoding d-serine deaminase, dsdA, in uropathogenic Escherichia coli strain CFT073 results in a hypermotile strain with a hypercolonization phenotype in the bladder and kidneys of mice in a model of urinary tract infection (UTI). The in vivo gene expression profiles of CFT073 and CFT073 dsdA were compared by isolating RNA directly from the urine of mice challenged with each strain individually. Hybridization of cDNAs derived from these samples to CFT073-specific microarrays allowed identification of genes that were up- or down-regulated in the dsdA deletion strain during UTI. Up-regulated genes included the known d-serine-responsive gene dsdX, suggesting in vivo intracellular accumulation of d-serine by CFT073 dsdA. Genes encoding F1C fimbriae, both copies of P fimbriae, hemolysin, OmpF, a dipeptide transporter DppA, a heat shock chaperone IbpB, and clusters of open reading frames with unknown functions were also up-regulated. To determine the role of these genes as well as motility in the hypercolonization phenotype, mutants were constructed in the CFT073 dsdA background and tested in competition against the wild type in the murine model of UTI. Strains with deletions of one or both of the two P fimbrial operons, hlyA, fliC, ibpB, c0468, locus c3566 to c3568, or c2485 to c2490 colonized mouse bladders and kidneys at levels indistinguishable from wild type. CFT073 dsdA c2398 and CFT073 dsdA focA maintained a hypercolonization phenotype. A CFT073 dsdA dppA mutant was attenuated 10- to 50-fold in its colonization ability compared to CFT073. Our results support a role for d-serine catabolism and signaling in global virulence gene regulation of uropathogenic E. coli.

Contribution of respiratory burst activity to innate immune function and the effects of disease status and agent on chemiluminescence responses by ruminant phagocytes in vitro

The mechanisms of interaction between phagocytes and different bacteria that help resolve lung infections or contribute to lung pathology are poorly defined. Alveolar phagocytes (resident macrophages and recruited neutrophils) make a major contribution to innate immunity by mounting a respiratory burst that helps kill internalised bacteria. However, this ability may be altered during or after exposure to infection. This review considers the application and limitations of a variety of analytical methods for oxygen-dependent mechanisms of respiratory burst in phagocytes initiated by soluble and particulate activators. Particular reference is given to the study in vitro of phagocytes from healthy and diseased ruminants during either natural infection with Mycobacterium avium paratuberculosis or experimental infection with Pasteurella multocida or Mannheimia haemolytica.

Coordinate expression of fimbriae in uropathogenic Escherichia coli

Uropathogenic Escherichia coli is the most common etiological agent of urinary tract infections. Bacteria can often express multiple adhesins during infection in order to favor attachment to specific niches within the urinary tract. We have recently demonstrated that type 1 fimbria, a phase-variable virulence factor involved in adherence, was the most highly expressed adhesin during urinary tract infection. Here, we examine whether the expression of type 1 fimbriae can affect the expression of other adhesins. Type 1 fimbrial phase-locked mutants of E. coli strain CFT073, which harbors genes for numerous adhesins, were employed in this study. CFT073-specific DNA microarray analysis of these strains demonstrates that the expression of type 1 fimbriae coordinately affects the expression of P fimbriae in an inverse manner. This represents evidence for direct communication between genes relating to pathogenesis, perhaps to aid the sequential occupation of different urinary tract tissues. While the role of type 1 fimbriae during infection has been clear, the role of P fimbriae must be further defined to assert the relevance of coordinated regulation in vivo. Therefore, we examined the ability of P fimbrial isogenic mutants, constructed in a type 1 fimbrial-negative background, to compete in the murine urinary tract over a period of 168 h. No differences in the colonization of these mutants were observed. However, comparison of these results with previous studies suggests that inversely coordinated expression of adhesin gene clusters does occur in vivo. Interestingly, the mutant that was incapable of expressing either type 1 or P fimbriae compensated by synthesizing F1C fimbriae.

Stochastic kinetics of reproduction of virions inside a cell

We analyze intracellular viral kinetics in the framework of the model incorporating viral genome replication, mRNA synthesis and degradation, protein synthesis and degradation, capsid assembly, and virion release from a cell. Due to the existence of the critical concentration of viral capsid proteins and other features of reproduction of virions inside a cell, the kinetics is demonstrated to exhibit three distinct initial stages. Specifically, (i) the exponential growth of the viral genome, mRNA and protein concentrations is followed by (ii) the transient stage to (iii) the steady-state regime. The formation of mature virions starts during the transient stage. Comparison of the kinetics, obtained by using the mass-action law and Monte Carlo (MC) technique, indicates that they are nearly identical during the initial exponential growth of the viral intermediates and also during the steady-state stage. The transition from the initial stage to the steady-state regime occurs however somewhat faster in the determenistic case even if the steady-state populations of virions and genomes are appreciable (e.g., about 250 and 500, respectively).

Monte Carlo simulation of bifurcation in the intracellular viral kinetics

Intracellular viral kinetics are of special interest because the virion population inside an infected cell is well known to tend to grow exponentially and the corresponding kinetics may be unstable. To clarify the special features of such kinetics, we present Monte Carlo simulations taking into account the key steps of virion formation and competition of the host and viral mRNA for the host translation apparatus. Asymptotically, the model employed predicts either a stable steady state or 'ignition' with the unlimited viral growth. Under steady-state conditions, the mean square fluctuations of the viral genome and virion numbers are found to be appreciably larger than those expected on the basis of the Poissonian distribution. In the case of unstable kinetics, the simulations show the type of deviations from the corresponding mean-field results.

Genome-scale models of microbial cells: evaluating the consequences of constraints

Microbial cells operate under governing constraints that limit their range of possible functions. With the availability of annotated genome sequences, it has become possible to reconstruct genome-scale biochemical reaction networks for microorganisms. The imposition of governing constraints on a reconstructed biochemical network leads to the definition of achievable cellular functions. In recent years, a substantial and growing toolbox of computational analysis methods has been developed to study the characteristics and capabilities of microorganisms using a constraint-based reconstruction and analysis (COBRA) approach. This approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells.

Transcriptome of uropathogenic Escherichia coli during urinary tract infection

A uropathogenic Escherichia coli strain CFT073-specific DNA microarray that includes each open reading frame was used to analyze the transcriptome of CFT073 bacteria isolated directly from the urine of infected CBA/J mice. The in vivo expression profiles were compared to that of E. coli CFT073 grown statically to exponential phase in rich medium, revealing the strategies this pathogen uses in vivo for colonization, growth, and survival in the urinary tract environment. The most highly expressed genes overall in vivo encoded translational machinery, indicating that the bacteria were in a rapid growth state despite specific nutrient limitations. Expression of type 1 fimbriae, a virulence factor involved in adherence, was highly upregulated in vivo. Five iron acquisition systems were all highly upregulated during urinary tract infection, as were genes responsible for capsular polysaccharide and lipopolysaccharide synthesis, drug resistance, and microcin secretion. Surprisingly, other fimbrial genes, such as pap and foc/sfa, and genes involved in motility and chemotaxis were downregulated in vivo. E. coli CFT073 grown in human urine resulted in the upregulation of iron acquisition, capsule, and microcin secretion genes, thus partially mimicking growth in vivo. On the basis of gene expression levels, the urinary tract appears to be nitrogen and iron limiting, of high osmolarity, and of moderate oxygenation. This study represents the first assessment of any E. coli pathotype's transcriptome in vivo and provides specific insights into the mechanisms necessary for urinary tract pathogenesis.

Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model

A fully compartmentalized genome-scale metabolic model of Saccharomyces cerevisiae that accounts for 750 genes and their associated transcripts, proteins, and reactions has been reconstructed and validated. All of the 1149 reactions included in this in silico model are both elementally and charge balanced and have been assigned to one of eight cellular locations (extracellular space, cytosol, mitochondrion, peroxisome, nucleus, endoplasmic reticulum, Golgi apparatus, or vacuole). When in silico predictions of 4154 growth phenotypes were compared to two published large-scale gene deletion studies, an 83% agreement was found between iND750's predictions and the experimental studies. Analysis of the failure modes showed that false predictions were primarily caused by iND750's limited inclusion of cellular processes outside of metabolism. This study systematically identified inconsistencies in our knowledge of yeast metabolism that require specific further experimental investigation.

Topological analysis of mass-balanced signaling networks: a framework to obtain network properties including crosstalk

Signal transduction networks have only been studied at a small scale because large-scale reconstructions and suitable in silico analysis methods have not been available. Since reconstructions of large signaling networks are progressing well there is now a need to develop a framework for analysing structural properties of signaling networks. One such framework is presented here, one that is based on systemically independent pathways and a mass-balanced representation of signaling events. This approach was applied to a prototypic signaling network and it allowed for: (1) a systemic analysis of all possible input/output relationships, (2) a quantitative evaluation of network crosstalk, or the interconnectivity of systemically independent pathways, (3) a measure of the redundancy in the signaling network, (4) the participation of reactions in signaling pathways, and (5) the calculation of correlated reaction sets. These properties emerge from network structure and can only be derived and studied within a defined mathematical framework. The calculations presented are the first of their kind for a signaling network, while similar analysis has been extensively performed for prototypic and genome-scale metabolic networks. This approach does not yet account for dynamic concentration profiles. Due to the scalability of the stoichiometric formalism used, the results presented for the prototypic signaling network can be obtained for large signaling networks once their reconstruction is completed.

Genome-scale analysis of the uses of the Escherichia coli genome: model-driven analysis of heterogeneous data sets

The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model.

Genome-scale microbial in silico models: the constraints-based approach

Genome sequencing and annotation has enabled the reconstruction of genome-scale metabolic networks. The phenotypic functions that these networks allow for can be defined and studied using constraints-based models and in silico simulation. Several useful predictions have been obtained from such in silico models, including substrate preference, consequences of gene deletions, optimal growth patterns, outcomes of adaptive evolution and shifts in expression profiles. The success rate of these predictions is typically in the order of 70-90% depending on the organism studied and the type of prediction being made. These results are useful as a basis for iterative model building and for several practical applications.