Architecture Of Metabolic Networks Research Articles

The Architecture of Metabolic Networks Constrains the Evolution of Microbial Resource Hierarchies.

Microbial strategies for resource use are an essential determinant of their fitness in complex habitats. When facing environments with multiple nutrients, microbes often use them sequentially according to a preference hierarchy, resulting in well-known patterns of diauxic growth. In theory, the evolutionary diversification of metabolic hierarchies could represent a mechanism supporting coexistence and biodiversity by enabling temporal segregation of niches. Despite this ecologically critical role, the extent to which substrate preference hierarchies can evolve and diversify remains largely unexplored. Here, we used genome-scale metabolic modeling to systematically explore the evolution of metabolic hierarchies across a vast space of metabolic network genotypes. We find that only a limited number of metabolic hierarchies can readily evolve, corresponding to the most commonly observed hierarchies in genome-derived models. We further show how the evolution of novel hierarchies is constrained by the architecture of central metabolism, which determines both the propensity to change ranks between pairs of substrates and the effect of specific reactions on hierarchy evolution. Our analysis sheds light on the genetic and mechanistic determinants of microbial metabolic hierarchies, opening new research avenues to understand their evolution, evolvability, and ecology.

Trade-offs predicted by metabolic network structure give rise to evolutionary specialization and phenotypic diversification.

Mitigating trade-offs between different resource-utilization functions is key to an organism’s ecological and evolutionary success. These trade-offs often reflect metabolic constraints with a complex molecular underpinning; therefore, their consequences for evolutionary processes have remained elusive. Here, we investigate how metabolic architecture induces resource-utilization constraints and how these constraints, in turn, elicit evolutionary specialization and diversification. Guided by the metabolic network structure of the bacterium Lactococcus cremoris, we selected two carbon sources (fructose and galactose) with predicted coutilization constraints. By evolving L. cremoris on either fructose, galactose, or a mix of both sugars, we imposed selection favoring divergent metabolic specializations or coutilization of both resources, respectively. Phenotypic characterization revealed the evolution of either fructose or galactose specialists in the single-sugar treatments. In the mixed-sugar regime, we observed adaptive diversification: both specialists coexisted, and no generalist evolved. Divergence from the ancestral phenotype occurred at key pathway junctions in the central carbon metabolism. Fructose specialists evolved mutations in the fbp and pfk genes that appear to balance anabolic and catabolic carbon fluxes. Galactose specialists evolved increased expression of pgmA (the primary metabolic bottleneck of galactose metabolism) and silencing of ptnABCD (the main glucose transporter) and ldh (regulator/enzyme of downstream carbon metabolism). Overall, our study shows how metabolic network architecture and historical contingency serve to predict targets of selection and inform the functional interpretation of evolved mutations. The elucidation of the relationship between molecular constraints and phenotypic trade-offs contributes to an integrative understanding of evolutionary specialization and diversification.

The Macroevolutionary Consequences of Niche Construction in Microbial Metabolism.

Microorganisms display a stunning metabolic diversity. Understanding the origin of this diversity requires understanding how macroevolutionary processes such as innovation and diversification play out in the microbial world. Metabolic networks, which govern microbial resource use, can evolve through different mechanisms, e.g., horizontal gene transfer or de novo evolution of enzymes and pathways. This process is governed by a combination of environmental factors, selective pressures, and the constraints imposed by the genetic architecture of metabolic networks. In addition, many independent results hint that the process of niche construction, by which organisms actively modify their own and each other’s niches and selective pressures, could play a major role in microbial innovation and diversification. Yet, the general principles by which niche construction shapes microbial macroevolutionary patterns remain largely unexplored. Here, we discuss several new hypotheses and directions, and suggest metabolic modeling methods that could allow us to explore large-scale empirical genotype-phenotype-(G-P)-environment spaces in order to study the macroevolutionary effects of niche construction. We hope that this short piece will further stimulate a systematic and quantitative characterization of macroevolutionary patterns and processes in microbial metabolism.

Functional characterization of the HMP-P synthase of Legionella pneumophila (Lpg1565).

The production of the pyrimidine moiety in thiamine synthesis, 2-methyl-4-amino-5-hydroxymethylpyrimidine phosphate (HMP-P), has been described to proceed through the Thi5-dependent pathway in Saccharomyces cerevisiae and other yeast. Previous work found that ScThi5 functioned poorly in a heterologous context. Here we report a bacterial ortholog to the yeast HMP-P synthase (Thi5) was necessary for HMP synthesis in Legionella pneumophila. Unlike ScThi5, LpThi5 functioned in vivo in Salmonella enterica under multiple growth conditions. The protein LpThi5 is a dimer that binds pyridoxal-5'-phosphate (PLP), apparently without a solvent-exposed Schiff base. A small percentage of LpThi5 protein co-purifies with a bound molecule that can be converted to HMP. Analysis of variant proteins both in vivo and in vitro confirmed that residues in sequence motifs conserved across bacterial and eukaryotic orthologs modulate the function of LpThi5. IMPORTANCE: Thiamine is an essential vitamin for the vast majority of organisms. There are multiple strategies to synthesize and salvage this vitamin. The predominant pathway for synthesis of the pyrimidine moiety of thiamine involves the Fe-S cluster protein ThiC. An alternative pathway utilizes Thi5, a novel enzyme that uses PLP as a substrate. The Thi5-dependent pathway is poorly characterized in yeast and has not been characterized in Bacteria. Here we demonstrate that a Thi5-dependent pathway is necessary for thiamine biosynthesis in Legionella pneumophila and provide biochemical data to extend knowledge of the Thi5 enzyme, the corresponding biosynthetic pathway, and the role of metabolic network architecture in optimizing its function.

Common biochemical properties of metabolic genes recurrently dysregulated in tumors

BackgroundTumor initiation and progression are associated with numerous metabolic alterations. However, the biochemical drivers and constraints that contribute to metabolic gene dysregulation are unclear.MethodsHere, we present MetOncoFit, a computational model that integrates 142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topology, and reaction flux. MetOncoFit uses genome-scale metabolic modeling and machine-learning to quantify the relative importance of various metabolic features in predicting cancer metabolic gene expression, copy number variation, and survival data.ResultsUsing MetOncoFit, we performed a meta-analysis of 9 cancer types and over 4500 samples from TCGA, Prognoscan, and COSMIC tumor databases. MetOncoFit accurately predicted enzyme differential expression and its impact on patient survival using the 142 attributes of metabolic enzymes. Our analysis revealed that enzymes with high catalytic activity were frequently upregulated in many tumors and associated with poor survival. Topological analysis also identified specific metabolites that were hot spots of dysregulation.ConclusionsMetOncoFit integrates a broad range of datasets to understand how biochemical and topological features influence metabolic gene dysregulation across various cancer types. MetOncoFit was able to achieve significantly higher accuracy in predicting differential expression, copy number variation, and patient survival than traditional modeling approaches. Overall, MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.

Environmental boundary conditions for the origin of life converge to an organo-sulfur metabolism.

It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals prior to genetically encoded enzymes. A major challenge in unraveling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms with physico-chemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fueled by a thioester- and redox-driven variant of the reductive TCA cycle, capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors seem not to be necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a proto-cell, and find that different combinations of carbon sources and electron donors can support the continuous production of a minimal ancient “biomass” composed of putative early biopolymers and fatty acids.

MoVE identifies metabolic valves to switch between phenotypic states

Metabolism is highly regulated, allowing for robust and complex behavior. This behavior can often be achieved by controlling a small number of important metabolic reactions, or metabolic valves. Here, we present a method to identify the location of such valves: the metabolic valve enumerator (MoVE). MoVE uses a metabolic model to identify genetic intervention strategies which decouple two desired phenotypes. We apply this method to identify valves which can decouple growth and production to systematically improve the rate and yield of biochemical production processes. We apply this algorithm to the production of diverse compounds and obtained solutions for over 70% of our targets, identifying a small number of highly represented valves to achieve near maximal growth and production. MoVE offers a systematic approach to identify metabolic valves using metabolic models, providing insight into the architecture of metabolic networks and accelerating the widespread implementation of dynamic flux redirection in diverse systems.

Metabolomics of mammalian brain reveals regional differences

BackgroundThe mammalian brain is organized into regions with specific biological functions and properties. These regions have distinct transcriptomes, but little is known whether they may also differ in their metabolome. The metabolome, a collection of small molecules or metabolites, is at the intersection of the genetic background of a given cell or tissue and the environmental influences that affect it. Thus, the metabolome directly reflects information about the physiologic state of a biological system under a particular condition. The objective of this study was to investigate whether various brain regions have diverse metabolome profiles, similarly to their genetic diversity. The answer to this question would suggest that not only the genome but also the metabolome may contribute to the functional diversity of brain regions.MethodsWe investigated the metabolome of four regions of the mouse brain that have very distinct functions: frontal cortex, hippocampus, cerebellum, and olfactory bulb. We utilized gas- and liquid- chromatography mass spectrometry platforms and identified 215 metabolites.ResultsPrincipal component analysis, an unsupervised multivariate analysis, clustered each brain region based on its metabolome content, thus providing the unique metabolic profile of each region. A pathway-centric analysis indicated that olfactory bulb and cerebellum had most distinct metabolic profiles, while the cortical parenchyma and hippocampus were more similar in their metabolome content. Among the notable differences were distinct oxidative-anti-oxidative status and region-specific lipid profiles. Finally, a global metabolic connectivity analysis using the weighted correlation network analysis identified five hub metabolites that organized a unique metabolic network architecture within each examined brain region. These data indicate the diversity of global metabolome corresponding to specialized regional brain function and provide a new perspective on the underlying properties of brain regions.ConclusionIn summary, we observed many differences in the metabolome among the various brain regions investigated. All four brain regions in our study had a unique metabolic signature, but the metabolites came from all categories and were not pathway-centric.

Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss

BackgroundMetabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear.ResultsTo address these questions, we developed a novel framework, coupling a reductive evolution model with a multi-species genome-scale metabolic model to simulate the evolution of two-species microbial communities. Simulating thousands of independent evolutionary trajectories, we surprisingly found that under certain environmental and evolutionary settings metabolic dependencies emerged frequently even though our model does not include explicit selection for cooperation. Evolved dependencies involved cross-feeding of a diverse set of metabolites, reflecting constraints imposed by metabolic network architecture. We additionally found metabolic ‘missed opportunities’, wherein species failed to capitalize on metabolites made available by their partners. Examining the genes deleted in each evolutionary trajectory and the deletion timing further revealed both genome-wide properties and specific metabolic mechanisms associated with species interaction.ConclusionOur findings provide insight into the evolution of cooperative interaction among microbial species and a unique view into the way such relationships emerge.

Depression comorbidity in epileptic rats is related to brain glucose hypometabolism and hypersynchronicity in the metabolic network architecture.

Temporal lobe epilepsy (TLE) is one of the most common types of epilepsy syndromes in the world. Depression is an important comorbidity of epilepsy, which has been reported in patients with TLE and in different experimental models of epilepsy. However, there is no established consensus on which brain regions are associated with the manifestation of depression in epilepsy. Here, we investigated the alterations in cerebral glucose metabolism and the metabolic network in the pilocarpine-induced rat model of epilepsy and correlated it with depressive behavior during the chronic phase of epilepsy. Fluorodeoxyglucose (18 F-FDG) was used to investigate the cerebral metabolism, and a cross-correlation matrix was used to examine the metabolic network in chronically epileptic rats using micro-positron emission tomography (microPET) imaging. An experimental model of epilepsy was induced by pilocarpine injection (320 mg/kg, ip). Forced swim test (FST), sucrose preference test (SPT), and eating-related depression test (ERDT) were used to evaluate depression-like behavior. Our results show an association between epilepsy and depression comorbidity based on changes in both cerebral glucose metabolism and the functional metabolic network. In addition, we have identified a significant correlation between brain glucose hypometabolism and depressive-like behavior in chronically epileptic rats. Furthermore, we found that the epileptic depressed group presents a hypersynchronous brain metabolic network in relation to the epileptic nondepressed group. This study revealed relevant alterations in glucose metabolism and the metabolic network among the brain regions of interest for both epilepsy and depression pathologies. Thus it seems that depression in epileptic animals is associated with a more diffuse hypometabolism and altered metabolic network architecture and plays an important role in chronic epilepsy.

Metabolic network architecture and carbon source determine metabolite production costs.

Metabolism is essential to organismal life, because it provides energy and building block metabolites. Even though it is known that the biosynthesis of metabolites consumes a significant proportion of the resources available to a cell, the factors that determine their production costs remain less well understood. In this context, it is especially unclear how the nutritional environment affects the costs of metabolite production. Here, we use the amino acid metabolism of Escherichia coli as a model to show that the point at which a carbon source enters central metabolic pathways is a major determinant of individual metabolite production costs. Growth rates of auxotrophic genotypes, which in the presence of the required amino acid save biosynthetic costs, were compared to the growth rates that prototrophic cells achieved under the same conditions. The experimental results showed a strong concordance with computationally estimated biosynthetic costs, which allowed us, for the first time, to systematically quantify carbon source-dependent metabolite production costs. Thus, we demonstrate that the nutritional environment in combination with network architecture is an important but hitherto underestimated factor influencing biosynthetic costs and thus microbial growth. Our observations are highly relevant for the optimization of biotechnological processes as well as for understanding the ecology of microorganisms in their natural environments.

MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.

Redox balance is key to explaining full vs. partial switching to low-yield metabolism

BackgroundLow-yield metabolism is a puzzling phenomenon in many unicellular and multicellular organisms. In abundance of glucose, many cells use a highly wasteful fermentation pathway despite the availability of a high-yield pathway, producing many ATP molecules per glucose, e.g., oxidative phosphorylation. Some of these organisms, including the lactic acid bacterium Lactococcus lactis, downregulate their high-yield pathway in favor of the low-yield pathway. Other organisms, including Escherichia coli do not reduce the flux through the high-yield pathway, employing the low-yield pathway in parallel with a fully active high-yield pathway. For what reasons do some species use the high-yield and low-yield pathways concurrently and what makes others downregulate the high-yield pathway? A classic rationale for metabolic fermentation is overflow metabolism. Because the throughput of metabolic pathways is limited, influx of glucose exceeding the pathway's throughput capacity is thought to be redirected into an alternative, low-yield pathway. This overflow metabolism rationale suggests that cells would only use fermentation once the high-yield pathway runs at maximum rate, but it cannot explain why cells would decrease the flux through the high-yield pathway.ResultsUsing flux balance analysis with molecular crowding (FBAwMC), a recent extension to flux balance analysis (FBA) that assumes that the total flux through the metabolic network is limited, we investigate the differences between Saccharomyces cerevisiae and L. lactis that downregulate the high-yield pathway at increasing glucose concentrations, and E. coli, which keeps the high-yield pathway functioning at maximal rate. FBAwMC correctly predicts the metabolic switching mode in these three organisms, suggesting that metabolic network architecture is responsible for differences in metabolic switching mode. Based on our analysis, we expect gradual, "overflow-like" switching behavior in organisms that have an additional energy-yielding pathway that does not consume NADH (e.g., acetate production in E. coli). Flux decrease through the high-yield pathway is expected in organisms in which the high-yield and low-yield pathways compete for NADH. In support of this analysis, a simplified model of metabolic switching suggests that the extra energy generated during acetate production produces an additional optimal growth mode that smoothens the metabolic switch in E. coli.ConclusionsMaintaining redox balance is key to explaining why some microbes decrease the flux through the high-yield pathway, while other microbes use "overflow-like" low-yield metabolism.

Automated Reconstruction of Metabolic Pathways of Homo Sapiens involved in the Functioning of GAD1 and GAD2 Genes based on Structural Grammars

Modeling and analyzing the architecture of metabolic networks using various computational strategies can be successfully used for studying their internal metabolic dynamics as well as predicting missing links in diseased networks. In the present work, we have implemented our algorithm based on structural grammars, for automated metabolic pathway reconstruction and modeling in metabolic pathways responsible for coding genes responsible for the cause of Type 1 Diabetes mellitus (T1D) in Homo sapiens. We have especially implemented our algorithm for studying the metabolic pairs responsible for the functioning of GAD1 and GAD2 genes. We have also used the algorithm for automated reconstruction of glutamate metabolism, β-alanine metabolism, taurine & hypotaurine metabolism and butanoate metabolism pathway datasets. We have also used the algorithm for missing and multiple link prediction as well as nodal point analysis for all the four metabolic pathways with 90.4-100% accuracy.

Signatures of Arithmetic Simplicity in Metabolic Network Architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.

Evolutionary Constraint and Adaptation in the Metabolic Network of Drosophila

Organisms must carefully control their metabolism in order to survive. On the other hand, enzymes must adapt in response to evolutionary pressures on the pathways in which they are imbedded. Taking advantage of the newly available whole-genome sequences of 12 Drosophila species, we examined how protein function and metabolic network architecture influence rates of enzyme evolution. We found that despite high overall constraint, there were significant differences in rates of amino acid substitution among functional classes of enzymes. This heterogeneity arises because proteins involved in the metabolism of foreign compounds evolve relatively rapidly, whereas enzymes that act in "core" metabolism exhibit much slower rates of amino acid replacement, suggesting strong selective constraint. Network architecture also influences enzymes' rates of amino acid replacement. In particular, enzymes that share metabolites with many other enzymes are relatively constrained, although apparently not because they are more likely to be essential. Our analyses suggest that this pattern is driven by strong constraint of enzymes acting at branch points in metabolic pathways. We conclude that metabolic network architecture and enzyme function separately affect enzyme evolution rates.

Modular co-evolution of metabolic networks.

BackgroundThe architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.ResultsIn this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do.ConclusionThe correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.

OXYGEN'S GIFT

A GRAND PLOT POINT IN THE history of life was the center-stage entry of oxygen into the atmosphere roughly 2.2 billion years ago. At the time, oxygen was a waste gas that emanated from the bacterial innovators of photosynthesis and posed mortal hazards to the anaerobic life that had been evolving for more than a billion years. With oxygen's arrival, the choices for the planet's microbial masses were to die, hide from oxygen, or evolve ways to live with it. Using a bioinformatics approach involving data from the genomes of 70 of today's aerobic and anaerobic microbes, as well as thousands of enzymatic reactions, Jason Raymond of Lawrence Livermore National Laboratory and Daniel Segre of both LLNL and Boston University have attempted to infer oxygen availability changed the architecture of metabolic networks ( Science 2006 , 311 , 1764). It's the type of insight that researchers can use to reconstruct how early microbial life evolved into complex ...

The Effect of Oxygen on Biochemical Networks and the Evolution of Complex Life

The evolution of oxygenic photosynthesis and ensuing oxygenation of Earth's atmosphere represent a major transition in the history of life. Although many organisms retreated to anoxic environments, others evolved to use oxygen as a high-potential redox couple while concomitantly mitigating its toxicity. To understand the changes in biochemistry and enzymology that accompanied adaptation to O2, we integrated network analysis with information on enzyme evolution to infer how oxygen availability changed the architecture of metabolic networks. Our analysis revealed the existence of four discrete groups of networks of increasing complexity, with transitions between groups being contingent on the presence of key metabolites, including molecular oxygen, which was required for transition into the largest networks.

Uncovering transcriptional regulation of metabolism by using metabolic network topology.

Cellular response to genetic and environmental perturbations is often reflected and/or mediated through changes in the metabolism, because the latter plays a key role in providing Gibbs free energy and precursors for biosynthesis. Such metabolic changes are often exerted through transcriptional changes induced by complex regulatory mechanisms coordinating the activity of different metabolic pathways. It is difficult to map such global transcriptional responses by using traditional methods, because many genes in the metabolic network have relatively small changes at their transcription level. We therefore developed an algorithm that is based on hypothesis-driven data analysis to uncover the transcriptional regulatory architecture of metabolic networks. By using information on the metabolic network topology from genome-scale metabolic reconstruction, we show that it is possible to reveal patterns in the metabolic network that follow a common transcriptional response. Thus, the algorithm enables identification of so-called reporter metabolites (metabolites around which the most significant transcriptional changes occur) and a set of connected genes with significant and coordinated response to genetic or environmental perturbations. We find that cells respond to perturbations by changing the expression pattern of several genes involved in the specific part(s) of the metabolism in which a perturbation is introduced. These changes then are propagated through the metabolic network because of the highly connected nature of metabolism.