Unraveling the complexity of flux regulation: a new method demonstrated for nutrient starvation in Saccharomyces cerevisiae

Flux-balance modeling of plant metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement.

A survey on methods for modeling and analyzing integrated biological networks

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

Comparative systems biology: from bacteria to man

Comparative analyses, as carried out by comparative genomics and bioinformatics, have proven extremely powerful to obtain insight into the identity of specific genes that underlie differences and similarities across species. The central concept developed in this chapter is that important aspects of the functional differences between organisms derive not only from the differences in genetic components (which underlies comparative genomics) but also from dynamic, molecular (physical) interactions. Approaches that aim at identifying such network-based rather than component-based homologies between species we shall call Comparative Systems Biology. It will be illustrated by a number of examples from metabolic networks from prokaryotes, via yeast, to man. The potential for species comparisons, at the genome-scale using classical approaches and at the more detailed level of dynamic molecular networks will be illustrated. In our opinion, comparative systems biology, as a marriage between bioinformatics and systems biology, will offer new insights into the nature of organisms for the benefit of medicine, biotechnology, and drug design. As dynamic modeling is becoming more mainstream in cell biology, the potential of comparative systems biology will become more evident.

Quantitative analysis of flux regulation through hierarchical regulation analysis

Regulation analysis is a methodology that quantifies to what extent a change in the flux through a metabolic pathway is regulated by either gene expression or metabolism. Two extensions to regulation analysis were developed over the past years: (i) the regulation of V(max) can be dissected into the various levels of the gene-expression cascade, such as transcription, translation, protein degradation, etc. and (ii) a time-dependent version allows following flux regulation when cells adapt to changes in their environment. The methodology of the original form of regulation analysis as well as of the two extensions will be described in detail. In addition, we will show what is needed to apply regulation analysis in practice. Studies in which the different versions of regulation analysis were applied revealed that flux regulation was distributed over various processes and depended on time, enzyme, and condition of interest. In the case of the regulation of glycolysis in baker's yeast, it appeared, however, that cells that remain under respirofermentative conditions during a physiological challenge tend to invoke more gene-expression regulation, while a shift between respirofermentative and respiratory conditions invokes an important contribution of metabolic regulation. The complexity of the regulation observed in these studies raises the question what is the advantage of this highly distributed and condition-dependent flux regulation.

Modelling dynamic plant cells

A major challenge in plant biology is to understand how functions in plant cells emerge from interactions between molecular components. Computational and mathematical modelling can encapsulate the relationships between molecular components and reveal how biological functions emerge. We review recent progress in modelling in metabolism, growth, signalling and circadian rhythms in plant cells. We discuss challenges and opportunities for future directions.

Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model

Motivation: The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations.

Results: IOMA is shown to successfully predict the metabolic state of human erythrocytes (compared to kinetic model simulations), showing a significant advantage over the commonly used methods flux balance analysis and minimization of metabolic adjustment. Thereafter, IOMA is shown to correctly predict metabolic fluxes in Escherichia coli under different gene knockouts for which both metabolomic and proteomic data is available, achieving higher prediction accuracy over the extant methods. Considering the lack of high-throughput flux measurements, while high-throughput metabolomic and proteomic data are becoming readily available, we expect IOMA to significantly contribute to future research of cellular metabolism.

Contacts:kerenyiz@post.tau.ac.il; tomersh@cs.technion.ac.il

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.

Organising metabolic networks: Cycles in flux distributions

Metabolic networks are among the most widely studied biological systems. The topology and interconnections of metabolic reactions have been well described for many species. This is, however, not sufficient to understand how their activity is regulated in living organisms. These descriptions depict a static set of possible chains of reactions, with no information about the dynamic activity of reaction fluxes. Cyclic structures are thought to play a central role in the homeostasis of biological systems and in their resilience to a changing environment. In this work, we present a methodology to help investigating dynamic fluxes associated to biochemical reactions in metabolic networks. We introduce an algorithm for partitioning fluxes between cyclic and acyclic sub-networks, adapted from an algorithm initially developed to study fluxes in trophic networks. Using this algorithm, we analyse three metabolic systems: the central metabolism of wild type and a deletion mutant of Escherichia coli, erythrocyte metabolism and the central metabolism of the bacterium Methylobacterium extorquens. This methodology unveils the role of cycles in driving and maintaining metabolic fluxes under perturbations in these examples, and may be used to further investigate and understand the organisational invariance of biological systems.

Tissue-level modeling of xenobiotic metabolism in liver: An emerging tool for enabling clinical translational research

This review summarizes some of the recent developments and identifies critical challenges associated with in vitro and in silico representations of the liver and assesses the translational potential of these models in the quest of rationalizing the process of evaluating drug efficacy and toxicity. It discusses a wide range of research efforts that have produced, during recent years, quantitative descriptions and conceptual as well as computational models of hepatic processes such as biotransport and biotransformation, intra- and intercellular signal transduction, detoxification, etc. The above mentioned research efforts cover multiple scales of biological organization, from molecule-molecule interactions to reaction network and cellular and histological dynamics, and have resulted in a rapidly evolving knowledge base for a "systems biology of the liver." Virtual organ/organism formulations represent integrative implementations of particular elements of this knowledge base, usually oriented toward the study of specific biological endpoints, and provide frameworks for translating the systems biology concepts into computational tools for quantitative prediction of responses to stressors and hypothesis generation for experimental design.

Interpreting metabolomic profiles using unbiased pathway models

Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules"--regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.

Mitigating release of the potent greenhouse gas N(2)O from the nitrogen cycle - could enzymic regulation hold the key?

When faced with a shortage of oxygen, many bacterial species use nitrate to support respiration via the process of denitrification. This takes place extensively in nitrogen-rich soils and generates the gaseous products nitric oxide (NO), nitrous oxide (N(2)O) and dinitrogen (N(2)). The denitrifying bacteria protect themselves from the endogenous cytotoxic NO produced by converting it to N(2)O, which can be released into the atmosphere. However, N(2)O is a potent greenhouse gas and hence the activity of the enzyme that breaks down N(2)O has a crucial role in restricting its atmospheric levels. Here, we review the current understanding of the process by which N(2)O is produced and destroyed and discuss the potential for feeding this into new approaches for combating N(2)O release.

Uncovering metabolic objectives pursued by changes of enzyme levels

Expression profiling and proteomic techniques reveal significant variations in the levels of thousands of mRNAs and proteins in response to environmental changes such as substrate depletion, oxidative stress, and hormonal stimulation. However, in most cases the functional implications of these variations remain elusive. One crucial problem complicating the functional interpretation of high-throughput data is that changes of protein levels do not simply translate into equivalent changes in the rate of the associated chemical processes due to various modes of enzyme regulation and the instantaneous effect of changed metabolite concentrations on adjacent flux rates. Here, we outline a theoretical concept to exploit information on (relative) changes in the level of metabolic enzymes for the prediction of (relative) flux changes in the underlying metabolic network. Our approach rests on the assumption that size and direction of fluxes (flux distribution) in the network are determined by an optimization principle in that the production of the physiologically relevant output metabolites is accomplished with minimal total flux. The prediction method comprises two main steps. First, we approximate (unknown) flux changes by a linear combination of so-called minimal flux modes, each representing a specific flux distribution minimally required to accomplish the production of only one of the numerous functionally relevant output metabolites. Second, the unknown coefficients of this decomposition are chosen such that a maximal correlation with observed differential expression data is obtained. Based on simulated enzyme expression scenarios in a metabolic model of the human red blood cell, we demonstrate the predictive capacity of our method.

Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein-protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental (13)C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional (13)C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.

Control, responses and modularity of cellular regulatory networks: a control analysis perspective

Cells adapt to changes in environmental conditions through the concerted action of signalling, gene expression and metabolic subsystems. The authors will discuss a theoretical framework addressing such integrated systems. This 'hierarchical analysis' was first developed as an extension to a metabolic control analysis. It builds on the phenomenon that often the communication between signalling, gene expression and metabolic subsystems is almost exclusively via regulatory interactions and not via mass flow interactions. This allows for the treatment of the said subsystems as 'levels' in a hierarchical view of the organisation of the molecular reaction network of cells. Such a hierarchical approach has as a major advantage that levels can be analysed conceptually in isolation of each other (from a local intra-level perspective) and at a later stage integrated via their interactions (from a global inter-level perspective). Hereby, it allows for a modular approach with variable scope. A number of different approaches have been developed for the analysis of hierarchical systems, for example hierarchical control analysis and modular response analysis. The authors, here, review these methods and illustrate the strength of these types of analyses using a core model of a system with gene expression, metabolic and signal transduction levels.

Personalizing nutrigenomics research through community based participatory research and omics technologies

Personal and public health information are often obtained from studies of large population groups. Risk factors for nutrients, toxins, genetic variation, and more recently, nutrient-gene interactions are statistical estimates of the percentage reduction in disease in the population if the risk were to be avoided or the gene variant were not present. Because individuals differ in genetic makeup, lifestyle, and dietary patterns than those individuals in the study population, these risk factors are valuable guidelines, but may not apply to individuals. Intervention studies are likewise limited by small sample sizes, short time frames to assess physiological changes, and variable experimental designs that often preclude comparative or consensus analyses. A fundamental challenge for nutrigenomics will be to develop a means to sort individuals into metabolic groups, and eventually, develop risk factors for individuals. To reach the goal of personalizing medicine and nutrition, new experimental strategies are needed for human study designs. A promising approach for more complete analyses of the interaction of genetic makeups and environment relies on community-based participatory research (CBPR) methodologies. CBPR's central focus is developing a partnership among researchers and individuals in a community that allows for more in depth lifestyle analyses but also translational research that simultaneously helps improve the health of individuals and communities. The USDA-ARS Delta Nutrition Intervention Research program exemplifies CBPR providing a foundation for expanded personalized nutrition and medicine research for communities and individuals.

SulfoSYS (Sulfolobus Systems Biology): towards a silicon cell model for the central carbohydrate metabolism of the archaeon Sulfolobus solfataricus under temperature variation

SulfoSYS (Sulfolobus Systems Biology) focuses on the study of the CCM (central carbohydrate metabolism) of Sulfolobus solfataricus and its regulation under temperature variation at the systems level. In Archaea, carbohydrates are metabolized by modifications of the classical pathways known from Bacteria or Eukarya, e.g. the unusual branched ED (Entner–Doudoroff) pathway, which is utilized for glucose degradation in S. solfataricus. This archaeal model organism of choice is a thermoacidophilic crenarchaeon that optimally grows at 80°C (60–92°C) and pH 2–4. In general, life at high temperature requires very efficient adaptation to temperature changes, which is most difficult to deal with for organisms, and it is unclear how biological networks can withstand and respond to such changes. This integrative project combines genomic, transcriptomic, proteomic and metabolomic, as well as kinetic and biochemical information. The final goal of SulfoSYS is the construction of a silicon cell model for this part of the living cell that will enable computation of the CCM network. In the present paper, we report on one of the first archaeal systems biology projects.

Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network

Significant insight about biological networks arises from the study of network motifs--overly abundant network subgraphs--but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction. Measuring protein abundance over a time course reveals that mRNA timing motifs also occur at the protein level. Timing motifs significantly overlap with binding activity motifs, where genes in a linear chain have ordered binding affinity to a transcription factor, suggesting a mechanism for ordered transcription. Finely timed transcriptional regulation is therefore abundant in yeast metabolism, optimizing the organism's adaptation to new environmental conditions.

Chemostat-based micro-array analysis in baker's yeast

Chemostat cultivation of micro-organisms offers unique opportunities for experimental manipulation of individual environmental parameters at a fixed, controllable specific growth rate. Chemostat cultivation was originally developed as a tool to study quantitative aspects of microbial growth and metabolism. Renewed interest in this cultivation method is stimulated by the availability of high-information-density techniques for systemic analysis of microbial cultures, which require high reproducibility and careful experimental design. Genome-wide analysis of transcript levels with DNA micro-arrays is currently the most commonly applied of these high-information-density analysis tools for microbial gene expression. Based on published studies on the yeast Saccharomyces cerevisiae, a critical overview is presented of the possibilities and pitfalls associated with the combination of chemostat cultivation and transcriptome analysis with DNA micro-arrays. After a brief introduction to chemostat cultivation and micro-array analysis, key aspects of experimental design of chemostat-based micro-array experiments are discussed. The main focus of this review is on key biological concepts that can be accessed by chemostat-based micro-array analysis. These include effects of specific growth rate on transcriptional regulation, context-dependency of transcriptional responses, correlations between transcript profiles and contribution of the corresponding proteins to cellular function and fitness, and the analysis and application of evolutionary adaptation during prolonged chemostat cultivation. It is concluded that, notwithstanding the incompatibility of chemostat cultivation with high-throughput analysis, integration of chemostat cultivation with micro-array analysis and other high-information-density analytical approaches (e.g. proteomics and metabolomics techniques) offers unique advantages in terms of reproducibility and experimental design in comparison with standard batch cultivation systems. Therefore, chemostat cultivation and derived methods for controlled cultivation of micro-organisms are anticipated to become increasingly important in microbial physiology and systems biology.

Network-based prediction of human tissue-specific metabolism

Direct in vivo investigation of mammalian metabolism is complicated by the distinct metabolic functions of different tissues. We present a computational method that successfully describes the tissue specificity of human metabolism on a large scale. By integrating tissue-specific gene- and protein-expression data with an existing comprehensive reconstruction of the global human metabolic network, we predict tissue-specific metabolic activity in ten human tissues. This reveals a central role for post-transcriptional regulation in shaping tissue-specific metabolic activity profiles. The predicted tissue specificity of genes responsible for metabolic diseases and tissue-specific differences in metabolite exchange with biofluids extend markedly beyond tissue-specific differences manifest in enzyme-expression data, and are validated by large-scale mining of tissue-specificity data. Our results establish a computational basis for the genome-wide study of normal and abnormal human metabolism in a tissue-specific manner.

Dynamics of glycolytic regulation during adaptation of Saccharomyces cerevisiae to fermentative metabolism

The ability of baker's yeast (Saccharomyces cerevisiae) to rapidly increase its glycolytic flux upon a switch from respiratory to fermentative sugar metabolism is an important characteristic for many of its multiple industrial applications. An increased glycolytic flux can be achieved by an increase in the glycolytic enzyme capacities (V(max)) and/or by changes in the concentrations of low-molecular-weight substrates, products, and effectors. The goal of the present study was to understand the time-dependent, multilevel regulation of glycolytic enzymes during a switch from fully respiratory conditions to fully fermentative conditions. The switch from glucose-limited aerobic chemostat growth to full anaerobiosis and glucose excess resulted in rapid acceleration of fermentative metabolism. Although the capacities (V(max)) of the glycolytic enzymes did not change until 45 min after the switch, the intracellular levels of several substrates, products, and effectors involved in the regulation of glycolysis did change substantially during the initial 45 min (e.g., there was a buildup of the phosphofructokinase activator fructose-2,6-bisphosphate). This study revealed two distinct phases in the upregulation of glycolysis upon a switch to fermentative conditions: (i) an initial phase, in which regulation occurs completely through changes in metabolite levels; and (ii) a second phase, in which regulation is achieved through a combination of changes in V(max) and metabolite concentrations. This multilevel regulation study qualitatively explains the increase in flux through the glycolytic enzymes upon a switch of S. cerevisiae to fermentative conditions and provides a better understanding of the roles of different regulatory mechanisms that influence the dynamics of yeast glycolysis.

Quantitative analysis of the high temperature-induced glycolytic flux increase in Saccharomyces cerevisiae reveals dominant metabolic regulation

A major challenge in systems biology lies in the integration of processes occurring at different levels, such as transcription, translation, and metabolism, to understand the functioning of a living cell in its environment. We studied the high temperature-induced glycolytic flux increase in Saccharomyces cerevisiae and investigated the regulatory mechanisms underlying this increase. We used glucose-limited chemostat cultures to separate regulatory effects of temperature from effects on growth rate. Growth at increased temperature (38 degrees C versus 30 degrees C) resulted in a strongly increased glycolytic flux, accompanied by a switch from respiration to a partially fermentative metabolism. We observed an increased flux through all enzymes, ranging from 5- to 10-fold. We quantified the contributions of direct temperature effects on enzyme activities, the gene expression cascade and shifts in the metabolic network, to the increased flux through each enzyme. To do this we adapted flux regulation analysis. We show that the direct effect of temperature on enzyme kinetics can be included as a separate term. Together with hierarchical regulation and metabolic regulation, this term explains the total flux change between two steady states. Surprisingly, the effect of the cultivation temperature on enzyme catalytic capacity, both directly through the Arrhenius effect and indirectly through adapted gene expression, is only a moderate contribution to the increased glycolytic flux for most enzymes. The changes in flux are therefore largely caused by changes in the interaction of the enzymes with substrates, products, and effectors.

Co-regulation of metabolic genes is better explained by flux coupling than by network distance

To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools.

Why do certain genes in a biological network show tight transcriptional co-regulation while others are more or less independently regulated? Prior studies showed that the degree of co-regulation between enzymatic genes decreases with their distance in the metabolic network. However, there are fundamental reasons to suspect that network distance is an incomplete descriptor of functional coherence (hence gene co-regulation), and other, biochemically more relevant measures, have been proposed to capture the functional dependencies between enzymes. We systematically examine whether flux coupling, a biochemically sound and computationally tractable measure of functional interaction between reactions, can better explain gene co-regulation than network distance in the metabolisms of Escherichia coli and Saccharomyces cerevisiae. After validating the flux coupling method using published experimental data on in vivo flux correlations (i.e., coherence of reaction usage), we demonstrate that it not only outperforms metabolic network distance in relation to in vivo flux correlations, but also in explaining transcriptional co-regulation and operonic organization. Future functional genomics studies could benefit from the concept of flux coupling by using it as a basis to test the reliability of computationally predicted functional associations.

Impact of systems biology on metabolic engineering ofSaccharomyces cerevisiae

Industrial biotechnology is a rapidly growing field. With the increasing shift towards a bio-based economy, there is rising demand for developing efficient cell factories that can produce fuels, chemicals, pharmaceuticals, materials, nutraceuticals, and even food ingredients. The yeast Saccharomyces cerevisiae is extremely well suited for this objective. As one of the most intensely studied eukaryotic model organisms, a rich density of knowledge detailing its genetics, biochemistry, physiology, and large-scale fermentation performance can be capitalized upon to enable a substantial increase in the industrial application of this yeast. Developments in genomics and high-throughput systems biology tools are enhancing one's ability to rapidly characterize cellular behaviour, which is valuable in the field of metabolic engineering where strain characterization is often the bottleneck in strain development programmes. Here, the impact of systems biology on metabolic engineering is reviewed and perspectives on the role of systems biology in the design of cell factories are given.

Cyclic AMP-dependent catabolite repression is the dominant control mechanism of metabolic fluxes under glucose limitation in Escherichia coli

Although a whole arsenal of mechanisms are potentially involved in metabolic regulation, it is largely uncertain when, under which conditions, and to which extent a particular mechanism actually controls network fluxes and thus cellular physiology. Based on (13)C flux analysis of Escherichia coli mutants, we elucidated the relevance of global transcriptional regulation by ArcA, ArcB, Cra, CreB, CreC, Crp, Cya, Fnr, Hns, Mlc, OmpR, and UspA on aerobic glucose catabolism in glucose-limited chemostat cultures at a growth rate of 0.1 h(-1). The by far most relevant control mechanism was cyclic AMP (cAMP)-dependent catabolite repression as the inducer of the phosphoenolpyruvate (PEP)-glyoxylate cycle and thus low tricarboxylic acid cycle fluxes. While all other mutants and the reference E. coli strain exhibited high glyoxylate shunt and PEP carboxykinase fluxes, and thus high PEP-glyoxylate cycle flux, this cycle was essentially abolished in both the Crp and Cya mutants, which lack the cAMP-cAMP receptor protein complex. Most other mutations were phenotypically silent, and only the Cra and Hns mutants exhibited slightly altered flux distributions through PEP carboxykinase and the tricarboxylic acid cycle, respectively. The Cra effect on PEP carboxykinase was probably the consequence of a specific control mechanism, while the Hns effect appears to be unspecific. For central metabolism, the available data thus suggest that a single transcriptional regulation process exerts the dominant control under a given condition and this control is highly specific for a single pathway or cycle within the network.

Getting to grips with the plant metabolic network

Research into plant metabolism has a long history, and analytical approaches of ever-increasing breadth and sophistication have been brought to bear. We now have access to vast repositories of data concerning enzymology and regulatory features of enzymes, as well as large-scale datasets containing profiling information of transcripts, protein and metabolite levels. Nevertheless, despite this wealth of data, we remain some way off from being able to rationally engineer plant metabolism or even to predict metabolic responses. Within the past 18 months, rapid progress has been made, with several highly informative plant network interrogations being discussed in the literature. In the present review we will appraise the current state of the art regarding plant metabolic network analysis and attempt to outline what the necessary steps are in order to further our understanding of network regulation.

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.

The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels

Metabolic fluxes may be regulated "hierarchically," e.g., by changes of gene expression that adjust enzyme capacities (V(max)) and/or "metabolically" by interactions of enzymes with substrates, products, or allosteric effectors. In the present study, a method is developed to dissect the hierarchical regulation into contributions by transcription, translation, protein degradation, and posttranslational modification. The method was applied to the regulation of fluxes through individual glycolytic enzymes when the yeast Saccharomyces cerevisiae was confronted with the absence of oxygen and the presence of benzoic acid depleting its ATP. Metabolic regulation largely contributed to the approximately 10-fold change in flux through the glycolytic enzymes. This contribution varied from 50 to 80%, depending on the glycolytic step and the cultivation condition tested. Within the 50-20% hierarchical regulation of fluxes, transcription played a minor role, whereas regulation of protein synthesis or degradation was the most important. These also contributed to 75-100% of the regulation of protein levels.

Generic and specific transcriptional responses to different weak organic acids in anaerobic chemostat cultures ofSaccharomyces cerevisiae

Transcriptional responses to four weak organic acids (benzoate, sorbate, acetate and propionate) were investigated in anaerobic, glucose-limited chemostat cultures of Saccharomyces cerevisiae. To enable quantitative comparison of the responses to the acids, their concentrations were chosen such that they caused a 50% decrease of the biomass yield on glucose. The concentration of each acid required to achieve this yield was negatively correlated with membrane affinity. Microarray analysis revealed that each acid caused hundreds of transcripts to change by over twofold relative to reference cultures without added organic acids. However, only 14 genes were consistently upregulated in response to all acids. The moderately lipophilic compounds benzoate and sorbate and, to a lesser extent, the less lipophilic acids acetate and propionate showed overlapping transcriptional responses. Statistical analysis for overrepresented functional categories and upstream regulatory elements indicated that responses to the strongly lipophilic acids were focused on genes related to the cell wall, while acetate and propionate had a stronger impact on membrane-associated transport processes. The fact that S. cerevisiae exhibits a minimal generic transcriptional response to weak organic acids along with extensive specific responses is relevant for interpreting and controlling weak acid toxicity in food products and in industrial fermentation processes.

Mixed and diverse metabolic and gene-expression regulation of the glycolytic and fermentative pathways in response to a HXK2 deletion in Saccharomyces cerevisiae

In Saccharomyces cerevisiae the HXK2 gene, which encodes the glycolytic enzyme hexokinase II, is involved in the regulatory mechanism known as 'glucose repression'. Its deletion leads to fully respiratory growth at high glucose concentrations where the wild type ferments profusely. Here we describe that deletion of the HXK2 gene resulted in a 75% reduction in fermentative capacity. Using regulation analysis we found that the fluxes through most glycolytic and fermentative enzymes were regulated cooperatively by changes in their capacities (Vmax) and by changes in the way they interacted with the rest of the metabolism. Glucose transport and phosphofructokinase were regulated purely at the metabolic level. The reduction of fermentative capacity in the mutant was accompanied by a remarkable resilience of the remaining capacity to nutrient starvation. After starvation, the fermentative capacity of the hxk2Delta mutant was similar to that of the wild type. Based on our results and previous reports, we suggest an inverse correlation between glucose repression and the resilience of fermentative capacity towards nutrient starvation. Only a limited number of glycolytic enzyme activities changed upon starvation of the hxk2Delta mutant and we discuss to what extent this could explain the stability of the fermentative capacity.

Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli

Background

Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.

Results

Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium E. coli to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of E. coli against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.

Conclusion

Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.

A genome-scale computational study of the interplay between transcriptional regulation and metabolism

This paper presents a new method, steady-state regulatory flux balance analysis (SR-FBA), for predicting gene expression and metabolic fluxes in a large-scale integrated metabolic–regulatory model. Using SR-FBA to study the metabolism of Escherichia coli, we quantify the extent to which the different levels of metabolic and transcriptional regulatory constraints determine metabolic behavior: metabolic constraints determine the flux activity state of 45–51% of metabolic genes, depending on the growth media, whereas transcription regulation determines the flux activity state of 13–20% of the genes. A considerable number of 36 genes are redundantly expressed, that is, they are expressed even though the fluxes of their associated reactions are zero, indicating that they are not optimally tuned for cellular flux demands. The undetermined state of the remaining ∼30% of the genes suggests that they may represent metabolic variability within a given growth medium. Overall, SR-FBA enables one to address a host of new questions concerning the interplay between regulation and metabolism.

Introduction and Perspectives

Classically, metabolism was investigated by studying molecular characteristics of enzymes and their regulators in isolation. This reductionistic approach successfully established mechanistic relationships with the immediate interacting neighbors and allowed reconstruction of network structures. Severely underdeveloped was the ability to make precise predictions about the integrated operation of pathways and networks that emerged from the typically nonlinear and complex interactions of proteins and metabolites. The burden of metabolic engineering is a consequence of this fact-one cannot yet predict with any certainty precisely what needs to be engineered to produce more complex phenotypes. What was and still is missing are concepts, methods, and algorithms to integrate data and information into a quantitatively coherent whole, as well as theoretical concepts to reliably predict the consequence of environmental stimuli or genetic interventions. This introduction and perspective to Domain 3, Metabolism and Metabolic Fluxes, starts with a brief overview of the panoply of global measurement technologies that herald the dawning of systems biology and whose impact on metabolic research is apparent throughout the Domain 3. In the middle section, applications to Escherichia coli are used to illustrate general concepts and successes of computational methods that approach metabolism as a network of interacting elements, and thus have potential to fill the gap in quantitative data and information integration. The final section highlights prospective focus areas for future metabolic research, including functional genomics, eludication of evolutionary principles, and the integration of metabolism with regulatory networks.

Growth control of the eukaryote cell: a systems biology study in yeast

BACKGROUND

Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking.

RESULTS

Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth.

CONCLUSION

This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell.

Systems biology: new paradigms for cell biology and drug design

In this chapter various facets of and approaches to systems biology will be discussed. This then leads to an illustration of how systems biology may be used in drug target design. We present five new paradigms for drug target research and show how these are based in systems biology.

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.

Metabolic networks in motion: 13C-based flux analysis

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism.

Time-dependent hierarchical regulation analysis: deciphering cellular adaptation

Cells adapt to changes in their environment by the concerted action of many different regulatory mechanisms. Examples of such mechanisms are feedback inhibition by intermediates of metabolism, covalent modification of enzymes and changes in the abundance of mRNAs and proteins. These mechanisms act in parallel at different levels in the cellular hierarchy while regulating a single process. Existing hierarchical regulation analysis determines the relative importance of these mechanisms when the cell regulates a transition from one steady-state to another. Here, the analysis is extended to the regulation of time-dependent phenomena, for which two methods are introduced and illustrated with a kinetic model incorporating transcription and translation of metabolic enzymes.