Dysfunction in energy metabolism—including in pathways localized to the mitochondria—has been implicated in the pathogenesis of a wide array of disorders, ranging from cancer to neurodegenerative diseases to type II diabetes. The inherent complexities of energy and mitochondrial metabolism present a significant obstacle in the effort to understand the role that these molecular processes play in the development of disease. To help unravel these complexities, systems biology methods have been applied to develop an array of computational metabolic models, ranging from mitochondria-specific processes to genome-scale cellular networks. These constraint-based (CB) models can efficiently simulate aspects of normal and aberrant metabolism in various genetic and environmental conditions. Development of these models leverages—and also provides a powerful means to integrate and interpret—information from a wide range of sources including genomics, proteomics, metabolomics, and enzyme kinetics. Here, we review a variety of mechanistic modeling studies that explore metabolic functions, deficiency disorders, and aberrant biochemical pathways in mitochondria and related regions in the cell. Energy-producing metabolic pathways—including those primarily active in mitochondria—are among the most central components of eukaryotic life. As such, dysfunction in these pathways has been implicated in numerous human diseases, including diabetes, cancer, epilepsy, myopathy, cognitive impairment, and neurodegenerative disorders [reviewed in DiMauro and Schon ]. AdvancMitochondria are maternally-inherited, dynamic organelles present in all cells of the body, excluding the red blood cells. They vary in number from a few hundred to thousands per cell, depending upon the cell type and its functions. Mitochondria play a vital role in cell life, death, and differentiation. They are centers of adenosine triphosphate (ATP) production and a multitude of other housekeeping functions such as biosynthesis of amino acids, oxidation of fatty acids, and apoptosis. The mitochondrion has been appropriately called the “powerhouse” of the cell, and this function has been the focus of intensive research.number of mitochondria per cell as well as in mitochondrial gene and protein expression. For example, the cells in the heart have three times more mitochondria as compared to cells in the liver. It was further observed that while different tissues display variation in mitochondrial gene expression, developmentally related organs exhibit higher similarity in their mitochondrial transcriptome and proteome  is a 16,569 base pair long circular molecule , for instance, has been implicated in various respiratory chain diseases, ranging from neonatal lethality to adult-onset of neurodegeneration . To date, the database includes 502 hierarchical clinical features with defects in 174 mitochondria-resident proteins. In addition to MitoPhenome, other mitochondrial databases such as MitoP2  modeling” in the systems biology community to denote stoichiometric modeling, we will henceforth consider this former term distinct from more detailed kinetic modeling. In contrast to kinetic modeling, CB modeling requires relatively few parameters and defines a set of non-excluded states that is consistent with the available data and applied physico-chemical constraints. As such, these models can be highly suitable to integrating and interrogating high-throughput datasets for a biological investigation of interest. Procedures for the construction and analysis of CB models are well established in microbial species  are represented by negative coefficients, whereas positive coefficients indicate production of metabolites; all metabolites that do not participate in a reaction are assigned a zero coefficient, resulting in a sparse matrix. S provides the relationship between reaction activity and metabolite concentration change as Sv = dx/dt, where the vector v represents the flux through each reaction, and concentrations of all metabolites are represented by vector x. In this way, the numbers in S impose stoichiometric constraints on reaction fluxes, where the net change of each metabolite is a direct function of the reactions in which it participates. Often, the system is assumed to be in a net mass-balanced quasi-steady state that is represented by Sv = 0. Lower and upper bounds (vmin ≤ v ≤ vmax) are implemented to enforce directionality and capacity constraints for each reaction.CB modeling is a mathematical representation of the chemical reaction rates  through metabolic reactions in a living system. The stoichiometry of the biochemical network is depicted in the form of a stoichiometric matrix Sv = 0. By optimizing a relevant objective function, thereby driving network flux toward the production or consumption of a defined set of metabolites, a single distribution v that satisfies the objective can be found. Often, an objective function is chosen to replicate the assumed in vivo biological selection pressure, whether this is maximum ATP production, maximum biomass production or other. In practice, the objective function is specified by vector c, with weights representing the relative contribution of each reaction. Linear programming can then be used to identify an optimal flux distribution that satisfies all constraints. Notably, there are typically many equivalent optimal solutions, and so the internal state of the network found to be optimal is usually not unique . This approach can be summarized mathematically as follows:in vivo relevant flux distributions. Another advantage is that FBA can be used to perform simulations under varied conditions simply by altering the constraints such as substrate availability or ATP production.In mathematical terms, the constraints define a multi-dimensional solution space of flux distributions for a specified condition that are consistent with the available data. While additional constraints such as metabolite concentrations and localization can further shrink the solution space, the system typically remains under-determined, with many alternative flux distribution solutions that satisfy S matrix represents the actual biochemical network of the living system. Therefore, construction of S for a specific organism is an important, data-intensive, and often arduous procedure. The process starts with collecting species-specific data from various sources such as genome annotations, high-throughput experiments, and relevant literature of the biochemistry and physiology of the organism. This leads to a draft network that must then be gap-filled  such as knockout lethality, growth rates on different substrates, and/or byproduct section rates can be compared to experiment, and the model can thus be iteratively tested and improved. Context-specific information is then imposed on the network to either simulate different conditions in silico, or—for higher organisms—to establish a representative model for some subset of the global species network , and FBA is performed to simulate mitochondrial function.2 uptake rates were then simulated for three common substrates: glucose, lactate, and palmitic acid. Their results showed that the model could uptake glucose to produce 38 moles of ATP with the concomitant utilization of 6 moles of O2; lactate uptake corresponded to 17.5 moles of ATP with the utilization of 3.0 moles of O2, and palmitic acid uptake (a 14-C fatty acid) led to 129 moles of ATP, but required 23 moles of O2, which is in agreement with experimental values.In 2002, Ramakrishna and Palsson characterized the optimal flux distributions for maximal ATP production in a mitochondria-specific metabolic network, which included glycolytic pathways, the citric or TCA cycle, and the electron transport system (ETS). ATP production and the concomitant OExtending the analysis further, the authors simulated changes in ATP production as a result of mutations in genes encoding TCA cycle enzymes . Mutations in TCA enzymes such as α-ketoglutarate dehydrogenase, fumarase, and succinate dehydrogenase have been implicated in mitochondrial diseases  detoxification. Each reaction and metabolite was assigned to its respective compartment such as mitochondria, extracellular space, or cytosol. In addition to the localization data, a confidence score was assigned to each reaction based on the data supporting it. For example, the highest confidence of 4 was assigned to a reaction supported by biochemical data, and the lowest confidence (0) to reactions with This model was used to simulate ATP production and calculate yield from three energy sources: glucose, fatty acids, and amino acids. The model produced 31.5 ATPs for each mole of glucose. For calculating ATP yield from fatty acids, the authors focused only on palmitate and calculated that each mole of palmitate led to production of 106 ATPs, which is different from the value of 129 reported in the literature  for various reactions such as lactate exchange, ATP demand, oxygen exchange, and others. The sampling technique produced a range of allowable metabolic network states that represented possible steady-state flux distributions of the network for all four states, defined using constraints from experimentally measured conditions. Results of this analysis showed that there is reduced network flexibility in diabetes, and changes in oxygen levels or ATP can greatly affect mitochondrial metabolism.One of the unique features of this study was that the authors modeled therapeutic approaches for ischemia and predicted the effectiveness of these approaches based on network simulations. Predictions agreed remarkably well with the observed side effects of these therapies; for example, one of the therapeutic approaches suggested previously for ischemia was to increase glucose uptake Apstein,  in the hperturbation from what seems to be an appropriate response of the system to compensate for the perturbation.Additionally, the model predicted that the oxygen requirements for the cardiac myocyte were much more constricted to be near maximal under typical diabetic conditions. This finding provided a possible mechanistic explanation for the clinical observation that ischemic events, which limit oxygen, are more likely to be fatal for patients with diabetes than for unaffected people  was reconstructed  is completely shut down; the TCA cycle thus ends at the step where fumarate would normally be converted to malate by fumarase, leading to accumulation of the former. The TCA network structure with this disorder precludes ATP production in the heart, however, patients with this disorder do not present with heart defects. To investigate this discrepancy, authors simulated elevated metabolism by removing the boundary constraints on all metabolites. Through this process, they identified that increased concentration of certain metabolites, such as glutamate, aspartate, or valine, lead to increased ATP production in the TCA cycle. This result agrees with findings in the literature . The study revealed that although LS-affected cells have slower metabolism as compared to normal cells, their ATP production rate was similar to that of the normal cells. Additionally, LS-affected cells exhibited a narrower flux range for the reactions involved in ATP production. This work implicated mutations in succinate cytochrome c reductase and defective respiratory complex II as the most likely candidates for inducing LS in the cell line, ruling out malfunction of ATP synthase, pyruvate dehydrogenase complex, or respiratory complexes I, III, and IV as likely causes of the disease. Another impressive aspect of this study was that the authors suggested an effective vitamin treatment for LS patients. Predictions of the model were confirmed in vitro with experimental measurements in both cell lines. This study is a prime example of how the inclusion of a multitude of experimental data can enhance the accuracy of in silico models and can suggest an effective treatment regime for a complicated disease such as LS.In 2007, Vo et al. published a model for LS and suggested a framework for employing CB modeling to recommend a treatment strategy for this disease. The authors modeled the status of mitochondrial processes in LS by constructing a model for LS-affected fibroblasts and normal fibroblasts, utilizing experimental measurements such as substrate uptake, metabolite secretion rates, and isotopomer data. The authors experimentally grew normal and LS cells in media with Mitochondria-centric network models have been reconstructed now for three different subtypes of neurons  of heterogeneous mitochondrial populations in the cell, in order to analyze effects on energy production from dynamic changes in mitochondria in cells. Changes in mitochondrial population, as well as errors in their turnover in the cell, have been implicated in several diseases (Schon and Przedborski, Focus areas mentioned above either singularly or cumulatively have the potential to reveal the intricacies of energy metabolism in mitochondria and in relation to emergent physiology and disease. The goal of CB modeling is to accurately and comprehensively simulate facets of energy metabolism and its effect on disease. Models with high predictive accuracy require accurate and precise context-specific biological measurements. As technology and experimental procedures continue to improve, providing more data and at higher resolution, CB modeling will present an important avenue to contribute toward the understanding and the treatment of energy and mitochondrial metabolism-based diseases.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Escherichia coli, Bacillus subtilis, Corynobacterium glutamicum, Streptomyces spp., Lactococcus lactis and other lactic acid bacteria. All of them are established producers of important classes of products, e.g. proteins, amino acids, organic acids, antibiotics, high-value metabolites for the food industry and also, promising producers of a large number of industrially or therapeutically important chemicals. Optimization of existing or introduction of new cellular processes in these microorganisms is often achieved through manipulation of targets that reside at major points of central metabolic pathways, such as glycolysis, gluconeogenesis, the pentose phosphate pathway and the tricarboxylic acid cycle with the glyoxylate shunt. Based on the huge progress made in recent years in biochemical, genetic and regulatory studies, new fascinating engineering approaches aim at ensuring an optimal carbon and energy flow within central metabolism in order to achieve optimized metabolite production.This paper gives an overview of the recent advances in engineering the central carbon metabolism of the industrially important bacteria  Metabolic engineering is a field that encompasses detailed metabolic analysis aiming at identifying targets for manipulation through recombinant DNA technology for the improvement and/or design of cells . MetabolEscherichia coli is by far the most extensively studied bacterium in this field. Most recently acquired knowledge provides fundamental insights into the regulation of fluxes in CCM through transcriptional control -labelled substrates. The results revealed that both carboxylation and decarboxylation occur simultaneously in C. glutamicum and a high cyclic flux of oxaloacete via PEP to pyruvate was found [C. glutamicum has been discussed extensively in the review by Sauer and Eikmanns [C. glutamicum. Overexpression of pyc resulted in seven-fold increase of glutamate production as well as increased lysine accumulation, while abolition of pyruvate carboxylase activity resulted in significantly lower amino acid formation [pyc in lysine production [pyc increased growth but decreased the specific productivity of lysine. Simultaneous expression of aspartate kinase abolished the deficiency and yielded more than 250 % increase of the lysine specific productivity without affecting the growth rate or final cell density of the culture [pck) reaction. In contrast to the situation found with pyruvate carboxylase [For anaplerotic replenishment of the TCA cycle,  enzymes . In addias found . The PEPEikmanns . Since aormation . “Genomeoduction . Howeveroduction  showed t culture . Anotherboxylase , abolitiboxylase .Streptomyces have been extensively isolated since 1940 and today the chance of discovering new antibiotics from them is increasing.The fame of streptomycetes as efficient producers of antibiotics started with the discovery of actinomycin in 1940, followed by streptomycin in 1943 . Today, Streptomyces. During the last years, genetic manipulation of biosynthetic pathways offered new tools and perspectives. The Embden-Meyerhof-Parnas (EMP) pathway, the PP pathway and the TCA cycle are present in a number of Streptomyces species [Secondary metabolic pathways have been until recently the obvious choice for investigations of strain manipulation and yield improvement in  species  but unti species . Moreove species .S. coelicolor, for various nutrient limitations [S. lividans grown on glucose or gluconate showed increased carbon flux through glycolysis and the PP pathway while the synthesis of both ACT and undecylprodigiosin (RED) was inverse to the flux through the PP pathway [zwf1 and zwf2 coding for isoenzymes of the initial enzyme of the PP pathway G6PDH, resulted in more efficient utilization of glucose via glycolysis and increased acetyl-CoA precursor pools that led to four-fold increased production of both ACT and RED by S. lividans[Optimized metabolic flux distributions were calculated for the production of actinorhodin (ACT)  and the itations . Performitations  found th pathway . Deletio lividans.S. coelicolor. Genes encoding G6PDH (either zwf1 or zwf2) and phosphoglucomutase (pgm) were deleted and those for the acetyl-CoA carboxylase (ACCase) were overexressed. G6PDH encoded by zwf2 was found to play a more important role than the encoded by zwf1 in determining the carbon flux to ACT. The pgm-deleted mutant produced abutant glycogen but was impaired in ACT production. Finally, overexpression of ACCase resulted in more rapid utilization of glucose and increased efficiency of its conversion to ACT. It was concluded therefore that the carbon storage metabolism plays a significant role in precursor supply for ACT production and that manipulation of the CCM can lead to increased ACT production by S. coelicolor.Ryu and co-workers  investigS. clavuligerus was that by Li and Townsend [l-arginine and d-glyceraldehyde-3-phosphate (G3P). Two genes (gap1 and gap2) whose protein products are distinct glyceraldehyde-3-phosphate dehydrogenases (GAPDHs) were inactivated in S. clavuligerus by targeted gene disruption. A two-fold increase in production of clavulanic acid was obtained when gap1 was disrupted, and reversed by complementation. Addition of arginine to the cultured mutant further improved clavulanic acid production giving a greater than two-fold increase over wild type, suggesting that arginine became limiting for biosynthesis.The first reported application of genetic engineering to channel precursor flux to improve clavulanic acid production by Townsend . ClavulaApart from their use as starter cultures in the industrial manufacture of fermented food products, lactic acid bacteria (LAB) are also used in a variety of industrial applications, e.g. production of lactic acid, antimicrobial peptides, stereoisomers of lactic acid, high-value metabolites involved in flavor, texture or health applications and probiotic products. Their industrial importance along with some characteristics, such as their small genome size (~2-3 Mb) and simple energy and carbon metabolism, make them promising targets of metabolic engineering strategies. Such strategies have mainly focused either on rerouting of pyruvate metabolism to produce important fermentation end products ,96 or onLactococcus lactis is by far the most extensively studied organism. The relative simplicity of L. lactis metabolism that converts sugars to pyruvate via the glycolytic pathway  resulted in a proportional increase of the maximum specific rates of glucose uptake (from 0.8 to 1.7 μMs-1 g CDW-1) and lactate formation (from 15 to 22.8 g lactate (g CDW)-1 h-1).nditions ,103 or dnditions ,105. Regnditions -110. Resnditions  concernis et al.  showed ts et al. ,113 consL. lactis is also affected by the carbon catabolite protein (CcpA) which besides its role in catabolite repression also regulates sugar metabolism through activation of the las operon . Inactivation of the lactococcal ccpA gene resulted in a strongly reduced expression level of the las operon genes that resulted in a shift from homolactic to mixed acid fermentation [las operon genes has also been observed in Lactobacillus plantarum and Streptococcus thermophilus[It has been shown by Luesink et al.  that theentation . The unirmophilus.lac operon) are plasmid encoded and the lactose mini-plasmid pMG820 is the best described so far [lacEF), lactose-6P hydrolysis (lacG) and the tagatose-6P pathway (lacABCD)  and the mannose-PTS system involved in glucose import. Growth of that strain on lactose involved the conversion of the galactose-6P moiety via the tagatose-6P pathway while the glucose moiety of the disaccharide remained unphosphorylated and secreted. The lack of the mannose-PTS in that strain prevented the subsequent utilization of the secreted glucose. Although the exact mechanism of the engineering is unclear because of the application of random mutagenesis, this early work by Thompson et al. [L. lactis can be completely separated.Because of its presence in milk and milk-based industrial media, lactose metabolism has been given considerable attention. In many lactococcal strains, the genes involved in lactose metabolism (d so far . The pla) Figure . In 1985n et al.  shows hopgmU gene). The intermediate product glucose-1P serves as building block for polymers and it is converted to UDP-glucose by a phosphorylase . Metabolic engineering strategies aiming at overproduction of UDP-glucose have focused on overexpression of the pathway involved genes galU and pgmU[L. lactis grown on glucose [Engineering of carbon distribution between glycolysis and sugar nucleotide biosynthesis is of special importance for the production of UDP-glucose and other activated sugars that serve as precursors for exopolysaccharide (EPS) biosynthesis. EPS from LAB have been given considerable attention recently as they contribute to the development of texture in fermented products; they provide health benefits to consumers (prebiotics) and also protect their producer organisms against bacteriophages and various environmental stresses . UDP-glu and pgmU,118. How glucose  but not  glucose , the latStreptomycetes and lactic acid bacteria. However, there is still lack of knowledge regarding the prediction of responses of these bacteria to various changes and different conditions. The goal remains to have a global overview of the metabolism of producer strains and accordingly to proceed in the construction of robust strains capable of efficiently producing the desired metabolites. In this view, it is expected that developing System Biology approaches that take into account the gathered information on central carbon metabolism regulation and connect it to global regulation along with improved computational approaches, will provide the opportunities to create efficient cell-factories tailor-made for the production of industrially important metabolites.As shown in this review, significant improvement in engineering the central carbon metabolism in industrially important bacteria has been obtained during recent years. The examples presented, exemplify the power of metabolic engineering by applying different methods and approaches, e.g. inactivation, overexpression, redox engineering or even in some cases engineering global control. The quantitative assessment of metabolic fluxes through the central carbon metabolism and of the flux distribution at metabolic branch points, provide detailed information not only for single reactions but also for whole pathways of a variety of bacteria. This information is expanding rapidly by the use of genome-based approaches to new areas and the discovery of novel metabolites especially in the case of The author declares that she has no competing interests.


Understanding the properties of a system as emerging from the interaction of well described parts is the most important goal of Systems Biology. Although in the practice of Lactic Acid Bacteria (LAB) physiology we most often think of the parts as the proteins and metabolites, a wider interpretation of what a part is can be useful. For example, different strains or species can be the parts of a community, or we could study only the chemical reactions as the parts of metabolism , as is done in flux balance analysis. As long as we have some understanding of the properties of these parts, we can investigate whether their interaction leads to novel or unanticipated behaviour of the system that they constitute.There has been a tendency in the Systems Biology community to think that the collection and integration of data should continue ad infinitum, or that we will otherwise not be able to understand the systems that we study in their details. However, it may sometimes be useful to take a step back and consider whether the knowledge that we already have may not explain the system behaviour that we find so intriguing. Reasoning about systems can be difficult, and may require the application of mathematical techniques. The reward is sometimes the realization of unexpected conclusions, or in the worst case, that we still do not know enough details of the parts, or of the interactions between them.We will discuss a number of cases, with a focus on LAB-related work, where a typical systems approach has brought new knowledge or perspective, often counterintuitive, and clashing with conclusions from simpler approaches. Also novel types of testable hypotheses may be generated by the systems approach, which we will illustrate. Finally we will give an outlook on the fields of research where the systems approach may point the way for the near future. Systems biology is a relatively new field of science that employs, in an iterative fashion, a combination of quantitative data, mathematical modeling and theory to come to a “systems-level” understanding. We interpret this as an understanding how the behaviour of the system, be it the frequency of a microorganism in a microbial community, or the flux through a metabolic pathway, depends on the properties of the components of the system, and the interactions between the components. It is therefore not the opposite of reductionism: in its bottom-up manifestation, systems biology uses the reductionist’s data (properties of the components), and builds a picture of the predicted collective behaviour if the interactions are included. In its top-down manifestation, systems biology aims at component and interaction identification from large data (omics) sets, where it has strong connections with (and may even be indistinguishable from) bioinformatics.Systems biology has penetrated mainstream biology considerably . Also inToday’s interest in systems biology is largely fuelled by high-throughput techniques that generate large amounts of data. There is a general consensus that functional genomics has enormous potential in the life sciences, in particular in biotechnology and medicine. How to use these technologies most efficiently, either for fundamental understanding, biomarker discovery or concrete biotech applications, is an area of active research. It is clear that the volume and complexity of the data are becoming too large to cope with by biologists alone, especially when the latter are poorly trained in advanced mathematics and computation (which is unfortunately still largely the case). So there is an understandable need from the biologist’s perspective for help in mining, interpreting and using the datasets that they collect. Such activities require modelling of one form or the other .a priori knowledge, even if it is available [Biostatistics and bioinformatics offer help in the analysis of genome-scale data sets, but they often rely on purely mathematical and statistical analysis . Althougvailable . “Bottomvailable   fluxes in metabolic networks, an area often referred to as metabolic flux analysis . In thaic pools . Models ic pools ,29, but ic pools ). This ii.e. the space of all feasible  flux distributions, is bounded in this case by the measured uptake and production rates. Hence, the algorithm will find optima at such boundaries. An interesting measure produced by the linear programming algorithm is the so-called reduced cost: it quantifies how much the objective would increase (or decrease in case of a minimization problem) if the boundary was allowed to be stretched a little [When uptake and production data are available, an extremely useful approach is to set these data as flux constraints in the model, and then perform Flux Balance Analysis (FBA). FBA requires an objective  that is optimized . The opta little . If thisa little .E. coli models , so this is not specific for LAB.There is one technical detail that is important however: although FBA is guaranteed to find the global optimal value for the objective function, there could potentially be many different flux distributions that provide that value. Thus, FBA solutions are not unique . One shoL. plantarum, interesting new biological discoveries were made applying reduced cost analysis. In a study by us, it was found that the catabolism of branched chain and aromatic amino acids contributed to ATP production [Streptococcus thermophilus, which lacks the oxidative steps of the PPP, and in this organism’s amino acid catabolism could be a major source for NADPH [S. pyogenes (and many more streptococci), a similar situation appears present .In two studies in oduction . Detaileor NADPH , althougL. plantarum[L. plantarum at progressively lower growth rates, and increasingly more amino acids were secreted (such as aspartate and arginine). Interestingly, other amino acids, notably aromatic and branched chain amino acids, were taken up in excess. Microarray data showed upregulation of plant-specific gene clusters [L. plantarum behaves as if in a plant-like environment [In the second case, reduced costs analysis was used to understand enigmatic production of amino acids at “zero growth” in plantarum. In thisclusters . Since tironment . ReducedL. plantarum, which could result in an aerobic “combustion” of glucose to CO2 and H2O, with a stunning 6 moles of ATP per mole of glucose as the result [Thus, genome-scale models can be used to analyse complex uptake and production data to get insight in limitations of fermentations and growth, with applications in growth medium optimisation. Moreover, as they constitute a comprehensive inventory of the metabolic potential of an organism, genome-scale metabolic models, in contrast to traditional MFA models, can lead to new pathway discoveries, as illustrated above with the transhydrogenase example. One more example of this is a putative transketolase cycle in e result . Althougin silico, and tested whether all co-factors can be made in the right proportions; the maximal theoretical yield can be computed, which by-products will be formed, and so forth [E coli, notably by the group of S. Y. Lee [L. lactis[Another option is to use genome-scale models for what-if scenarios, which would be very useful for metabolic engineering and synthetic biology. New pathways can be introduced so forth . Conversso forth -39. Alth. Y. Lee ,41, not L. lactis.Finally, we would like to remind the reader of the fact that genome-scale metabolic models are stoichiometric models that lack any kinetic detail. Hence, only ratios of fluxes can be computed, i.e. only questions such as “how much comes out if I put in so much?” can be addressed. These are yields, and stoichiometric models can only predict yields, or in the case of FBA optimal yields. In the section on optimality, we will revisit this point and argue that organisms, and LAB in particular, very often are not selected for yield or efficiency, but for rate. Therefore all predictions with genome-scale metabolic models will have to be weighted against this potential confounder , and the potential in vivo – in vitro differences that are unavoidable and difficult to address. Moreover, the enzyme kinetics are usually taken from databases such as Brenda or Sabio-RK [in vitro kinetics result in parameter values that are rather uncertain, yet no comprehensive  sensitivity analysis has been presented as far as we know that addresses which parameters in the model have a large effect on the dynamics and control of glycolysis. Given these limitations, the correspondence between the models and experimental data are surprisingly good, and they form a solid basis for further refinement of the models, and of understanding the biochemical basis of primary metabolism in L. lactis.Both approaches have their pros and cons. The Hoefnagel approach is closer to biochemistry and biological intuition, but suffers from insufficient kinetic data , the total amount of phosphate contained in all metabolites such as PEP, F16bP, ATP and Pi, cannot change . Note that these moiety conservations may be artefacts of models since we ignore potential sources an sinks, such as polyphosphate, but these fluxes are likely slow compared to the high glycolytic flux. Hence, if the kinetics in the model are such that F16bP drops because of glucose exhaustion, the associated phosphates have to go to some other pools, notably PEP and Pi. So the question really is what the most important kinetic parameters are that cause the change in distribution of phosphate over the glycolytic pools, ATP and Pi. This has not been fully addressed yet, and thus the claim that the feed-forward activation of PK by F16bP is relevant in this respect, is in our opinion still pending.Backed up by on experimental evidence Hoefnagel  stressedL. lactis. Most if not all glycolytic pathways exhibit this (over 30 cases in the Brenda database), notably organisms that do not have a PTS glucose uptake system, and hence, do not rely on high PEP levels to “start-up” glycolysis upon glucose re-addition. So, one may wonder about the functional interpretation of the feed-forward activation in other organisms. One hypothesis we have is linked to the kinetics and thermodynamics of GAPDH: this enzyme operates near equilibrium and is very sensitive to mass action [E coli[It should be noted moreover, that the F16bP feed-forward loop on PK is by no means unique for s action . High F1n [E coli) and henn [E coli, showingn [E coli: these sL. lactis that has not received the attention we think it deserves: the negative feedback of F16bP (and Pi) on the PTS system. Within the PTS system in gram positives, the HPr protein has a dual role: when phosphorylated at the His-15 residue, it allows phosphate group-transfer within the chain of phosphorylation events that lead to uptake and phosphorylation of glucose. When phosphorylated at the Ser-46 residue, however, HPr acts as a signalling intermediate in glucose repression, activating CcpA [Apart from the feed-forward loop, there is another important regulatory loop in the glycolytic system in ing CcpA . The lating CcpA . Studiesing CcpA . This feing CcpA  looking ing CcpA ).L. lactis glycolysis that is screaming for a mechanistic explanation –still- is the  “switch” between homolactic and mixed acid fermentation, i.e. the fact that L. lactis exhibits mixed acid fermentation  at low dilution rates in the chemostat, and homolactic fermentation at high dilution rates  in modern molecular biology. Efficient causes are, for example, the molecular details of passive or active regulatory mechanisms that lead to a certain behaviour. The apparent final cause, or function of such regulatory mechanisms is the efficient survival and replication of organisms in which they act. Darwin already noticed that this final cause, as he also calls it, is programmed by the mechanism of natural selection [in silico cell” type [The distinction made between different types of explanatory causes by Aristotle is still applicable in biology. In particular, it is important to distinguish the efficient and final causes . Similar effects are predicted by models in the FBA framework when a crowding constraint is imposed on the total amount of enzymes [E. coli, S. cerevisiae and B. subtilis[L. lactis different observations were made. In L. lactis ML3 the specific activity of LDH increased with increasing growth rates and substrate concentrations, but the authors mentioned noticeable differences between strains in shift behaviour [L. lactis IL1403 where proteomics was used to measure relative protein concentrations in cells grown at different dilution rates showed that the level of LDH is relatively constant, but that protein ratios in the mixed acid branch decreased with increasing growth rate and substrate concentration [las-operon in L. lactis[L. plantarum [Some time ago we published a hypothesis in which we proposed that several global characteristics of microorganisms, like overflow metabolism, might be the result of maximization of the growth rate . For the enzymes . Indeed  subtilis,78,85-87ehaviour . More rentration ,89. HencL. lactis does notlantarum ), there E. coli quickly adapts to lactose concentrations in the medium. When cultures are serially transferred on media containing a fixed lactose concentration, mutants appear with an adapted expression of the lacZ gene [lacZ at higher lactose concentrations in the medium. Stabilization of expression levels occurred after 300-500 generations. This example showed that selection pressure on optimal expression of one protein, amounting to perhaps 3% of the total protein can be very strong .L. lactis and E. coli wild types. For example, when the growth rate of E. coli is measured at various expression levels of the proton-ATPase gene, the maximal growth rate is observed exactly at the wild-type level [L. lactis, like LDH, PFK, PK, PGM, PGE, GAPDH, as well as the activity of the entire las operon [In the optimal state apparent in vivo flux control coefficients equal 0”). The control coefficient of an enzyme is defined as the ratio of relative changes in a flux over relative change in that enzyme, without changes happening in the other enzymes. The latter condition can not be guaranteed in living systems, as they may adapt the amounts of other enzymes in response to experimentally induced changes in a target enzyme.A series of experiments from the group of P.R. Jensen at the Technical University of Denmark, show a signature of optimal expression levels in pe level . Other ps operon -97,67. Fs operon , but it N different enzymes iE, where i is an index for the different enzymes running from 1 to N. The metabolic flux J through the pathway depends on the concentrations ie of the enzymes iE, i.e. it is a function of those concentrations. Then the flux control coefficient of iE is defined as [The flux control coefficient of an enzyme on a pathway flux is defined as the relative effect of the concentration of the enzyme on the metabolic flux through the pathway. To state this in mathematical terms; suppose we have a pathway with fined as :J / ∂ie indicates). This is an important condition which is likely not to hold in living systems, as will be discussed below  in L. lactis is doomed to fail. The economic perspective to cellular growth strategies, including conceptual models and theory sketched above, allow the test of scenarios in which still more resources may be allocated to glycolysis. These activities could be strengthened by strain diversity studies, in which differences in acidification rates are screened and mapped to molecular mechanisms. Alternatively, one may have to conclude that L. lactis has indeed hit the physical boundaries of its biological apparatus, which may be somewhat disappointing, but useful to know. These boundaries are nevertheless likely to be dependent on the environmental conditions, especially if these are dynamic in nutrient composition and competing species. Thus what is optimal in one state is probably sup-optimal in another, and this is still rather uncharted territory in microbial systems biology. In particular, many methods in systems biology, such as FBA, work only for monocultures under constant environments, and there is an urgent need to move to more complex (eco)systems. One theoretical framework that deals with such conditions is evolutionary game theory, which will be illustrated for LAB in the next session.Although this effect is expected to hold only for a flux that is evolutionarily optimized, which in case of growth is the biomass synthesis rate, any flux closely linked to biomass synthesis, like ATP production, or the glycolytic flux, can be expected to behave similarly. So, the seminal work of P.R. Jensen’s group should not be viewed as a failure to figure out where control of glycolysis is located, but as important evidence that Lactococcus lactis is one of the dominant bacterial species in many dairy starter cultures [cultures ,102. Strcultures ,104 and cultures . The precultures -108, thecultures -113. In cultures  first decultures . The faccultures ,117.Bacillus subtilis is heterogeneous within a clonal population [+ relative to Prt- cells. The second assumption is that in milk a Prt+ cell can take up a fraction of the extracellular degradation products before they diffuse away. If this is the case in a mixed culture of Prt+ and Prt- cells, the burden of protease expression can be compensated by the ability to capture more peptides. This ability increases with decreasing total cell densities and with a decreasing fraction of Prt+ cells in the culture , likely caused by accumulative adaptations to different cultivation histories. For a number of systems biology projects in The Netherlands, we have therefore developed standards for cryopreservation, chemically defined medium and assay buffer (and assays) for all glycolytic enzymes, which has been adopted by the Dutch researchers active in systems biology of L. lactis. Although we obviously hope that these standards will be accepted by the community, it is more important that we agree on some standards: the advantages should be obvious. It will also allow better disentanglement of effects caused by external condition and for example genetic diversity.In this light, and as promised, a remark on the issue of standardization. Systems biology has clearly pointed at the inefficient and fragmented use of resources within the life sciences. Each lab now uses its own medium, assay buffer conditions and notation of data and model components. Hence data cannot be pooled easily for modelling purposes, or not at all, even if the same question was addressed in the same strain. If we want to become precise and quantitative in biology, this will have to change. For example, we have found substantial physiological differences between L. lactis, in particular the recent study in which protein and mRNA stability were modelled based on transcriptome and proteome data [So what are the further challenges in microbial systems biology? Obviously, we are far away from capturing the complexity of true living cells with current models. Functional genomics tools become more and more quantitative, and provide valuable, comprehensive data sets on relevant processes in the cell. A number of studies from the groups in Toulouse have demonstrated that beautifully for ome data ,131. AddBacillus subtilis[Apart from additional components and interactions, we also see a clear trend towards single-cell technologies. At this level, we observe that noise and heterogeneity are crucial factors to be included in our understanding of phenotype . Such st subtilis, bet-hedLactobacillus bulgaricus and Streptococcus thermophilus. We expect major efforts and breakthroughs in the direction of linking microbial physiology with population dynamic modelling and ecological theories.Finally, we need to bridge the gap between cellular processes and population dynamics in communities. Sequencing of such populations and communities has become affordable, giving rise to the field of metagenomics, and early steps into the direction of metatranscriptomics and metametabolomics. Obviously, for LAB and their applications, these developments are extremely relevant. We have discussed how game theoretical approaches can help in understanding general principles and forces at play in populations, giving rise to often non-intuitive phenotype dynamics. At a more molecular level, we have only begun to scratch the surface of compounds involved in the interactions, technologies to measure community fluxes , and modCFU: Colony Forming Units; F16bP: Fructose 1,6-bisphosphate; FBA: Flux balance analysis; GAPDH: Glyceraldehyde 3-phosphate dehydrogenase; LAB: Lactic acid bacteria; LDH: Lactate dehydrogenase; PEP: Phosphoenol pyruvate; PFK: Phosphofructokinase; PFL: Pyruvate:formate lyase; PGE: Phosphoglycerate enolase; PGM: Phoshoglycerate mutase; Pi: iIorganic phosphate; PK: Pyruvate kinase; PPP: Pentose phosphate pathway; Prt+/Prt-: Protease positive and negative phenotypes; PTS: Phosphotransferase system.The authors declare that they have no competing interestsBT, DM and HB wrote the review together.


Plasmodium falciparum is an obligate intracellular parasite and the leading cause of severe malaria responsible for tremendous morbidity and mortality particularly in sub-Saharan Africa. Successful completion of the P. falciparum genome sequencing project in 2002 provided a comprehensive foundation for functional genomic studies on this pathogen in the following decade. Over this period, a large spectrum of experimental approaches has been deployed to improve and expand the scope of functionally annotated genes. Meanwhile, rapidly evolving methods of systems biology have also begun to contribute to a more global understanding of various aspects of the biology and pathogenesis of malaria. Herein we provide an overview on metabolic modelling, which has the capability to integrate information from functional genomics studies in P. falciparum and guide future malaria research efforts towards the identification of novel candidate drug targets. Plasmodium falciparum genome sequence [Plasmodium or apicomplexan species, are of particular interest as their unique nucleotide sequences may provide higher selectivity for new antiparasitic drugs. However, in the absence of tools for high-throughput gene knockdown such as RNAi [P. falciparum are cumbersome due to several unique properties of the pathogen. First, it possesses an extremely AT-rich genome along with an unusually low frequency of homologous recombination, which makes it refractory to genetic manipulation such as gene replacement [in vitro cultivation is a delicate process [in vivo assessment of gene essentiality very limited and expensive. Nevertheless, emerging experimental breakthroughs hold promises for cost-effective gene knockdown strategies of the P. falciparum essential genes at a high-throughput scale [According to the recent ‘World Malaria Report’ by the World Health Organization, malaria remains a major healthcare issue, being responsible for more than 200 million cases and hundreds of thousands of deaths in 2010 alone [sequence . These g as RNAi , the ide as RNAi . Availablacement . Second, process  that is ut scale , 7. In tin silico) metabolic models offer a cost-effective pipeline to identify putatively indispensable metabolic functions that, in the case of pathogens, represent potential targets for medical intervention [P. falciparum, it has become evident that reconstruction and analysis of in silico models is a valuable tool for studying various aspects of the pathogen [in silico metabolic modelling efforts to functional genomics studies on the deadly malaria parasite.Metabolic modelling is a modern approach of systems biology that, among several other applications, has been extensively exerted to predict gene essentiality in various bacteria including a number of pathogenic species . Computarvention . Eight ypathogen . In thisP. falciparum [in silico metabolic models have been shown to provide such context, thus enabling researchers to use available datasets (i.e. content) to improve and challenge the models and to derive additional, non-intuitive insights. In particular, this need for an explicit, up-to-date context is a driving force for the progress from a scope of generic biochemical knowledge to organism-specific (or life stage specific) metabolic networks, which we will discuss further.With the constantly decreasing cost of high-throughput measurements, the tendency to generate very large -omics datasets is emerging and this holds also true for lciparum . There aP. falciparum metabolism, such as cofactor utilization, compartmentalization and proteins involved in the transport of metabolites. The web-resource Malaria Parasite Metabolic Pathways (MPMP) is essentially the product of an extensive manual revision of KEGG maps according to known and recently established metabolic features of the P. falciparum intraerythrocytic life stage. Furthermore, integration with other web-resources facilitates quick access to additional information and related primary literature [P. falciparum’s metabolism, but unfortunately the lack of any application-programming interface limits the access of a broad research community to this high-quality data. Utility of MPMP for research purposes has been demonstrated via interpretation of the expression patterns for the genes involved in the pentose-phosphate pathway using the corresponding maps [In the past two decades, several databases of biochemical reactions and metabolic pathways have been developed to provide a systematic and comprehensive overview of metabolism. A pertinent example is the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database , which hterature . MPMP ising maps . This leing maps . In conting maps .P. falciparum in a systematic manner. GBMs represent metabolism as graphs with nodes (to denote metabolites) and links between them (respective metabolic reactions) [Graph-based models (GBMs) were built to comprehend and analyse the metabolic capabilities of actions) . The linOver the past decade flux-balance analysis (FBA) has been established as a leading approach for studying constraint-based models (CBMs) of cellular metabolism . ConstraCBMs, unlike GBMs, do require a pre-defined objective towards which utilization of available substrates should be optimized . A commoLeishmania major, which previously had no functional annotations [P. falciparum that has more than 2000 genes without even putative functional annotation.A conventional reconstruction workflow  clearly otations . Althougin silico essentiality studies in the CBM of Saccharomyces cerevisiae [One of the most important applications for CBMs of pathogenic species is the capability to predict potential vulnerabilities in their metabolism . Often mrevisiae  for furtP. falciparum . These were ranked as targets of particular interest based on evidence of activity during the multiple stages of P. falciparum life cycle studied and/or the presence in SuperTarget database [Using a constraint-based approach, Huthmacher et al.  assembledatabase  as candidatabase . Furtherdatabase .et al. [in silico were found to be in correspondence with reports in the primary literature: 100% agreement when compared with gene deletion studies and 70% in case of enzymatic inhibition experiments. Comparison of the number of essential genes in the models of P. falciparum and S. cerevisiae confirmed the notion that the parasite likely possesses significantly lower metabolic flexibility to bypass single-gene deletions when compared with free-living organisms with similar genome sizes [in silico essentiality and extremely low or absent sequence identity to human proteins [P. falciparum proliferation at an IC50 of 50 µM in in vitro culture [et al. [in silico double-gene deletion simulations in P. falciparum leading to the identification of 16 pairs of genes that were predicted as non-essential by single-gene knockout simulation but resulted in a dramatic impairment of the metabolism if targeted simultaneously.The second CBM developed independently by Plata et al.  also toome sizes . Forty gproteins . The ess culture . In addi [et al.  were theP. falciparum metabolic network [in silico the essentiality of P. falciparum genes in liver stage and assessed, using a ‘reduced fitness’ approach, effects of targeting enzymes that are homologous and predicted to be essential both in the host and the pathogen [Holzhutter and co-workers have developed the most recent CBM of the  network  by updat network . In Plas network  they hav network  and remo network , they evpathogen .et al. aimed at predicting the essentiality of genes, whereas the other studies assessed the essentiality of enzymes, the predictions made do not overlap when gene-to-enzyme relations are not one-to-one. Second, a gene or enzyme may be absent from the list of ‘predicted as essential’ not only because it has been predicted as non-essential, but possibly, also due to the fact that the corresponding metabolic process simply is not included in the model of interest. Third, essentiality predictions in CBMs are directly dependent on the set of metabolites included into their biomass reaction, so that differences in assumed biomass composition directly affect the results of in silico simulations. For an overview of P. falciparum genes and enzymes predicted as essential in existing models, we refer the reader to the Supplementary Table 1. The table also provides the reader with literature references on the experimental assessment of genes/enzymes predicted to be essential by the different models.Comparison of the essentiality predictions made by the aforementioned models is not a trivial task. First, because the study by Plata ad hoc assignment of directionality to the reactions, which are pre-set either as all reversible [ad hoc assignments might violate this fundamental constraint. This issue has been addressed rigorously in genome-scale metabolic networks of several organisms [An important simplification common to all the aforementioned models is the versible  or assumversible . In prinrganisms  by impleP. falciparum to date lags several years behind the similar efforts for the model eukaryote S. cerevisiae; there exist a few independently reconstructed models that often lack consistency with each other due to the differences in the reconstruction workflows, the sources of primary information, the level of complexity and the varying degree of comprehensiveness. Similarly to the trends in modelling of the yeast metabolism, we expect the emergence of reconciliation efforts that will aim at obtaining a consensus, up-to-date CBM of P. falciparum. Recently developed workflows for manual [P. falciparum; the second is a deliberate revision of the objective function (i.e. biomass reaction) to make it consistent with the actual biomass composition of the parasite at the life stage of interest; the third is an experimental verification and quantification of the uptake fluxes present in the models. These improvements are likely to make in silico predictions of gene essentiality more reliable and also expand the number of metabolic functions currently known to be essential for P. falciparum.Overall, metabolic modelling of r manual  and semir manual  reconcilP. falciparum in the form of in silico models, which not only reproduce the prior knowledge, but also provide novel insights. Nevertheless, the fact that cultivation of the parasite in a fully defined medium is still impossible clearly highlights that some important metabolic peculiarities remain to be discovered. The promising direction is the utilization of the CBMs as frameworks of current knowledge, which can then be challenged, refined and constrained by various high-throughput datasets as discussed in the following section.Metabolic reconstruction efforts to date have summarized the results of decades of experimental research on the metabolism of As mentioned above, CBMs hold a great potential to incorporate various types of experimental data as content for the context of computational metabolic networks. In this section of the review we provide an outlook of the currently published studies that have integrated computational modelling and experimental research efforts for each type of high-throughput data introduced in P. falciparum have yielded so far several complete genome sequences (for 3D7 and IT strains [in silico.Genomics studies of  strains ); howeveP. falciparum is a nascent field in malaria research since only a limited number of studies have succeeded in generating single-gene mutant parasite clones at a large scale. The largest coverage reported to date was achieved with a forward genetics approach based on transposon mutagenesis using the transposable element piggyBac [P. falciparum genome. Among those, 24 single-gene disruptions caused severe growth defects in vitro, which might lead to lethal phenotypes in vivo [in vitro essentiality data of the genetically tractable parasite Trypanosoma brucei have been obtained for nearly all the coding sequences of its genome using the RNA-interference target sequencing approach [High-throughput functional genomics of piggyBac . The col in vivo . A major in vivo , it stilapproach .in silico simulation of similar genetic perturbations. This offers reciprocal benefits for experimental and computational research: CBMs can often suggest the underlying reason for viable/non-viable phenotypes of knockout strains and, otherwise, reveal the weak points, where the model should be improved. Accordingly, the algorithm GrowMatch [S. cerevisiae with in silico gene essentiality predictions [P. falciparum, GrowMatch or a similar algorithm could validate the computational model, to improve understanding of the obtained results and produce hypotheses for experimental investigation, applicable to malaria drug research.The value of the gene essentiality datasets is reinforced by the possibility of rowMatch  has beendictions . This apdictions . Once a http://www.plasmodb.org) for different lineages of the parasite under various conditions. Although the scope of gene expression data is growing, new increasingly advanced algorithms to integrate these datasets into CBMs are also being developed [P. falciparum metabolism have incorporated available transcriptomics data as constraints [Transcriptomic profiling appears to be the one of most common high-throughput methods applied in malaria research; numerous gene expression datasets are available in PlasmoDB database , the st enolase . However enolase . Mair et [et al.  demonstrP. falciparum. Meanwhile however, it has been done for an intercellular pathogen Trypanosoma cruzi: Roberts et al. have integrated in their CBM a proteomics dataset that constrains fluxes through reactions whenever the corresponding enzyme is not detected in life-stage-specific proteome. Using this constraint the authors aimed at making the model the most representative possible of the metabolic state of the pathogen at a particular stage of its life cycle. Initially this resulted into an over-constrained model that was unable to simulate growth, suggesting that even though enzymes for some reactions were not detected in the proteome they were likely to be present. On the other hand, constraints inferred from the proteomics data corrected those reaction essentiality predictions that were not in agreement with experimental data without these constraints [Despite the availability of the data and necessary computational methods, proteomics have not been used systematically in the development and analysis of CBMs of straints .Although a complex interplay between concentrations of mRNAs and enzymes with the fluxes through the corresponding reactions remains to be elucidated, utilization of transcriptomics and proteomics information together can ensure higher confidence that an enzyme of interest is (or is not) present in the life stage of interest. For instance, it is reasonable to assume that a reaction for which both enzyme and transcripts cannot be detected is most likely not to occur and should be tightly constrained in the stage-specific CBMs.P. falciparum-infected human erythrocytes [P. falciparum putative metabolome achieved in this study is between 15% and 20%  and comstigotes . Current dataset  gives a 13C-NMR can identify metabolites that are otherwise undetectable by MS due to their low ionization potential . Using hozoites . The unbhozoites .P. falciparum is that, for metabolites present in more than one compartment , the measured concentration only reflects an average value, which may differ significantly from the actual concentration in each of the compartments. Metabolomics on separate organelles is an emerging field in functional genomics research, as exemplified in algea [P. falciparum.An important drawback of the current metabolomics studies of in algea , which wP. falciparum, to date, are the assessments of influx rates for single substrates: glucose , ishrocytes , pantothhrocytes  and inorhrocytes . Due to S. cerevisiae, measured metabolic fluxes were included into the CBMs and allowed in silico resolution of experimentally observed metabolic features that could not be inferred otherwise—neither from transcriptomics nor proteomics data [In the case of model organisms, e.g. ics data . A relevics data . This leics data .P. falciparum in particular. We argue that this is not only due to the complexity of the experimental study of pathogens, but also due to the fact that a majority of the in silico methods discussed above were initially designed for free-living organisms. Although the methods are, in principle, applicable to intracellular pathogens, some aspects unique to the parasites may be crucial for obtaining relevant computational results. Examples of such aspects would be the common absence of a clearly defined set of substrates and by-products for the metabolism of intracellular parasites, the changes in composition of biomass across multiple stages of their life cycles or sub-optimal utilization of available substrates, etc. These issues and the aforementioned experimental challenges represent the area where we expect forthcoming improvements, leading to a better understanding of metabolic peculiarities encoded in the genome of P. falciparum.Overall, out of numerous methods developed for the integration of experimental data with CBMs, only a modest proportion have hitherto been applied to pathogenic organisms and P. falciparum represents a significant challenge for medical treatment, as it is virtually impossible to avoid impact of antimalarial drugs on the host. Thus, high selectivity is an essential prerequisite for all potential drug candidates. However, many of the genes and enzymes predicted as being essential in P. falciparum do possess a variable level of sequence identity to their human counterparts [in silico, the ‘reduced fitness’ approach was applied by Bazzani et al. [P. falciparum embedded into the model of a human hepatocyte. This method can estimate quantitatively how production of biomass would be affected in the host and the parasite’s metabolic networks when the flux through a targeted reaction is gradually reduced to the same extent in both models. Although it shows a purely theoretical sensitivity of the host and the parasite to inhibition of a certain enzymatic activity, this approach should be considered with caution as it neglects some important aspects, which cannot be captured by CBMs.Obligate intracellular parasitism of terparts , 12, 13,i et al.  to the CFBA is indeed suitable for simulation of the outcomes for gene knockout experiments, when targeted enzymatic activity is abolished completely. However, a realistic simulation of the effects of reduced enzymatic activity (i.e. action of a drug) requires careful consideration of kinetic and thermodynamics properties. Ultimately, even if one assumes identical kinetic properties for inhibited enzymes in the infected host cell and its parasite, sensitivity of the flux through the catalyzed reaction to the same amount of inhibitor can be different simply if the expression level of this enzyme is not the same in these organisms .et al. [Metabolic control analysis (MCA) considers all the relevant properties to provide a more realistic estimation about the extent to which flux through a reaction of interest is sensitive to the action of an inhibitor . Using tet al.  have demP. falciparum is the need to integrate the kinetic parameters of enzymes in the model and the concentration profile of key metabolites [in silico predicted drug targets. These efforts may also provide a valuable guidance for further development in the functional genomics of P. falciparum, whereas analytical technologies will provide the information necessary for building a kinetic model of its metabolic network.A major limiting factor for application of MCA to metabolic models of abolites . To dateabolites , which iabolites , 87. Theabolites . Kinetic


Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor. A detailed description of the human metabolic network is essential for a better understanding of human health and disease . Severalin silico model of the human metabolic network is Homo sapiens Recon 1 [Over the past fifteen years several groups have constructed high-quality human (metabolic) pathway databases that can be used in this endeavor -11. One  Recon 1 . This moin silico. Most of the initial content of HumanCyc [The various pathway databases available differ in a number of ways and all have their own strengths and weaknesses. For example, they have different solutions for technical issues such as how the data is presented to the user, how one can query the database ,13, and HumanCyc  was autoe.g., a biomedical scientist who wants to use these databases as a reference repository or a bioinformatician who wants to perform a systems-level analysis of human metabolism, it is crucial to know the extent of the differences in content as well as their underlying causes. The choice for a particular database may, for example, influence the outcome of a computational analysis, as evidenced by diverging results for methods that were applied to multiple metabolic pathway databases [S. cerevisiae [S. typhimurium [It may be apparent that the described differences will have an effect on the metabolic networks defined by the databases. However, so far the various metabolic networks available have not been systematically compared, nor has the extent to which they differ been quantified. For a researcher, atabases -22. Moreatabases . This harevisiae  and for himurium . The resWe performed a systematic comparison of five frequently used databases, each of which is based on a different approach towards reconstructing the human metabolic network and built by an independent research group: EHMN, Homo sapiens Recon 1 (referred to as BiGG in the rest of the paper), HumanCyc, and the metabolic subsets of KEGG and Reactome. We compared the metabolic reactions, Enzyme Commission (EC) numbers, enzyme encoding genes as well as combinations of these three elements across the five selected databases. We provide an overall analysis, but also compare the tricarboxylic acid (TCA) cycle separately to see in how far the databases agree on this classical metabolic pathway. Our comparison allows us to identify the parts the databases agree on and at the same time to reveal conflicting information. Moreover, current reconstructions of the human metabolic network are work in progress and, therefore, still contain gaps as evidenced by the regular updates of the various databases, reported dead-end metabolites (Recon 1) , and lisOur results show a surprisingly limited level of agreement between the five databases and highlight the challenges to be met when integrating their contents into a single metabolic network.i.e., BiGG, EHMN, HumanCyc, KEGG, and Reactome  under consideration. The consensus is constrained by the smallest database for a specific entity, which is in all cases Reactome. Therefore, we also calculated a score that is less sensitive to these differences in database size, the majority score, defined as the number of entities that occurs in at least three out of the five pathway databases as a percentage of their union. To limit the impact of out-of-date identifiers and EC numbers on our comparison, the ones that had been transferred were replaced by their new ID/EC number and otherwise they were not used in the comparison  annotation of the 510 genes in the consensus on gene level versus the union of the remaining genes using FatiGO  to gain The Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB) classifies and names enzymes according to the reaction they catalyze . EC numbIf we compare complete EC numbers, thus taking into account the serial number that represents substrate specificity, the consensus decreases to 18% of the 1410 EC numbers contained in the union  or on metabolite name, provided that also the chemical formula matched. The consensus for the metabolites is only 9% of the 4679 metabolites contained in the union Table . The majReactions were considered to be the same if all substrates and products matched (see above). As expected, given the outcome of the metabolite comparison, the number of reactions included in all five databases is small: consensus on reaction level equals 1% of 7758 reactions in the union of all five databases Table .-), protons (H+) and water (H2O) [Reactions are not always balanced, especially with respect to electrons (eer (H2O) . TherefoWe compared a relatively large number of pathway databases, each restricting the consensus, which partly explains the small overlap. If we only compare pairs of databases, the overlap on reaction level increases substantially. The consensus of two databases ranges from 11%, when comparing EHMN and Reactome, to as much as 28% when comparing EHMN and KEGG  connected to a gene. For 63% of the consensus genes the databases agree on at least one EC number. The mismatches suggest that the databases do not fully agree on the enzymatic activities that gene products can have.-, H+ and H2O). For 44 reactions the databases agree only on the EC number and for 25 reactions only on the genes. For 24 consensus reactions there is not a single EC number the databases agree on and not a single gene for 9 reactions. The main reason (57 reactions) that there is no agreement on all genes is because one or more of the databases links additional genes to the reaction in comparison to the other databases. See Additional file If we require an exact match on all three levels - EC number, gene, and reaction - then the five pathway databases agree on the genes and EC numbers of 85 of the 199 reactions in the consensus  disagreement on pathway definition, (ii) difference in number of intermediate steps, (iii) a different number of possible alternative substrates. In addition, it is difficult to determine when databases refer to the same metabolite. Missing and out-of-date gene identifiers also hinder the comparison. Since genes and EC numbers are tightly linked to reactions, most differences on these two levels are caused by differences on the reaction level. We conclude with additional causes for lack of consensus for genes and EC numbers.In what follows we present the main, sometimes overlapping, causes for lack of consensus at the reaction level: . This reaction is found in BiGG, EHMN, and KEGG. According to Reactome the reaction belongs to the pathway 'Fatty Acyl-CoA Biosynthesis' and HumanCyc assigns it to 'acetyl-CoA biosynthesis (from citrate)'. Moreover, Reactome also provides evidence that the reaction takes place in the cytosol and not in the mitochondrion where the TCA cycle takes place. Interestingly, BiGG and EHMN also claim that the reaction does not take place in the mitochondrion, but they include it in the TCA cycle nevertheless.C. The reaction transforming succinyl-CoA into succinate via GDP → GTP (EC 6.2.1.4). This reaction is described in HumanCyc, but was not assigned to any pathway.+/NADPH and NADH/NADP+. Only Reactome includes this reaction in the TCA Cycle, in the other four databases it is part of pathways related to nicotinate and nicotinamide metabolism.D. The interconversion of NADDifferences in pathway definition explain why 14 of the 30 reactions are not in the consensus . KEGG describes this reaction in four steps. In BiGG, HumanCyc and Reactome the entire oxidative decarboxylation is described in a single step. Interestingly, EHMN describes it both in a single step as well as in three steps.The databases also disagree on the number of steps for describing the conversion of citrate to isocitrate (EC 4.2.1.3). In BiGG and Reactome this is a single step, but it takes two steps in HumanCyc and KEGG with cis-aconitate as intermediate. Indeed, cis-aconitate has been shown to be an intermediate in the conversion of citrate into isocitrate ,32. EHMNA third explanation for the observed differences is the variation in the number of possible alternative substrates listed. This is, for example, observed for the type of nucleotide diphosphate as cosubstrate for the conversion of succinyl-CoA to succinate. According to EHMN and KEGG, not only ADP (EC 6.2.1.5) can be used, but also IDP (EC 6.2.1.4). Differences caused by alternative substrates explain six mismatches on reaction level.e.g., lipoamide-E (KEGG Compound ID: C15972), and the other is indicated as being unbound, e.g., lipoamide (KEGG Compound ID: C00248). For a relatively small pathway like the TCA Cycle, such highly similar compounds can be easily identified manually, but on database level this is very challenging.The comparison of the metabolites is hindered by the difficulty of determining in an automated way when databases refer to the same compound. For example, we decided for three pairs of metabolites that the databases are referring to the same metabolite, despite that the databases linked different KEGG Compound IDs to these metabolites . The only difference between these pairs, is that one is the enzyme bound form of the metabolite, Also out-of-date and missing identifiers influence the comparison. Five unmatched genes from Reactome had an Entrez Gene ID that had become obsolete and could not be transferred to another entry. For a single gene in HumanCyc there were no gene identifiers available at all.On gene level, ten differences remain that are not caused by differences on the reaction level or out-of-date identifiers. Three genes  encode for proteins that are not localized in the mitochondrion, according to the UniProt annotation. Since the TCA cycle takes place in the mitochondrion, these may be annotation errors of the pathway databases. In BiGG PDHX encodes for a component of the 2-oxoglutarate complex, but according to Entrez Gene it encodes for a component of the similar, but different, pyruvate dehydrogenase complex. The gene OGDHL, which is found in three databases, is described by Entrez Gene as 'oxoglutarate dehydrogenase-like', which refers to the OGDH gene that is part of the consensus. For two genes (LOC283398 and SUCLA2P1) in Reactome the RefSeq status is 'inferred', which may be a reason for the other databases to not include these genes. For the other three genes  there is no clear explanation. Possibly these are incorrectly connected to the reactions of the TCA cycle.+/NADPH as cosubstrates instead of NAD+/NADH.For four EC numbers the differences on reaction level do not explain why they are not part of the consensus. All four are assigned to the reaction converting 2-oxoglutarate to succinyl-CoA by at least one of the databases. Three of these EC numbers belong to the individual components of the complex catalyzing the reaction. BiGG only assigns one (EC 1.2.4.2) of these three to the catalyst, EHMN assigns all three and HumanCyc leaves the EC number blank. According to IUBMB the EC number (EC 1.2.1.52) assigned by Reactome belongs to the enzyme that can catalyze a similar reaction, but with NADPThe explanations we gave for the lack of consensus in the TCA cycle can be generalized to the comparisons at database level. One exception is the difference in pathway definition, as the subdivision of the network in pathways no longer plays a role in the comparisons on database level. However, a similar effect can be observed due to differences in metabolic network coverage.All five databases are work in progress and, therefore, do no yet fully cover the complete metabolic network. As the database content statistics Table  show, thTo gain a better understanding of which parts of the metabolic network are only described in a single database, we compared the GO annotation of all 1139 unique genes versus the union of the remaining genes using FatiGO .Next to the reasons outlined above, we also identified a number of more subtle and less frequent explanations for the limited overlap. An example at the metabolite level is that BiGG uses D-glucose in its reactions instead of specifying whether it is α-D-glucose or β-D-glucose, while Reactome only uses α-D-glucose. The other databases use all three variations. On the other hand, BiGG does not use generic metabolites like 'an alcohol' (KEGG Compound ID: C00069) or 'an L-amino acid' (KEGG Compound ID: C00151) in contrast to HumanCyc, KEGG and EHMN. Furthermore, BiGG and HumanCyc explicitly state that their reactions are charge and mass balanced. The chemical formula and charge of the metabolites were based on their ionization state at a pH level of 7.2 and 7.3, respectively, while the other three databases use the neutral form of the metabolites. This partly explains the observed increase in consensus when we did not take into account HOur comparison revealed that there is only a small core of the metabolic network on which all five databases agree. Especially on reaction level the overlap is surprisingly low, only 199 reactions could be found in all five databases. Our analysis shows that the small overlap between the databases is partly explained by conceptual differences like a difference in coverage of the metabolic network. One clear example is the large set of transport reactions and reactions in lipid metabolism in EHMN, which account for 23% of the unique reactions.Our decision to compare five pathway databases, also limits the consensus: the more databases one includes in the comparison, the lower the consensus is likely to be. We indeed observe a substantial increase in overlap when we compare pairs of databases  in reactions, as HumanCyc does. This may be done to model the broad substrate specificity of the enzyme or to indicate that the exact substrate specificity is unknown. Other databases, for example BiGG, focus more on indicating the specific metabolite, e.g., ethanol instead of alcohol. This difference may be amplified by the number of specific instances given. Also more subtle conceptual differences play a role, like a different protonation state , the detail in which the structure of a metabolite is described  or whether the metabolite is described as enzyme bound or not . Finally, our GO enrichment analysis showed that the scope of the metabolic networks described by the five databases differs. The set of genes that are only found in at most two databases is, compared to the genes found in the majority of the databases, enriched for terms related to protein metabolic processes, like protein phosphorylation, proteolysis, and RNA metabolism  discern useful complementary information from conflicting information. In this respect, a more widespread use of evidence codes indicating the type of evidence supporting the data would enable to make a distinction between high and low confidence data. However, extensive annotation of evidence is currently only provided by BiGG and HumanCyc.http://www.molgenis.org/humanpathwaydb. Here, results of the comparison can be queried, sorted, and exported in a number of ways. The web application was generated using the MOLGENIS toolkit [e.g., an R interface. Using, for example, the majority reactions as a starting point for curation these overviews could aid experts on the human metabolic network to consolidate the differences between the networks and arrive at a unified model of human metabolism.Significant manual intervention will be needed to reach the ultimate goal of a single human metabolic network. A promising model is a community-based approach, such as WikiPathways  or an an toolkit  and nextAn accurate and complete reconstruction of the human metabolic network is of utmost importance for its successful application in the life sciences. Our results will help curators to even further improve the metabolic network as described in the individual databases. Furthermore, as our analysis shows, each of the five pathway databases discussed in this paper provides us with a valuable piece of the puzzle. Combining the expert knowledge put into these five reconstructions and the evidence provided will improve our understanding of the human metabolic network. However, we explicitly identified many issues that prohibit the (automatic) integration of the metabolic networks. Not only the unambiguous identification of metabolites is required but the conceptual differences need to be addressed as well. Considerable manual intervention and a broad community effort are needed to reach the ultimate goal of a consolidated and biologically accurate model of human metabolism. Community efforts, such as BioPAX  and SBGNFor each of the five pathway databases, we retrieved all metabolic reactions with their corresponding gene(s), EC numbers, and pathway(s). All files mentioned below were downloaded in May, 2011.http://www.genome.jp/kegg/compound/, KEGG Glycan http://www.genome.jp/kegg/glycan/, ChEBI http://www.ebi.ac.uk/chebi/, PubChem http://pubchem.ncbi.nlm.nih.gov/, and CAS Registry Numbers . There are two types of PubChem IDs, Substance and Compound. Substance IDs are specific for the depositor of the metabolite. Compound IDs unite the different Substance IDs for the same metabolite. We used the CID-SID file ftp://ftp.ncbi.nih.gov/pubchem/Compound/Extras/CID-SID.gz to convert PubChem Substance IDs to PubChem Compound IDs.For the metabolites we retrieved the following, most frequently provided, types of identifiers, if available in the specific pathway database  pathway(s) (ii) genes, represented by an Entrez Gene ID, (iii) EC number(s). A separate file was provided to us by the curators of this database containing information about the metabolites including the five types of identifiers mentioned above.We downloaded from i.e., the 'BMP Signalling Pathway' and the 'MAP kinase cascade'. HumanCyc uses classes as substrates in some reactions  as a way of catering for enzymes with broad substrate specificity or enzymes for which the exact substrate specificity is unknown. For the metabolite comparison we retrieved the instances provided for each metabolite class. There are 563 metabolite classes that do not have instances, of which 192 have a metabolite identifier, e.g., a KEGG Compound ID. To retrieve the identifiers for these metabolite classes we used the Lisp API as they were not available in the exported flat files. Finally, the Entrez Gene ID is missing for 605 genes. If provided, the Ensembl Gene ID was mapped to an Entrez Gene ID, if available, via Ensembl BioMart (181 genes). If both gene identifiers were absent the UniProt ID was mapped to an Entrez Gene ID via the UniProt ID Mapping service (101 genes). After mapping an Entrez Gene ID was still missing for 323 genes and these were therefore not included in the comparison. For 82% of this set all three IDs mentioned are missing.We used Pathway Tools  to exporWe selected all human pathways from the metabolism category. For each pathway, we downloaded from the KEGG FTP site the human-specific KGML file, from which we retrieved the genes, and the KGML file containing the reference pathway linked to the EC numbers. Entries in both files are numbered, which we used to link genes to their associated EC numbers. In both files, the catalyzed reaction can be found. A single entry can contain more than one reaction, gene, and/or EC number. In that case, we assigned all genes and EC numbers contained in the entry to each reaction. Note that we cannot retrieve spontaneous reactions and reactions for which the human gene encoding the catalyst is unknown. Since KGML files only contain the main metabolites of a reaction, we retrieved the complete reaction from the flat 'reaction' file available on the FTP site. We used the 'H.sapiens.ent' file to get the Ensembl Gene IDs, and the 'compound' and 'glycan' files to extract ChEBI, PubChem Substance, and CAS IDs for metabolites.e.g., signaling and disease-related pathways  metabolic processes, excluding, Two EC numbers only mentioned in the comment field of the SBML file of BiGG were also taken into account. We left out the transport reactions that EHMN included in this pathway as KEGG does not contain any transport reactions in its metabolic network.We compared five metabolic pathway databases at different levels: genes, EC numbers, metabolites, reactions, and relations between these components. Below, we describe in detail how we compared each of these components.For the primary comparison at gene level we used Entrez Gene IDs, since it is the only gene identifier common to all five databases. BiGG, HumanCyc, and Reactome provide syntactic mechanisms for defining protein complexes, while EHMN and KEGG do not. Therefore, we did not make a distinction in the comparison between genes encoding a component of a catalyst or genes that encode a single protein catalyst.A fully specified EC number consists of four numbers separated by a period . The fire.g., on CAS ID, and between database B and C on, e.g., ChEBI ID then the metabolite was considered to match between database A and C as well. Instances of metabolite classes in HumanCyc and members of sets in Reactome were included in the comparison at metabolite level. To make the comparison as accurate as possible we did not match more generic metabolites, like alcohol or glucose, with more specific metabolites, like ethanol or α-D-glucose.Establishing identity between metabolites is a challenging task. For the comparison we, in general, used the KEGG Compound ID, which is in each database the most frequently provided metabolite identifier. However, KEGG Compound IDs are not available for each metabolite . The direction of a reaction was not taken into account in the comparison. The same reaction written in two directions was counted as one reaction. Compartment(s) were not considered as well. We again took the transitive closure for the reaction matches (see above).e.g., lipoamide-E, and the other is indicated as being unbound, e.g., lipoamide. The reactions were compared while not taking into account H+. In contrast to the comparison of the entire networks we removed neither the obsolete Entrez Gene IDs nor the gene for which the Entrez Gene was not available at all.In our detailed comparison of the TCA cycle, the following three pairs of metabolites were considered to match despite not having the same KEGG Compound ID: s-succinyldihydrolipoamide-E and s-succinyldihydrolipoamide; lipoamide-E and lipoamide; dihydrolipoamide-E and dihydrolipoamide. The only difference between these pairs is that one is the enzyme bound form of the metabolite, http://babelomics.bioinfo.cipf.es/) FatiGO uses the Fisher's exact test for 2 × 2 contingency tables to check for significant over-representation of GO biological process terms (levels 3-9) in one of the sets with respect to the other one. We used the default settings except that we set the filter for the minimum and maximum number of annotated IDs per term to 1 and 10000, respectively. GO terms were considered to be significantly over-represented if the p-values, adjusted for multiple testing by using Benjamini and Hochberg's method, were <0.01.Differences in GO biological process annotation between two lists of genes were assessed with the FatiGO functional enrichment module of the Babelomics suite  genes in the consensus on gene level versus the union of the remaining genes, WS2) all unique genes versus the union of the remaining genes, WS3-WS7): unique genes per database  versus the remaining genes contained in the union, WS8) genes contained in the majority of the databases versus the union of the remaining genes.Click here for fileConsensus reactions. Overview of the reactions part of the consensus of all five pathway databases . For each consensus reaction the corresponding EC numbers, genes (Entrez Gene IDs), and pathways are also given for each database.Click here for filePairwise comparison of the five databases on gene, EC, metabolite, and reaction level. Consensus between pairs of databases is calculated as in the main text: (|CDB1 ∩ CDB2|/|CDB1 ∪ CDB2|) × 100%, where C is the set of entities under consideration. Databases are compared on Entrez Gene IDs, EC numbers, metabolites, and reactions, which were not required to match on e-, H+ and/or H2O.Click here for fileTCA cycle as represented in each of the five metabolic pathway databases. Adapted version of Figure 2 in the main text for each of the metabolic pathway databases separately. Reactions occurring in the TCA cycle for the selected database are highlighted. Metabolites are represented by rectangles, genes by rounded rectangles, and EC numbers by parallelograms. Color indicates how many of the five databases include a specific entity. Color of an arrow indicates the number of databases that agree upon an entire reaction, i.e., all its metabolites (except H+ which was matched separately). 'x' denotes a missing EC number.Click here for fileTCA cycle as represented in each of the five metabolic pathway databases. Breakdown of the TCA cycle per database. WS1) Overview of all reactions, plus corresponding EC numbers and genes. WS2) Reactions of each database; matching reactions are aligned. For reactions that are not part of the TCA cycle consensus an explanation for the differences observed is given in column B . WS3) Metabolites of each database; matching metabolites are aligned. WS4) EC numbers of each database; matching EC numbers are aligned. WS5) Genes of each database; matching genes are aligned. In WS3-WS5 metabolites, EC numbers, and genes are matched across the entire TCA cycle.Click here for fileGrouping of pathways into categories. Overview of the manual grouping of pathways of each database into one of eleven categories, see Materials and Methods.Click here for fileIdentifier types for genes and metabolites present in each of the databases.Click here for fileMetabolite counts per database. For each of the five databases the percentage of metabolites without a chemical formula and the percentage of metabolites without an identifier is indicated. Furthermore, for each pathway database the percentage of metabolites linked to a particular metabolite database  is indicated. We also included the instances of metabolite classes for HumanCyc and members of sets for Reactome, see Materials and Methods.Click here for fileNames of metabolites without a match in any of the four other databases and without any of the five types of metabolite identifiers.Click here for fileMetabolite counts per database for the comparison of core metabolic processes. For the metabolites of the core metabolic processes in each of the five pathway databases the percentage of metabolites without a chemical formula and the percentage of metabolites without an identifier is indicated. Furthermore, for each pathway database the percentage of metabolites linked to a particular metabolite database  is indicated. We also included the instances of metabolite classes for HumanCyc and members of sets for Reactome, see Materials and Methods.Click here for fileTCA cycle: majority vote. Adapted version of Figure 2 in the main text when retaining only the entities that at least three out of five databases agree on. Reactions occurring in the majority are highlighted. Metabolites are represented by rectangles, genes by rounded rectangles, and EC numbers by parallelograms. Color indicates how many of the five databases include a specific entity. Color of an arrow indicates the number of databases that agree upon an entire reaction, i.e., all its metabolites (except H+ which was matched separately).Click here for fileOverview of all reactions and their matches. Overview of all reactions and their matches . Rows are colored according to the number of databases that agree on a reaction. For each reaction the corresponding EC numbers, genes (Entrez Gene IDs), and pathways are also given for each database.Click here for fileTop-level pathways from Reactome (not) considered in the comparison.Click here for fileInstantiating reactions containing sets of metabolites. Reactome contains reactions defined in terms of sets of metabolites. For five of such reactions the specific instantiations could not be derived automatically, this document gives two detailed examples.Click here for file