This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy) data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments (~10 12) and the vast amount of heterogeneous data available for designing experiments overwhelms the human ability to assimilate. The authors have developed an algorithm that utilizes a mathematical model of a reconstructed metabolic network integrated with a partially reconstructed TRN to identify the experiment designs with the highest potential of yielding the most new regulatory knowledge. The authors show that the produced experiment designs are similar to those a human expert would produce, and that the algorithm has a facility to incorporate any relevant data source to design such experiments.In recent years, the exploration of life has been bolstered through the advent of whole genome sequencing. This new data source significantly enables the reconstruction of genome-scale metabolic networks. After a metabolic reconstruction, it will be necessary to discover the genetic control mechanisms that operate within an organism. Transcriptional regulatory network (TRN) reconstruction is costly both in terms of time and money, so it is critical that the reconstruction efforts be made as efficient as possible. Experiments must be designed so that the most new regulatory knowledge is discovered in each experiment. The huge number of possible experiments (~10 As of January 2006, the TIGR Comprehensive Microbial Resource  containeEscherichia coli. The computational algorithm is intended to be applied iteratively, in conjunction with an experimental laboratory component that discovers direct TF–target gene interactions and the logic of the interactions –target gene regulatory connections, the (Boolean) logic of how TFs regulate target genes, and the environmental stimuli that modulate TF activity. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in ractions . The comEfficient reconstruction means minimizing the number of experimental iterations. Efficiency gains are realized from fewer iterations in two key ways: time spent researching the next most informative experiment design (which could conceivably require weeks to months); and, laboratory supply and personnel costs required to perform each iteration. Minimizing the total number of iterations necessitates maximizing the number of new regulatory interactions discovered in each iteration. Given the experimental protocol described above, maximal rule discovery in each iteration depends on a simultaneous maximization and minimization. Assuming for a moment that one had a KG in mind for which one wanted to discover the TRN, it would be necessary to identify two growth environments that each maximally activate the regulatory connections for the identified TFs. Simultaneously, one would want to minimize the connections identically activated in both environments to minimize redundant discoveries.This strategy is complicated by three facts. First, one would not have a complete TRN by which to judge whether the proposed growth environment shift–TF KG combination would be the one to yield the most new regulatory logic at the current stage of the reconstruction. Second, the best picture one could draw for the complete, real TRN would come from various data sources  that are characterized by uncertainty and noise. Third, it would not be an optimal strategy to first choose a KG and then the best growth environment shift for that KG. Since the growth environment determines how a TRN is activated, and each of the possible KGs would be associated with different patterns of regulatory activity depending on the environment, it is the combination of growth environment shift and TF KG that determines the maximum yield of new regulatory logic.The goal of designing a maximally efficient experimental strategy thus requires the resolution of three critical issues. First, how does one use criteria from various sources—with their attendant uncertainties, incompleteness, and noise—to infer as complete a picture as possible of the structure and logic of the TRN? Second, how does one utilize this incomplete picture to decide which growth environment shift–TF KG combination is most likely to yield the greatest amount of new regulatory knowledge? And third, how does one do this while steering away from previously discovered regulatory logic and towards new knowledge? After detailing the algorithm that addresses these issues in the next section, we describe how it has been applied and assessed in the Implementation section below.The logic of the algorithm, depicted in E. coli model in each environment from a library of minimal media growth environments. The observed gene expression states and the logic of interaction between TFs and the genes they regulate are summarized in a single “activity profile” for each simulation  network is utilized in both environments of a growth environment shift. It is created by combining the two activity profiles of the two growth environments, as illustrated in To prevent the algorithm from re-suggesting previously implemented experiment design(s), a record is kept of those cells of the shift activity profiles that were used to suggest the experiments that were previously implemented. In this step, the history mask is applied to each shift activity profile generated in Step 3 see .Enumerate all combinations of TFs for KGs in a range of sizes . See .k defined in Step 5, calculate its interconnectedness (NI), NI, in each growth environment shift Sij from Step 2.  gene. The interconnectedness for a KG depends upon the particular growth environment. For each KG k defined in Step 5, calculate its regulatory activity (NR), NR, in each growth environment shift Sij from Step 2.  and second NI. This ranking identifies the KG whose TFs' regulatory networks are both most widely influential and differently activated, and most densely interacting—and the two growth environments for which this is the case.With all growth environment shifts and all KGs enumerated, identify the growth shift–KG combination that maximizes first NAfter an experiment design has been implemented in the experimental phase of the iterative cycle, a set of new logic rules will have been generated and old rules will have been confirmed. Some of these rules will be for TF–target gene interactions that were in the shift activity profile, whereas others will correspond to newly discovered regulatory interactions. For those rules in the former set, “mark” their corresponding cells in the history mask.In practice, the algorithm does not produce just one experiment design. Since its purpose is to aid the experimentalist, it produces many designs in a structured format so that the experimentalist can factor in any additional criteria not considered by the algorithm. This aspect of the algorithm output is discussed further in the following Implementation section.The issue of algorithm validation presents a difficult situation, for there is no complete TRN in existence for any organism. We were able, though, to perform a limited retrospective analysis using two experimental TRN reconstruction iterations performed before the development and completion of this algorithm. For these two experiments, human research and intuition were used to choose the growth environment shifts and TF KOs. The algorithm and its development were in no way influenced by the human-made choice of experiments for the first two iterations.To date, only a limited amount of research has been reported towards the development of fictitious TRNs that would be complex, realistic, and logically consistent enough to test this algorithm. The logical consistency requirement is critical, for a “randomly-wired” TRN joined to a metabolic network would not be able to support simulated growth, and certainly not in a large number of growth environments. When such networks are successfully developed, they will be an additional test bed on which to evaluate the algorithm.The two experimental reconstruction iterations previously performed are given in Δfnr and ΔarcA and about 70 for ΔnarL), the ΔsoxS and ΔappY strains from the first iteration were found to be relatively uninformative; they produced 30 and four new regulatory rules, respectively.  These results suggest that more informative TF choices could probably have been made.Although the Δfnr and ΔarcA strains from the first iteration and the ΔnarL strain from the second iteration were found to be highly informative  were utilized, so all probability values in the activity profiles were unity. We considered KGs composed of between two and five TFs. For metabolic regulation in E. coli, for which iMC1010v1 currently includes 104 TFs, combinatorial calculation gives C + C + C + C = 96,748,106 unique KGs available for analysis. With 9.6 × 107 KGs and 21,121 legal growth shifts for E. coli, this gives roughly 2 × 1012 potential experiment designs.For the comparison we implemented the algorithm using iMC1010Inspection of the growth environment shifts in R values. The ranking of experiment designs in The last four experiment designs are of a qualitatively different nature. These last four are mainly aimed at elucidating nitrogen-related regulation, and so do not probe regulation states that are as different as seen with electron acceptor and glucose shifts. They do this by shifting from a growth environment containing two components, where at least one can also function as a nitrogen source, to an environment containing just one component that functions as both the carbon and nitrogen source. These experiments are expected to reveal less, and less globally acting, regulation—as implied by the lower NThe TFs suggested for the experiment designs in Both of the experiment designs of Just as the algorithm was run to retrospectively suggest experiments for the first iteration, we used the updated reconstruction resulting from the completed first iteration to retrospectively suggest experiment designs for the second iteration. We do not show an associated table for these results, but report that they are very similar to those of The rationale for Step 8 of the algorithm is based on the power law nature of TRNs ,18, whicTo grasp why the algorithm produces the experiment designs that it does and how it arrives at a rank ordering of their information potential, it is helpful to consider a result from previous work  directlyThe observed clustering in E. coli represents a special case because of the large body of scientific literature that exists for E. coli. For the algorithm presented here to be applicable to an organism, there must be some initial characterization of its TRN. Because the algorithm presented here has the capability to incorporate and utilize essentially any source of probabilistic data through the basic units that constitute the activity profiles, it can be flexibly applied in an organism-specific manner since certain types of data may be more readily or inexpensively attainable, depending upon the organism. To mention a few possibilities, initial characterization could be achieved through inference of TFs in raw genomic sequence [The demonstration of the algorithm with sequence , of TF–tsequence , mRNA exsequence –26, or gsequence , of TF bsequence ,28, and sequence .Systems biology is characterized by the integration of heterogeneous high-throughput data into a mathematical model and the subsequent use of this model to gain understanding of the cellular systems(s) under study in a way that would not be possible or feasible otherwise. Moreover, this is done iteratively, whereby new understanding is used to design subsequent experiments. Such a framework has been established and demonstrated in a four-step iterative procedure ,31, but R, of a KG will be required. And last, depending on the new approach, a function for defining and quantifying the regulatory connectedness, NI, of a KG will be needed.The algorithm has a modular aspect due to its ability to accommodate alternative transcriptional regulatory modeling strategies or different dynamical modeling frameworks altogether. In order to implement such modifications, four adaptations to the algorithm would be required. First, the basic unit will need to be able to capture the logical relationship between each TF–target gene pair, and do so in such a manner that external probabilistic data can be added. Second, a history mask will be needed that is appropriate for the new basic unit design. Third, a function for defining and quantifying the regulatory activity, NWe have presented an algorithm for systematically reconstructing a TRN with efficiency and human expert–like reasoning as prime considerations. Efficiency is based on time and cost; time is minimized through the algorithm-based experiment design process that would likely take a human expert weeks to months, and cost is minimized through the minimization of the number of laboratory experiments that need to be performed. The algorithm operates by deciding, given the current state of knowledge embodied in the TRN reconstruction, which experiment design—consisting of a group of TF KO strains and a growth environment shift—is most likely to yield the greatest number of new regulatory logic rules. The designs are equally applicable to overexpression studies. The algorithm has the ability to base its decisions on any data source that can assign a probability to the direct interaction of a TF and its regulatory targets and/or to the logical nature of its interactions. This aspect of the algorithm is significant, for it overcomes the finiteness inherent in a model and synthesizes a potentially vast amount of experimental and computational data that would not be possible by a human. We performed a limited retrospective comparison involving two previously performed TRN reconstruction iterations and found that the experiment designs that would have been suggested by the algorithm closely match the experiment designs that were chosen by human experts. This result illustrated our second goal of developing an algorithm with human expert–like reasoning ability. We expect this ability, when coupled with large amounts of probabilistic experimental and computational data, will significantly augment the limited assimilation ability of human experimentalists. Given the increasing number of organisms whose genomes have been sequenced and whose gene complement characterization is ever improving, we expect that this algorithm or others like or based on it will be necessary to efficiently uncover their transcriptional regulatory systems.E. coli (iMC1010v1) [The algorithm presented herein is demonstrated using the first available genome-scale reconstruction of the integrated transcriptional regulatory and metabolic network for C1010v1) . It accoDynamic batch culture growth simulations ,35 were Every growth simulation must occur in a defined nutritional environment. In this work, we simulated growth in all possible minimal media growth environments. In order to enumerate all such possible environments, we collected all environmental components that could have an effect in the model described above and categorized each one as a carbon, nitrogen, phosphate, sulfur, or electron acceptor source. Some components were placed in multiple categories . Additionally, each category contains “None.” All combinations consisting of one component from each category formed the library of minimal media.f, the target gene expression state g, and of the event of the TF binding to the target gene's promoter (TFbT), orThe central element in the algorithm is the basic unit. The purpose of a basic unit is to summarize the observed logical relationship between a single TF–target gene pair during a particular growth simulation. Implicit in this summary is also the expression states of the two genes. A basic unit is created for every TF–target gene pair between which a regulatory interaction is known or suspected to occur. Each basic unit is composed of four cells, which correspond to the four possible (Boolean) combinations of TF and target gene states . Each of these four cells contains a single numeric value, which is a probability value reflecting the degree to which it is believed that the physical and logical interactions actually occur in the organism in the given growth environment. Each probability value is computed as the joint probability of the TF activity state Basic units are parameterized using data from two qualitatively different sources. These two approaches are discussed in the following two sections and are illustrated in For TF–target pairs whose direct interaction and logic of interaction have been experimentally confirmed (and so are explicitly accounted for in the model), the equation for parameterizing the cells of a basic unit is derived from Equation 1 and Bayes rule:For regulatory connections between TF–target pairs that have not been experimentally confirmed but are suggested by data, computational and/or experimental data can be used to estimate the probability that (1) the TF represses/activates the target gene, (2) the TF is inactive/active in a particular growth environment, and/or (3) the TF binds to the promoter of the target. For integrating these varied types of data, Equation 2 is further expanded using Bayes rule to giveThe first term is the probability that the TF is an activator (corresponding to the basic unit cells for TF/Target states 0/0 and 1/1) or is a repressor (corresponding to the basic unit cells for TF/Target states 0/1 and 1/0). The second term is the probability that the TF is inactive or active in the environment E. The third term is the probability that the TF binds the promoter for the target in E.An “activity profile” summarizes the regulatory logic and gene expression states observed in a single growth simulation for the entire model. The procedure for creating an activity profile begins by recording the computed expression state of the genes in the model in every time step see . For thoWe used a “shift activity profile” to summarize the regulatory activity and gene expression states for a simulated shift between two growth environments. It is created from the two activity profiles corresponding to simulated growth in each of the two environments. The shift activity profile has the same dimensions as the activity profiles, and each of its cells has the larger of the two values observed in the corresponding cells of the two activity profiles. The larger value is chosen because it reflects the highest confidence of having observed the particular TF–target gene off/on state combination.The algorithm presented herein is intended to be applied repeatedly and iteratively to systematically discover the TRN of an organism. To prevent the algorithm from repeatedly suggesting the same experiments, it is necessary to record which criteria were used to suggest any experiments performed in earlier iterations. As is explained in Steps 6–8 of the Algorithm section, these criteria consist of particular cells of the shift activity profiles. Thus, we record in a “history mask” those cells of the shift activity profile that were the criteria for choosing experiment designs that were actually implemented in previous iterations. The history mask has the same dimensions as a shift activity profile, and any cell whose corresponding logic has been confirmed by an experiment that was used to suggest a design is “marked.” The history mask is applied to each shift activity profile; those shift activity profile cells whose corresponding history mask cells are marked are overwritten with the value 0.0.One of the fundamental outputs of the algorithm is the identification of groups of TFs for which to create single-deletion KO strains. Such groups of TFs we term “knockout groups,” or KGs. A KG is characterized by the number of TFs it contains and the identity of the TFs. For a total of m TFs, there are C unique KGs composed of n TFs.k, it is first necessary to quantify the interconnectedness between every pair p of TFs in the KG. For this, we define Itotal(p| Sij) as the sum of the interconnection weights for all interconnections between the two TFs of p in growth environments i and j. For the first type of interconnection, the largest probability for such a physical interaction in both growth environments is used as the interconnection weight. In the second interconnection type, there will be a direct TF–promoter interaction probability for each TF in each of the two growth environments. The product of the two environment-specific probabilities is the probability that both TFs regulate the gene in that environment. The maximum of these two interaction probabilities is used as the interconnection weight. The interconnectedness of KG k in growth environment Sij is then computed asIn regards to the TFs of a KG, we define two types of TF interconnections. In the first type, one TF directly regulates the other. In the second type, both TFs directly regulate a common (third) gene. To quantify the  interconnectedness of a KG f, we define R(f | Sij) as the sum over all connection weights between f and all of its inferred and/or known regulatory targets in both growth environments i and j. The connection weights are derived from the shift activity profiles in the following manner. First, all of the basic units for f in the shift activity profile for Sij are identified. For each basic unit that meets two conditions, its largest contained weight is added to R(f | Sij). The first condition states that the TF must be active in at least one of the environments i or j, because an experiment utilizing this growth environment shift with this TF KO strain would be uninformative otherwise. Specifically, this means that the last two cells of the basic unit, corresponding to TF “active”/target “not expressed” and TF “active”/target “expressed,” must both not be zero. The second condition states that the activity relationship between a TF and its target gene must have changed in the shift, otherwise the KO in the second environment may likely provide no new information. Specifically, the number of non-zero cells in the basic unit must be greater than one. The regulatory activity of KG k in growth environment Sij is then computed asWe define the regulatory activity of a TF to be the probabilistically-weighted count of genes that it directly regulates. This number will be growth environment dependent. To quantify the regulatory activity of a TF Figure S1The clusters are projected into three-dimensional space, allowing visualization of the “space” of transcriptional regulation and metabolic functional capabilities. The numbers in parentheses by each cluster in the key are the numbers of different activity profiles in the cluster. Comparison of the clusters shows that they can be distinguished by the available electron acceptor (indicated by the ellipses) and the carbon source, and to a lesser degree by the nitrogen source. The units of each axis are in bits, as given by the Hamming distance computed between computation-based activity profiles that are contained within the clusters.(190 KB DOC)Click here for additional data file.Table S1(32 KB DOC)Click here for additional data file.


Saccharomyces cerevisiae. We do this by quantifying correlations between changes in CEFs and changes in transcript levels for shifts in carbon source, i.e. between the fermentative carbon source glucose and nonfermentative carbon sources like ethanol, acetate, and lactate. The CEF analysis is based on a simple stoichiometric model that includes reactions of the central carbon metabolism and the amino acid metabolism.Control effective flux (CEF) of a reaction is the weighted sum of all fluxes through that reaction, derived from elementary flux modes (EFM) of a metabolic network. Change in CEFs under different environmental conditions has earlier been proven to be correlated with the corresponding changes in the transcriptome. Here we use this to investigate the degree of transcriptional regulation of fluxes in the metabolism of The effect of the carbon shift on the metabolic fluxes was investigated for both batch and chemostat cultures. For growth on glucose in batch (respiro-fermentative) cultures, EFMs with no by-product formation were removed from the analysis of the CEFs, whereas those including any by-products  were omitted in the analysis of growth on glucose in chemostat (respiratory) cultures. This resulted in improved correlations between CEF changes and transcript levels. A regression correlation coefficient of 0.60 was obtained between CEF changes and gene expression changes in the central carbon metabolism for the analysis of 5 different perturbations. Out of 45 data points there were no more than 6 data points deviating from the correlation. Additionally, up- or down-regulation of at least 75% of the genes were in qualitative agreement with the CEF changes for all perturbations studied.The analysis indicates that changes in carbon source are associated with a high degree of hierarchical regulation of metabolic fluxes in the central carbon metabolism as the change in fluxes are correlating directly with the change in transcript levels of genes encoding their corresponding enzymes. For amino acid biosynthesis there was, however, not found to exist a similar correlation, and this may point to either post-transcriptional and/or metabolic regulation, or be due to the absence of a direct perturbation on the amino acid pathways in these experiments. E. coli [S. cerevisiae [Metabolic fluxes are functions of metabolite levels, enzyme properties (affinities and specific activities), and the concentrations of enzymes. The latter are controlled at transcriptional, translational and/or post-translational levels, and is therefore referred to as hierarchical regulation ,2. In thE. coli  and S. crevisiae  metabolirevisiae .S. cerevisiae used in our previous study [In this work, the metabolic model for us study  was imprS. cerevisiae [81a) rather than including individual reactions responsible for their formation in the model. The metabolic model could therefore be kept at a manageable size. This processing of the model was particularly necessary to avoid combinatorial explosion in the number of elementary flux modes with an increase in the number of considered reactions [The set of experimental gene expression data for carbon source perturbations used in this study are summarized in Table revisiae  were direactions ,18. The eactions .S. cerevisiae (r81b), and this calculation led to noticeable differences in the resultant number of EFMs for the same carbon source  and ATP production flux for maintenance purposes (r51) (see Methods section for details). The resulting score, called the efficiency, was used as a weight for the fluxes passing through the corresponding EFM. As a result, control-effective flux of a reaction represents the sum of all weighted fluxes for this particular reaction that may participate in all the EFMs. Both efficiency and flexibility of the network is thereby reflected in the CEF score. The correlation between fold changes in CEFs and in mRNA levels were then investigated as depicted in Figure For each case Table , the ope2 = 0.60. An acceptable correlation  with a slope close to unity was possible by omitting at most 6 points for all the studied carbon shifts (Table 2 = 0.60). Plots of mRNA ratios versus CEF ratios for all cases studied are shown in Figure Table ts Table  indicatets Table , for whiThe correlation between mRNA ratios of the genes and corresponding CEF ratios were investigated for the genes belonging to central carbon metabolism and amino acid metabolism separately using the M81 model.Our results indicate that the change in fluxes in the central carbon metabolism is to a large extent controlled at the transcriptional level for carbon source perturbations Table . Howeverpfk12, fbp1 and pyc12, which displayed a weak correlation as a response to a diauxic shift in batch cultures [fbp1, pfk12) are responsible for the expression of enzymes involved in conversion between fructose-6-phosphate and fructose-diphosphate in reverse directions.fbp1 is known to be active during growth on ethanol whereas pfk12 is active during growth on glucose. Although their up or down regulation matches well with CEF predictions, there is no quantitative correlation. In other words, relatively insensitive responses at the level of gene expression may indicate an amplified transmission of the signal after the transcriptional level. However, investigation of another dataset [For each carbon source perturbation, a small set of genes whose mRNA ratios were weakly correlated with the CEF ratios were omitted; e.g. 3 genes, cultures   were to be omitted from the analysis to reach R2 0.60, indicating that these genes undergo other kinds of regulation (Table 2 = 0.60), with a slope several folds higher than unity (3.5). This indicates that the use of CEF approach which reflects different metabolic capabilities of the microorganism for growth on a given carbon source (as reflected in the EFMs) results in a better representation of the hierarchical control, i.e. control at the transcriptional level, on metabolic fluxes, compared with focusing on a single flux distribution predicted by MFA. This is also valid for the diauxic shift in batch cultures, where our CEF approach gives a 82% qualitative agreement . The other three are from different pathways, lsc12 from TCA cycle, fba1 and adh145 from glycolytic pathway. Unlike chemostat cultures, lack of transcriptional regulation through fluxes of reactions governed by two different genes, namely idp23 and mae1, is implied in the case of batch cultures for a glucose-acetate shift  (figure not shown).For the glucose/acetate shift in chemostat cultures, 3 of the 6 omitted genes belong to pentose phosphate pathway .For respiratory chemostat datasets, it is difficult to establish a correlation between the ratios of expression levels of amino acid genes and corresponding CEF ratios since numerical values of both experimental mRNA and model CEF ratios for these genes are very close to unity. Therefore, these genes do not have pronounced effect on the resultant overall correlation and slope.2 0.60. That is, there was a lack of correlation between the ratios of expression levels of amino acid pathway genes and corresponding CEF ratios. The observed lack of correlation for amino acid genes was also found for E. coli [For respiro-fermentative batch datasets, on average ten more data points had to be removed from the graph to get the predetermined correlation of R E. coli . There wS. cerevisiae under different growth conditions [In another study where we compared the significance of statistical change in both transcriptome and metabolome profiles of nditions , we conc2 = 0.60). This result suggests that the regulation behaviour can strongly depend on the genotype of the strain itself as suggested elsewhere [The fluxes of the central carbon metabolism are found to be mainly transcriptionally regulated in response to the carbon source perturbations studied here Table . In ordelsewhere ,26-29.The W303 strain is suggested to exhibit more fermentative behaviour than the SK1 strain in a glucose containing medium . The expIn order to further investigate the sensitivity of our approach, we analyzed data from a study on the influence of the transcriptional response to a glucose-lactate shift . That stIn the present study, a methodology was presented to investigate the hierarchical transmission of transcriptome changes to flux level using control effective fluxes. The high degree of correlation between the transcriptome and the fluxome obtained by the CEF approach shows that the major reason for lack of correlation reported so far was due to neglecting the flexibility of the network in operation. The detailed analysis using CEFs calculated based on the active EFMs in a particular growth type showed that fluxes in the central carbon metabolism are predominantly regulated at the transcriptional level in response to changes in carbon source. Regulation of amino acid metabolism seems to be mainly at the metabolic level; however, a definite conclusion can not be drawn since the analyzed perturbations were not directly related to this metabolism. This leads us to the hypothesis that if an applied perturbation has a direct effect on a metabolic pathway, then the genetic response of that pathway at the mRNA level is propagated into the fluxome, as demonstrated for the central carbon metabolism in this study.j is the index for EFMs, and i is the index for fluxes.EFMs were calculated using FluxAnalyzer 5.3 . CEF calThereby, EFMs which are equivalent in terms of cellular objectives were distinguished by assuming that the shorter EFMs were more efficient as reflected in the denominator of the formulation ,17. Thisvi) of a particular reaction (Control-effective flux (S. cerevisiae.Here we used the ratio of CEFs of reactions at two different conditions to correlate with the expression ratios of metabolic genes responsible for the enzymes of the reactions in 2) of 0.60 ensuring as well that the slope was between 0.80–1.25, and these points were omitted from the plot. The script written in MATLAB first identifies maximum possible regression coefficient value (R2) for an allowed number of simultaneous omissions. And, then, the number of omissions required to reach cut-off value (0.60) is determined. The correlation coefficient value of 0.60 was selected as the threshold for an acceptable degree of correlation since it corresponds to a Pearson correlation coefficient around 0.80, which is considered to be the lower limit for a good correlation [The methodology is depicted in Figure relation -35. More2, above 0.60 was assumed to be one of the criteria for identifying the type of regulation imposed on the fluxes for a particular carbon shift. If many points are to be omitted to reach the threshold, this means that: a) fluxes are not transcriptionally regulated, but regulated at the translational or post-translational level  or b) there is predominant metabolic regulation corresponding to substantial changes in the metabolite levels, or c) there is a combination of these two types of regulation. In addition, a second qualitative criterion, which is based on the number of points in the first (up-regulation) and third (down-regulation) quadrants of the plotted coordinate axis, was also used, as employed by others [The number of data point omissions required to keep the regression coefficient, R-8) even when no points were omitted from the dataset. Additionally, the selected cut-off value (R2 = 0.60) ensures that the retained dataset after omitting points to reach the cut-off is free of outliers in all cases (data not shown). In other words, the chosen cut-off is above all R2 values determined by our statistical outlier analysis. One disadvantage with statistical analysis is that it does not allow us to constrain the slope (between 0.80 and 1.25) in addition to constraining the correlation coefficient. Since statistical outlier analysis leads to the omission of fewer points than in the original analysis, it supports our conclusions.Alternatively, a statistical outlier analysis for the regressions can be employed by calculating confidence intervals on the residuals at a confidence level (e.g. 0.05), and thereby identifying outliers as those points whose residual confidence intervals do not contain zero. In this study, we preferred to use a single cut-off value for all cases analyzed. We think that this is reasonable since the correlations for all the cases was highly significant (p-value below ~10TÇ, BK, KÖÜ and JN designed the research. TÇ performed computational work. The manuscript was written by TÇ, BK, ZİÖ, KÖÜ and JN. All authors read and approved the final version.S. cerevisiaeMetabolic reaction set of Click here for file


Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology.Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model.We developed the Genome-based Modeling (GEM) System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site. Given the burgeoning wealth of knowledge in molecular biology, including the ever more rapidly accumulating quantitative high-throughput data, and with more than a hundred complete genomes now at hand, the grand challenge of what we might call "the post-genome era" is to obtain a system-level understanding of the dynamic behavior of the mechanisms of life. However, the dynamic behavior of biological systems, a result of the diverse nonlinear interactions of multiple molecular components possessing various properties, is complex and unintuitive. An integrative systems biology approach is therefore required to complement traditional reductionism, and computer simulation has proven to be an invaluable tool for system-level analysis . SimulatSeveral simulation studies of large-scale biological systems have been reported, but most are achieved by manual modeling of the cellular networks and simulation of network models by the use of approaches such as biochemical systems theory  and fluxAlthough quantitative modeling currently requires a thorough bottom-up approach with expert knowledge and a large amount of public kinetic information, a substantial part of qualitative modeling can be automated by integrating information from numerous databases. Although not intended for generating simulation models, several software tools for the reconstruction of the pathway database from the genome exist, including metaSHARK , IdentiCHere we describe a novel database-driven intelligent software system named the Genome-based Modeling (GEM) System, which automatically generates a cell-wide metabolic pathway simulation model suitable as a draft model to build upon for computer-based studies. The model is based on complete genome sequence data, and the software provides an environment that allows analysis of the system-level behavior of the organism of interest.A traditional modeling approach scales up from a basic model by adding new information by hand, but because our aim is to provide a draft model to reduce the labor-intensive qualitative modeling steps, we take an opposite, top-down approach of automated modeling. A rough image of the entire metabolic network is extracted from genome data and modeled on the basis of genetic information, and then more specific information is later added from expert knowledge with minimal manual work. Another merit to this approach is that the process starts from the genome sequence. In whole-cell modeling, integration of different biological databases is a challenging task, because the target field is broad and the scheme of each database differs. Moreover, the names of genes and proteins are often ambiguous and thus difficult to match. However, most databases contain a link to the genome sequence regardless of the subject, so by modeling from the genome as the starting point, it becomes possible to link a large amount of biological information by an automated method.The GEM System takes the complete genome database, both annotated and unannotated data, as input, and automatically goes through several steps to produce a simulation model of the organism in a flat-file format that can be readily converted to standard SBML suitable for simulation with various simulation software systems Figure  7,13]. ,13. 7,13The second step matches the genes to the product protein through a combined method of annotation reference, homology search, and orthology search. The system first checks for a direct external database link (db_xref) to SWISS-PROT and TrEMBL  in the aWhen the SWISS-PROT and TrEMBL entries match with the annotation and the homology search does not contain an EC number, there is an additional search in the ortholog entries in the same WIT Cluster  categoryThe GEM System holds information on enzymes extracted from the major enzyme and pathway databases ,24-27 anE. coli as examples. Firstly, the coding regions are identified by Glimmer unless an annotated complete genome is available, and the nucleotide sequences are translated into amino acid sequences. If database reference to SWISS-PROT is available in annotation, this reference is used to identify the gene and the EC number of enzyme coded by the gene, and if the reference is not available, BLAST similarity search matches the amino acid sequence to the corresponding SWISS-PROT entry. In this case, gene located on the complementary strand of position 1684755 to 1686401 is identified to be FUMA_ECOLI that is 4.2.1.2 in EC number with complete identify , and likewise, FUMB_ECOLI  is also identified to be 4.2.1.2. Similarly, two genes GLTB_ECOLI  and GLTD_ECOLI  are identified to be 1.4.1.13. Even when the homology search fails, orthology search identifies fumA and fumB genes to be both COG1838 , and the majority of genes belonging to the orthologous cluster is identified to be 4.2.1.2 in WIT Cluster. Likewise, gltB is identified to be COG0069 (Glutamate synthase domain 2) and gltD to be COG0493 (NADPH-dependent glutamate synthase beta chain and related oxidoreductases) and subsequently to be 1.4.1.13 with WIT Cluster. FumA is an aerobic isozyme and FumB is an anaerobic isozyme, whereas GltB and GltD are subunits of glutamate synthase. This is correctly identified by pathway check, because 4.2.1.2 occurs both in aerobic citrate cycle and in anaerobic reductive carboxylate cycle pathways, but 1.4.1.13 only occurs in Glutamate metabolism pathway . Reaction for 4.2.1.2 is a reversible reaction of (S)-malate = fumerate + H2O, and that of 1.4.1.13 is an irreversible reaction of 2 L-glutamate + NADP = L-glutamate + 2-oxoglutarate + NADPH + H. Taking account of the existence of isozymes (here we igonore gltF for convenience), the stoichiometric matrix is derived as follows  and glutamate synthase (1.4.1.13) in .2 and glwhere the first four rows represent two sets of reversible reaction of 4.2.1.2 and the fifth row represents the irreversible reaction of 1.4.1.13, and the columns represent (S)-malate, fumerate, H2O, L-glutamate, NADP, 2-oxoglutarate, NADPH, and H, respectively. For gap-filling, the reaction connecting 2-Hydroxy-ethyl-ThPP and Acetaldehyde, namely 4.1.1.1 is suggested from the connectivity of upstream reactions  and downstream reactions  in the glycolysis pathway.E. coli K12 MG1655 yielded the best numbers, with 968 reactions of 835 enzymes and 1195 metabolites in the computer-based model with an accuracy of 100% KEGG coverage, in other words, without any false negatives. Organisms that are not well understood are limited by this database driven approach, but even the model with lowest coverage, in this case Mycobacterium leprae, achieved over 90% coverage.Stoichiometric simulation models of all available complete annotated bacterial genomes have been generated by using the GEM System with the default parameters. Complete genome flatfiles were obtained from the EMBL database, and corresponding PTT files were used for COG annotation. Using these inputs, the BLAST searches were limited to the genes that did not contain direct external database link (db_xref) to SWISS-PROT or TrEMBL, and COG searches were replaced with data integration of PTT file with EMBL data. In this way, functional annotation process was optimized and therefore the calculation speed was remarkably fast, finishing the entire process in a few hours on a dual-processor PC server . Statistics describing the scale of computer-based cell models with over 500 enzymes are shown in Table E. coli specific entries of KEGG PATHWAY and SWISS-PROT data for pathways with unidentified genes or genes that have no EC number assigned are shown in Table E. coli, the EC coverage is obviously 100% for all pathways. However, there are several reactions that cannot be EC coded in some metabolic pathways, and a large fraction of reactions for pathways other than metabolism. Although the reactions that are not EC coded is not included in the stoichiometric model since they are beyond the purpose of this work to create metabolic pathway models, GEM System correctly identified all genes except for 3 cases in comparison with SWISS-PROT entries, so that the information can easily be incorporated for future applications. Three misidentified genes were actually correctly identified but the homology search identified them in different organisms or strains, namely, GPMA_ECOLI was identified to be GPMA_SHIFL (same gene in Shigella flexneri), ISPH_ECOLI and UPK_ECOLI were identified to be ISPH_ECO57 and UPPP_ECO57 (same gene in O157 strain of E. coli).Accuracy comparison with E. coli model was further compared with the genome-scale metabolic flux model of Reed et al. (iJR904) [E. coli specific entries of KEGG or SWISS-PROT are manually checked through EcoCyc and iJR904 as shown in Table E. coli, our automatic modeling system should keep the manual effort to a minimum. Obviously E. coli is the most well studied organism, and the manual procedure required for other organisms would be greater than 5%. However, most of the other models also yielded over 500 reactions at 90% or more KEGG coverage, and since the models are provided with pathway-wise accuracy table similar to Table The iJR904)  and the (iJR904) . EC numb  and theThe number of EC numbers extracted from the iJR904 model, 425, may seem small compared with the total number of reactions, 931. However, iJR904 contains 184 transporters that cannot be EC-coded, and it contains many enzymes without EC numbers that are EC-coded in KEGG database. Moreover, since many enzymes have multiple reactions, the total number of EC-coded reactions in iJR904 is 519. It is worth noting that iJR904 selects the pathways to include in the model, whereas the GEM System takes a greedy approach where every possible enzyme that is predicted to exist in a genome is included, regardless of the types of pathway the enzyme belongs to, leading to greater number of enzymes than in the iJR904 model. To summarize, the generated model has very high coverage (91~100%) compared to KEGG, EcoCyc, and iJR904, and the overall accuracy is also high, with false-positives of 6 entries (0.72%) and possible false-negatives of less than 43 entries (5.14%).Our web site  makes puet al.'s model. Although the generated models are draft models and thus still require expert curation to ensure the accuracy of simulations, manual involvement is minimized.We have developed the GEM System, automated software for the rapid construction of draft simulation models of cell-wide metabolic pathways from genome sequence information by integration of public biological databases. Automatic generation of the models is currently limited to metabolism in bacteria, and depends on the availability of EC numbers in public databases, but we have shown that qualitative models of the metabolic pathways of bacteria can be generated with low false positives and negatives, as validated by the comparison with KEGG, EcoCyc, and Reed There are, however, several limitations of this approach. Firstly, although EC numbers are generally effective for enzyme data representation for well known pathways, certain number of reactions have no EC number assigned, and therefore majority of the transporters are identified as genes but not included as reactions in GEM System, making a large fraction of the model different from iJR904. Secondly, some EC numbers are incomplete and therefore ambiguous, and some become quickly obsolete, being assigned to new EC numbers. This resulted in more than 40 inconsistent enzyme assignments in GEM System. Thirdly, since the GEM System identifies enzymes and the corresponding reactions based on the genome information, it cannot identify reactions that are experimentally observed but with no corresponding gene found. To overcome these problems, more general nomenclature for enzymes should be used in addition to the EC numbers and integrate necessary information that have no link to the gene sequences.The system generates a stoichiometric simulation model in SBML format, which is readily applicable to flux-based analyses on a number of simulation platforms. The stoichiometric models can be used for metabolic flux analyses by supplying experimental data for exchange fluxes as reported elsewhere ,34. One The GEM System can generate models automatically from public databases, but can also utilize private databases if such experimental data becomes available. Mining of high-throughput data by bioinformatics may facilitate the quantitative modeling step; for example, it should be possible to take advantage of recent progress in "metabolomics". Once genome-wide metabolome data becomes available via high-throughput techniques such as the capillary electrophoresis – electrospray ionization – mass spectrometry (CE-ESI-MS) method, metabolome data can be used to add unknown pathways, to supply the initial values of the metabolites, and to optimize kinetic parameters. Parameter fitting of time-series metabolite concentration data to general dynamic equations such as Generalized Mass Action is a possible substitution for accurate kinetic modeling, at least in the given time frame of the data set used for parameter optimization.Our next step is to model the gene expression layer, including transcription, translation, and degradation processes. The GEM System is a powerful platform for this purpose, in no small part because the genome-based approach enables a link to databases of different fields based on the nucleotide sequences already described. Because the GEM System has been based on a generic bioinformatics workbench, that is, the G-language Genome Analysis Environment , the sysThe GEM System facilitates systems biology research by prototyping a metabolic pathway simulation model from a genome. Given a complete genome, all modeling procedures are automated with configurable options, generating stoichiometric models in SBML format that are readily usable by cell simulators. In comparison with the KEGG organism-specific databases, the qualitative modeling step has high accuracy, with few false positives and negatives. More than 90 models generated from complete bacterial genomes are available for download online, with visualized pathway maps and gene lists.KA conceived the system, developed the software, carried out the validations, and drafted the manuscript. YY and KS participated in the design of the software. YN and MT supervised the work. All authors read and approved the final manuscript.


Mycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing. M. tuberculosis, a deadly human pathogen, owes many of its unique qualities to its thick, waxy coat, containing fatty acids called mycolic acids. Several front-line drugs used for treating tuberculosis indeed inhibit mycolic acid synthesis. Understanding the biochemical pathway that makes these compounds is therefore of great interest. Availability of the genome sequence and various computational methods enable us to study pathways as whole functional units, rather than having to infer from the study of individual proteins. Here, we present a comprehensive identification of the components of the mycolic acid pathway and represent it mathematically based on reaction stoichiometry. Such models are amenable to perturbations and simulations using flux balance analysis, allowing the study of pathways from a metabolic capacity perspective, and yielding information about reaction fluxes. The perturbations studied here are in silico gene knock-outs and drug effects, which led us to identify genes essential to the pathway and hence for survival of the pathogen. The results are in good agreement with essentiality determined through experimental genetics. Such essential genes can be good targets for drug design, especially when they do not have homologues in the human proteome. FBA followed by sequence analyses have resulted in identification of potential anti-tubercular drug targets. Genomics is rapidly changing the very foundation of several aspects of drug discovery research, one of them being a systems biology and bioinformatics approach for rational identification of drug targets. It is now possible to carry out a metabolic analysis of a system to gain insights into fundamental molecular mechanisms of several processes such as those that are critical for the survival of the pathogen. Here, we seek to apply such an approach to identify targets for designing anti-tubercular drugs. Despite the availability of several drugs and the Bacillus Calmette-Guérin vaccine, tuberculosis remains a major health concern worldwide, warranting identification of new drug targets for the design of more efficacious drugs.Mycobacterium tuberculosis [M. tuberculosis [The mycobacterial cell wall is distinctive and is associated with the pathogenicity of rculosis –4. The trculosis  covalentrculosis . The synrculosis . InhA  has been of great interest, and a large amount of biochemical and genetic information is available in the literature, in addition to the entire genome sequence of analysed . Construanalysed –18. Fluxanalysed ,20, whicanalysed ,22.Given the biological importance of mycolic acids, it would be useful to understand the behaviour of the pathway as a whole and of its individual components, both in a normal mycobacterial cell as well as upon perturbations, such as when a drug is acting upon it. We have therefore built a model of the MAP, represented it as a stoichiometric matrix, and performed FBA of this model, to gain insights into the critical steps of the pathway. Further, we have used the knowledge gained from these analyses for rational identification of putative drug targets and estimated their appropriateness by sequence analysis.The model of the MAP built here , contain16 to C18 and C24 to C26 acyl CoAs [16-acyl CoA to C58-acyl-ACP (meromycolate) [The FAS-I system, present predominantly in eukaryotes, is capable of de novo fatty acid synthesis, whereas the FAS-II system in mycobacteria, although similar to that in other bacteria, elongates the products of FAS-I, resulting in the production of meromycolates, key precursors of mycolic acids . The bascyl CoAs ,26. The ycolate) –31.desA1, desA2, and desA3 has been suggested based on sequence annotations [cis-unsaturated meromycolate chain further undergoes cyclo-propanation, processing for keto- and methoxy-mycolic acids [24-acyl CoA [Except for reactions 190–192, experimental data clearly indicating the involvement of the appropriate proteins are available in literature. However, clear identification of the proteins referred to as UNK1 and UNK2 is not yet available. Although no explicit experimental evidence is available for reactions 190–192, the involvement of otations  and indiotations , thus juic acids ,33–39 anacyl CoA ,40, to yM. tuberculosis H37Rv cell cultures clearly indicate that α-mycolate is the predominant mycolic acid and it comprises as much as 49% of the mycolates in the cell wall, whereas methoxy- and keto-mycolates are present in smaller quantities of 27% and 24%, respectively [Biochemical characterisation of mycolic acids in ectively . These dThe MAP system outlined here, though involving only 28 proteins, has 197 metabolites participating in 219 reactions with high interconnectivity, and thus benefits from a systematic FBA. The system considered here, though relatively small in comparison to genome-scale metabolic models previously studied by FBA , is stilv  only the most important mycolate is produced, and (b) the known ratios of different mycolates are fixed. We encode these two scenarios as two objective functions, andvmycolates represents the flux of a hypothetical reaction:where cis-methoxy-mycolate0.4926 α-mycolate + 0.2334 trans-methoxy-mycolate + 0.2117 cis-keto-mycolate+ 0.0327 trans-keto-mycolate → mycolate-biomass.+ 0.0297 cis-methoxy and trans-methoxy mycolates, as well as cis-keto and trans-keto mycolates indicated in the above equations are based on the biochemical data about α-, methoxy-, and keto-mycolates present in the cell wall in the ratio of 1.0:0.54:0.49, with the cis forms dominating the trans forms of the methoxy- and keto-mycolates, in a ratio of 1:0.14 [The coefficients of the fluxes of α-mycolates, f 1:0.14 . Productc2 captures an analogous scenario, where the total mycolate content can be considered as the biomass. We feel that c1 reflects the biological situation more closely than c2 for this analysis for the following reasons: (a) it has been reported [c2; (b) the objective function c2 directly precludes the possibility of the cell surviving in the absence of even a single mycolic acid; for any genotype, c2 necessitates that the mycolates be produced in an all-or-none fashion in the corresponding phenotype. On the other hand, c1 favours the production of the most important mycolate that can be produced under the given conditions ; and (c) reported biological data [The objective functions used in previous FBA studies (such as in ) have bereported  that thecal data  suggest c1 (Equation 2) accounts for the relative importance of the mycolates. The coefficients are indicative of the precise ranking order of the various mycolates, based on cell wall composition. On the other hand, the objective function in the second case, c2 (Equation 3), fixes the relative ratios of the mycolates based on the absolute values of the coefficients.However, to make the analysis comprehensive, both objective functions have been used independently and the results presented. In the first case, the objective function c1 and c2 yields flux distributions specifying the fluxes of all the internal reactions and the exchanges, as shown in 2, indicating their exit from or entry into the MAP system, respectively. A repetitive pattern is observed for reactions 65–172, which is comprehensible in view of the cyclic nature of the reactions involved in extension of the carbon chain in FAS-II. Malonyl CoA is an important metabolite, since it is required not only for the formation of C4-acyl-ACP, but also for each of the ten steps of chain elongation by the FAS-I system, where the chain length of the fatty acid component of mycolic acid grows from four carbons to 24 carbons  problem as in Equation 7 . The solution of the LP problem, using objective functions bons see . Besidesc1  in silico gene deletions and (b) inhibition by known drugs. Each of the 28 genes and hence its gene product was systematically deleted from the MAP model, one at a time, and its effect on the flux distribution was analysed . Figures 189 see C, the flmycolate D1, the fmycolate D2. A flaomponent ,45. Expentiality . In our ntiality  into thec2 as the objective function. Here too, each of the 28 genes and hence its product was systematically deleted from the MAP model, one at a time, and its effect on the flux distribution was analysed, as presented in c1 is illustrated by pcaA deletion. Upon deletion of pcaA, using c2, almost all of the reaction fluxes were seen to have dropped to zero, except for reactions 1–2 and their corresponding external metabolites . There were no significant changes in the flux profiles, and hence in the interpretations, as compared with those described above.Minimisation of metabolic adjustment (MOMA), using the methodology described by Segrè and co-workers , was alsc1, a good correlation was observed for 19 genes, no experimental data was available for four genes, and disagreement was seen for only five genes. High correlation with experimentally observed data about the essentiality of individual genes indicates the usefulness of our MAP model and its study using FBA. With c2, a good correlation was observed for 13 genes and disagreement was seen for 11 genes.Experimental data from systematic gene deletion studies using the transposon site hybridisation mutagenesis technique are available in the literature . Comparic1 and c2, it is apparent that the objective function c1 is able to reflect the biological situation better. c2 requires the production of all mycolates in a definite ratio under all conditions, which is not very appropriate, considering the fact that cells can survive even in the absence of one or more mycolates [c1.On analysing the results of the gene deletion studies obtained using ycolates  and thatc1) for accD3, fabG2, fabH, inhA, and desA3 are discussed below. We had identified malonyl CoA as an internal metabolite, in the absence of concrete experimental evidence of its being produced by other reactions in the cell. However, if indeed malonyl CoA is produced by the cell through some other means, then AccD3 would no longer be essential, agreeing with the experimental results. fabG2 has been reported as essential, while our analysis identified it as non-essential. Clearly, in our model, FabG2 can be substituted for by either FabG1 or FabG4. However, it may be possible that FabG2 could be responsible for catalysing some other critical reaction outside the MAP, which could contribute to its essentiality. fabH is a critical gene, whose product catalyses the important step that links FAS-I to FAS-II. Our sequence analysis studies also show that FabH has no homologues in the mycobacterial proteome. It is unclear as to why this is a non-essential gene in experimental studies. Similarly, inhA, identified as essential in our analysis but reported as non-essential in the experimental studies using transposon site hybridisation mutagenesis, is a well-known target for drugs such as isoniazid and ethionamide. While that may be the case for the conditions under which the transposon site hybridisation mutagenesis experiment was carried out, it is well-known that inhibition of InhA leads to a significant reduction in the growth of mycobacteria, making its inhibitors as front-line drugs. InhA is also known to be essential for mycolic acid synthesis [desA3 has been reported as non-essential but was identified as essential from our analysis. It is possible that our model may not have accounted for its exact physiological role, due to lack of information in literature.The possible reasons for the disagreements , although conclusive experimental proof is still awaited . The FBAWhile the usefulness of FBA for large systems with high network connectivities and redundancy is well established, its application for specific pathways, which can be considered as simpler systems, has not yet been well explored in the literature. The study reported here illustrates the usefulness of FBA even for individual pathways. The effects of the perturbations to a system even of this size, either at single points or at multiple points, are beyond unambiguous comprehension and thus benefit from systematic studies such as FBA to get meaningful results. Moreover, FBA provides a handle to systematically identify essential genes in the pathway, irrespective of the size of the system, in a systematic, efficient, and much simplified manner. An advantage of considering specific pathways individually is that the objective functions for optimisation can be better defined, with specific biological relevance, to generate hypotheses useful for designing molecular biology studies quite readily. It must be noted that at the present time our understanding of systems, and hence their reconstructions in general, is not sufficient to generate knowledge that can replace biochemical or genetic experiments. However, an in silico framework for predicting gene essentiality, while complementing experimental data where available, has the additional advantage of enabling studies under various environmental conditions such as during low nutrition or upon oxidative stress, or even in the presence of drugs, which are difficult to perform experimentally.M. tuberculosis H37Rv and human proteomes was therefore carried out for each of the identified targets and the results are summarised in Those genes that were classified as essential in the above analysis automatically form a first list of putative targets for anti-tubercular drugs, since their total inactivation results in loss of production of mycolic acids and hence the viability or the pathogenicity of the bacillus. However, it was reasoned that an ideal target should be essential not only in terms of the reaction it can catalyse, but also as the only protein coded by the genome that can perform the same task. Moreover, an ideal target should also have no recognisable homologue in the host system, which can in principle compete with the same drug, leading to unintended/adverse effects in the host system. Sequence analysis with the M. tuberculosis H37Rv proteome for seven proteins: FabH, AccD3, InhA, FabD, Fas, Pks13, and DesA3. Similarities greater than 50% using the BLOSUM62 substitution matrix with an e-value of less than 0.1 for a length greater than 70% of the query sequence were considered as close homologues (in both proteomes). For identifying more distant/fast-evolving homologues in the human proteome, homologues were identified with a second set of criteria, considering similarities greater than 30% using the BLOSUM62 substitution matrix with an e-value of less than 10−5 for greater than 70% of the query sequence length. None of the seven proteins have close homologues in the human proteome. A distant homologue was identified with the second set of criteria for FabD. Those proteins, which have no other homologues either in the mycobacterial proteome or in the human proteome, are therefore obvious potential drug targets. Proteins lacking homologues with multiple cut-offs can be considered targets with greater confidence. Identification of their presence only in the bacterial cell helps in the process of validation as useful drug targets. Front-line anti-tubercular drugs in current clinical practice, isoniazid and ethionamide, in fact turn out to be inhibitors of InhA [Of the 16 proteins classified as essential in  of InhA ,10, prevM. tuberculosis H37Rv proteome. Of these, DesA1 and DesA2 are homologues of each other, but it is not clear as yet whether they are required together or if they can substitute for one another. KasA and KasB too are homologues of each other and do not share similarities with any other mycobacterial protein. However, they share considerable sequence similarities with a hypothetical human protein FLJ20604. FadD32 too exhibits distant homology with six other proteins in the human proteome. Such mycobacterial proteins would also be interesting drug targets, provided such homologies are considered during drug design.Besides the seven targets mentioned above, DesA1, DesA2, and FadD32 do not have any close homologues in the human proteome but have homologues in the In conclusion, the work presented here provides a framework to rationally identify targets for use in tuberculosis drug design and provides a ready shortlist that can be experimentally tested. This also outlines a general strategy for analysis of microbial metabolism, providing insights into targets for drug design. Systems approaches are being increasingly applied for understanding the metabolic capabilities of organisms, which can be exploited for drug design. A major bottleneck in this process is the accuracy of the model, which requires expert curation of available literature. The MAP model presented here should be of value not only in drug design but also for understanding mycolic acid synthesis in general. The model can also be adapted to perform quantitative simulations when kinetic data become available, and it can be used as a framework for incorporating newer or alternate components when such information becomes available.M. tuberculosis H37Rv. Information about the FAS-I and FAS-II pathways in M. tuberculosis was available in the Kyoto Encyclopedia of Genes and Genomes database [http://genolist.pasteur.fr/TubercuList/) database, were therefore carefully analysed to fill the missing links and obtain a comprehensive model of the MAP, which contained information about 28 proteins, catalysing 219 reactions involving 197 metabolites, thus yielding a detailed pathway landscape. The model was encoded using SBML Level 2 Version 1 [Initially, the Kyoto Encyclopedia of Genes and Genomes database was explored to obtain information about various proteins that comprise the MAP in database , but appdatabase , on the ersion 1 .Sm × n, with every metabolite being represented by a row (m metabolites) and every reaction by a column (n reactions). The entries in each column correspond to the stoichiometric coefficients of the metabolites (negative for reactants and positive for products) for each reaction. The thi row of the matrix defines the participation of a particular metabolite across all metabolic reactions, and the thj column provides the stoichiometry of all metabolites in that reaction. Exchange fluxes were considered for metabolites that are typically exchanged with the environment  and those that are not produced by the system, or those that are produced in the system for use in other metabolic pathways. Such metabolites, referred to as external metabolites, will have corresponding exchange fluxes in the pathway. The other metabolites were regarded as internal, which are produced by the system and consumed within the system itself. The external metabolites in the MAP model were identified manually. A list of external metabolites has also been supplied as part of The set of reactions in the landscape were then mathematically represented as a stoichiometric matrix, vn × 1 to time derivatives of metabolite concentrations, xm × 1 asFBA ,53,54 haiv signifies the internal fluxes, ib represents the exchange fluxes in the system, and next is the number of external metabolites in the system. At steady-state,where S. Since m < n, the system is under-determined and may be solved for v fixing an optimisation criterion, following which, the system translates into an LP problem:Therefore, the required flux distribution belongs to the null space of c represents the objective function composition, in terms of the fluxes. Further, we can constrain:where http://www.mathworks.com/) was used for solving the LP problem. The “linprog” routine from the Optimization Toolbox was used, which uses a large-scale interior point algorithm.which necessitates all internal irreversible reactions to have a flux in the positive direction and allows exchange fluxes to be in either direction. Practically, a finite upper bound can be imposed, so that the problem does not become unbounded. This upper bound may also be decided based on the knowledge of cellular physiology. In our analysis, the upper bound was set as unity, which then effectively gives the relative ratios of the reaction fluxes. MATLAB  corresponding to the gene(s) , to zero. Effects of inhibitors of particular proteins were also studied in a similar way, by constraining the upper bounds of their fluxes to any defined fraction of the normal flux, corresponding to the extents of inhibition.MOMA ,47 is a Analysis of the effects of deletion of individual genes on the flux profiles of the five mycolates provided us a handle to define essential and non-essential genes. Those deletions that resulted in zero or near-zero fluxes of all the mycolates were considered as essential, and the rest were considered as non-essential.M. tuberculosis H37Rv and human proteomes, using BLOSUM62 substitution matrix. The BLAST outputs were parsed with home-grown scripts using BioPython modules, to identify homologues that satisfied various length and similarity criteria.Sequence analysis was carried out using BLAST  to adjudDataset S1M. tuberculosis.An SBML model for the MAP in (124 KB XML)Click here for additional data file.Dataset S2A flat file containing all the reactions in the MAP, along with the genes corresponding to proteins that catalyse the reactions. The external metabolites have also been indicated in the file.(19 KB TXT)Click here for additional data file.http://www.ebi.ac.uk/trembl/) accession number for hypothetical human protein FLJ20604 is Q9NWU1. Gene accession numbers are listed in The SwissPROT/TrEMBL (


Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis) with many more under development. All of these reconstructions are inherently incomplete with some functionalities missing due to the lack of experimental and/or homology information. A key challenge in the automated generation of genome-scale reconstructions is the elucidation of these gaps and the subsequent generation of hypotheses to bridge them.Currently, there exists tens of different microbial and eukaryotic metabolic reconstructions  reversing the directionality of one or more reactions in the existing model, b) adding reaction from another organism to provide functionality absent in the existing model, c) adding external transport mechanisms to allow for importation of metabolites in the existing model and d) restore flow by adding intracellular transport reactions in multi-compartment models. We demonstrate this procedure for the genome- scale reconstruction of We have proposed systematic methods to identify and fill gaps in genome-scale metabolic reconstructions. The identified gaps can be filled both by making modifications in the existing model and by adding missing reactions by reconciling multi-organism databases of reactions with existing genome-scale models. Computational results provide a list of hypotheses to be queried further and tested experimentally. Escherichia coli [Saccharomyces cerevisiae [The genome of several microorganisms has been completely sequenced and annotated in the past decade -4. This hia coli ) and mulrevisiae ). All ofrevisiae -11.Most of the aforementioned efforts are based on the use of sequence homology to uncover missing genes. Specifically, sequence homology is used to pinpoint genes in related species that have significant similarity with an unassigned ORF of the curated microorganism . Green eE. coli iJR904 model by pinpointing discrepancies between in silico model predictions and known in vivo growth phenotypes [Alternatively, a recent systems based computational approach identifies the location of missing metabolic functions in the enotypes . Subsequenotypes -11,17 orroot no-production  and root no-consumption metabolites  respectively. At steady-state conditions no flow can pass through them due to the incomplete connectivity with the rest of the network. Clearly, such pathologies are not physiologically relevant and thus must be caused by omission and/or errors in the model reconstruction process. Notably, the lack of flow in root no-production metabolites and root no-consumption metabolites is propagated downstream/upstream respectively giving rise to additional metabolites that cannot carry any flow. We refer to these metabolites that are indirectly prevented from carrying flow as downstream no-production  metabolites and upstream no-consumption metabolites  respectively. It is important to note that by restoring connectivity for the root problem metabolites all upstream/downstream metabolites are also automatically fixed. We concentrate on resolving only no-production metabolites in the case of cytosolic metabolites. In the case of non-cytosolic  metabolites, we identify mechanisms to resolve both no-production and no-consumption metabolites.Gaps in metabolic reconstructions are manifested as (i) metabolites which cannot be produced by any of the reactions or imported through any of the available uptake pathways in the model; or (ii) metabolites that are not consumed by any of the reactions in the network or exported based on any existing secretion pathways. We refer to these metabolites as Mechanism 1), (ii) adding new reactions from a multi-species database  or finally (iii) allowing for the direct importation of the problem metabolite restores flow into the no-production metabolite (Mechanism 3). For multi-compartment models,  . For non-cytosolic problem metabolites, present in internal compartments, direct importation from the extracellular space is not possible. Thus, their connectivity to the network is attempted to be restored based solely on reversing directionalities, adding external reactions or adding transport reactions with the cytosol. In both single and multi-compartment models, downstream/upstream no-production/consumption metabolites are automatically fixed by restoring connectivity to their corresponding root no-production/consumption metabolite. Alternatively, we identify connectivity restoration mechanisms for downstream problem metabolites in addition to the indirect mechanisms through the fixing of root problem metabolites.For single compartment metabolic networks (where we have only cytosolic metabolites), we postulate three separate mechanisms for fixing no-production metabolites  (MechanE. coli [S. cerevisiae[E. coli model (Mechanism 1) using two independent methods. First, we query the EcoCyc database [E. coli) available to thermodynamically characterize reactions in S. cerevisiae, we only employ the first method of testing for the latter model. We test if reactions are reversible by querying the MetaCyc database for corresponding information; if no such information is available, we check if the same reaction is reversible in other organisms.The proposed procedures are demonstrated on two widely used genome-scale metabolic models: E. coli  and S. cerevisiae. The resdatabase  about thdatabase . Values E. coli [Saccharomyces cerevisiae [Evidence for the presence of newly added reactions in the model is identified by checking for sequence similarity based on bidirectional BLAST scores searches . Next, wE. coli  and Saccrevisiae .E. coli genome-scale model [In this study, we first identify all no-production metabolites (both root and downstream) using the (GapFind) formulation using the most recent le model . All metle model  that arele model . Figure E. coli. As shown in Figure E. coli. Also, 19 out of these 26 require a single reaction directionality reversal while three  require the reversal of directionality of two reactions while four metabolites  require the reversal of three reactions respectively. To ensure that all 26 metabolites are produced in the network, (GapFill) identifies that the directionalities of at least twenty eight reactions have to be reversed. It is important to note that the identified reaction directionality reversals leading to the establishment of production routes for the problematic metabolites are to a large extent unique. Specifically, only two additional reaction reversal are identified when the (GapFill) model is re-solved (using integer cuts) to exhaustively identify all possible reaction reversals capable of resolving all no-production metabolites (See Table E. coli rather than adding new ones.We next proceed with the gap-filling procedure using the (GapFill) formulation. We first identify the metabolites for which production mechanisms are established by reversing directionalities of existing reactions in E. coli genome-scale model. As shown in Table The validity of the identified reaction directionality reversals is examined by employing two independent procedures as stated above. First we queried the identified reaction directionalities in the EcoCyc  databaseE. coli iJR904 model according to the procedure of Henry et al [As a second method of testing, we used the free energy change values ΔG of the identified reactions as approximated by Henry et al . It shoury et al   for multi-compartment models in the 50 model . All met50 model . Figure Saccharomyces cerevisiae model. Interestingly, 33 no-production metabolites are fixed by more than one of the above mechanisms. Finally, since (GapFill) does not identify production mechanisms for any of the remaining fifteen no-production metabolites, we enable their production by enabling transport reactions for them from the environment. As shown in Figure Figure Saccharomyces cerevisiae model. Also, as shown in Figure Connectivity restoration mechanisms for the remaining problem metabolites in the inner compartments are next identified using (GapFill). Figure As an example, we examine in detail the results obtained for the golgi apparatus. As shown in Figure Saccharomyces cerevisiae model. Ten out of the 33 reactions are reversible in other organisms according to information in the MetaCyc [As shown in Figures  MetaCyc  database MetaCyc .Saccharomyces cerevisiae model to enable the production of 47 problem metabolites (43 exclusively by adding reactions and 4 by a combination of adding missing reactions and other mechanisms). Of these, 18 are cytosolic metabolites  predicts potential activities of existing enzymes across compartments in the model and hence, it could be used effectively to aid in deciphering additional potential locations for activities of existing enzymes in a genome-scale metabolic reconstruction.Fifteen reactions are added to the existing s Figure  and 29 as Figure ) metabolparalogs . NotablySaccharomyces cerevisiae model is enabled by adding 22 intracellular transport reactions between the cytosol and the remaining compartments  fix 16 problem mitochondrial metabolites. Two of these transport reactions serve to transport gdp and gtp between the cytosol and the mitochondria. Also, the three problem metabolites in the vacuole are fixed by adding transport reactions to transport adp and atp from the cytosol and the vacuole. Finally the production of fifteen cytosolic metabolites is enabled by uptaking them directly from the extracellular space. We found no evidence in the literature to support or contradict these uptake mechanisms.The production of the 65 (33 exclusively and 32 in combination with other mechanisms) problem metabolites in the  Figures  and 11.  Figures  by addinE. coli and the multi-compartment model of Saccharomyces cerevisiae[E. coli, the (GapFind) procedure identified that about 10.4% of all metabolites were disjoint from the rest of metabolism. Flow through a majority of these metabolites was restored by reversing directionalities of existing reactions in the E. coli model. As many as 40% of them could not be fixed by any of the postulated mechanisms instead requiring their free uptake from the extracellular space. Flow through the remaining metabolites was restored by either exclusively adding missing reactions from MetaCyc or a combination of reversing directionalities of existing reactions in the model and adding missing reactions. Interestingly, for almost 50% of the reactions identified as reversible by (GapFill) had supporting evidence in the information obtained from the EcoCyc database. In the case of the multi-compartment model of Saccharomyces cerevisiae, (GapFind) identified that approximately 30% of all metabolites in the model were disconnected. Flow through 22% of them was restored by adding intracellular transport reactions in the model. Connectivity in the remaining 78% of the metabolites was restored by a combination of the mechanisms discussed in the case of E. coli. This left approximately 17.5% of all metabolites in the Saccharomyces cerevisiae model whose connectivity restoration cannot be accomplished though the postulated mechanisms.In this paper, we introduced two optimization based procedures, (GapFind) and (GapFill), to identify and fill gaps in genome-scale metabolic reconstructions. This was achieved by pinpointing metabolites can cannot be produced or consumed in the network using (GapFind) and then using (GapFill) to generate hypotheses that restored flow through these metabolites. These procedures were demonstrated on the single compartment model of erevisiae. When apin silico growth predictions using models of E. coli and S. cerevisiae are typically in good agreement with in vivo results [de novo metabolic reconstructions of less well curated microorganisms using software applications such as the hole filler algorithm in the Pathway Tools software [in silico predictions. We believe that for such automated reconstructions of less well studied microorganisms the utility of (GapFind) and (GapFill) will be even more pronounced. We have already applied some of these concepts in the reconstruction of the metabolic model of Mycoplasma genitalium currently underway in our group.For both models we found that a substantial percentage of the metabolites are disconnected from metabolism and cannot carry any flux. Despite these gaps  results ,25. Thissoftware  gaps in in silico predictions of growth phenotypes with experimental observations. An inherent limitation of (GapFill) is the reliance on a candidate database of reactions. One could envision extending (GapFill) to consider hypothetical reactions in the spirit of the methods proposed by Hatzimanikatis and coworkers [Clearly, the role of (GapFill) is to simply pinpoint a number of hypotheses which need to subsequently be tested. Using the basic set of our validation test we were able to relatively confidently validate or invalidate approximately 53% of the reversal of directionality of reactions that were predicted by (GapFill). An increase in experimental data, such as more information regarding ΔG values of reactions, which would help determine more accurately the thermodynamic feasibilities of transformations, will help increase this percentage. Also, use of recently developed computational procedures which combines available ΔG values with heuristic rules to elucidate thermodynamic constraints in genome-scale models  may alsooworkers . In concM metabolic reactions and N metabolites yield:First we describe a straightforward procedure to identify root no-production metabolites in the metabolic network under steady-state conditions. Under steady-state conditions, metabolite balances for a metabolic network comprised of bi is a parameter which signifies if metabolite i is uptaken (bi is negative) or secreted (bi is positive), vj is the flux in reaction j of the metabolic network, Sij is the stoichiometric coefficient of metabolite i in reaction j. A metabolite i is inferred to be a root no-production metabolite by simply scanning the ith column of the matrix containing the Sij elements and examining whether there exist any positive entries (for irreversible reactions) or non-zero (for reversible reactions) signifying production/uptake terms. If no such entries are present then there is neither a direct production nor importation route for the tested metabolite implying that it forms the root node of a no-production branch  is rewritten asIn single compartment models, the identification of downstream no-production metabolites cannot be accomplished by simply inspecting the entries of the NP (a subset of N) requires first the introduction of binary variables xnpi and wij which are defined as:The identification of the no-production metabolite set M and a set N of metabolites, the solution of the following optimization formulation (GapFind) identifies all downstream no-production metabolites in addition to the root no-production metabolites:Given a parent metabolic network consisting of a reaction set s.tR and IR comprise of reversible and irreversible reactions in the model respectively. Constraints (4) and (5) ensure that for each irreversible reaction that produces the metabolite i, the binary variable wij assumes a value of one only if the reaction produces atleast ε units of metabolite i . Similarly, constraints (6) and (7) ensure that for each reversible reaction in which the metabolite i participates, the binary variable wij assumes a value of one only if the reaction produces ε units of metabolite i. Constraint (8) then ensures that metabolite i must have at least one production route if xnpi = 1 by requiring a minimum of that atleast for one reaction j that can produce metabolite i, the value of a wij = 1 consequently implying that atleast ε units of the metabolite i is produced. Constraint (9) restricts the fluxes in the reactions present in the model organism between predetermined upper and lower bounds, UBj and LBj (LBj = 0 for irreversible reactions). The net positive balance assumption which led to (2) is incorporated in constraint (10). Finally the objective function (3) maximizes the sum of the binary variables xnpi over all metabolites ensuring the identification of all metabolites that have at least one production route given a set of available substrates. Therefore, if at the optimal solution xnpi is equal to zero then metabolite i is a no-production metabolite thus belonging to set NP. By using this procedure, all no-production metabolites are identified. Metabolites identified previously as root no-production are subsequently subtracted from the list to yield only the downstream no-production metabolites.In (GapFind), sets The (GapFind) procedure is slightly modified for multi-compartment models. As shown in Figure Note that a GAMS implementation of (GapFind) is available as an additional file [see Additional file Database comprised of candidate reactions while set Model is composed of the original genome-scale model reactions. It should be noted that if none of the above two/three mechanisms is capable of connecting the cytosolic no-production metabolite in single/multi-compartment models then an uptake reaction is arbitrarily added to the model to restore connectivity. However, if a non-cytosolic metabolite (in the case of multi-compartment models) present in an inner compartment cannot be fixed by any of the above mechanisms it is flagged as unfixable given the employed mechanisms.Upon the identification of all no-production metabolites in the model the next step involves filling these gaps using minimally the three mechanisms described earlier. We first explore whether reaction directionality reversal and/or addition of reactions from Metacyc  absent fwij defined previously, the proposed (GapFill) formulation relies on the binary variables yj defined as follows:In addition to the binary variable i* is posed as the following mixed integer linear programming problem (GapFill).For the case of single compartment models, the task of identifying the minimal set of additional reactions that enable the production of a no-production metabolite s.tDatabase so as to restore flow through metabolite i*. Constraints (14) and (15) are identical to (6) and (7). Constraint (16) ensures that these additions are subject to a minimum of δ units for the no-production metabolite i* being produced. Constraint set (17), as in (GapFind), allows for the free drain of all cytosolic metabolites while bounds on reactions present in the Model are imposed by constraint set (18). Constraint set (19) ensures that only those reactions from the Database that have non zero flow are added to the model. This formulation restores flow through no-production metabolites in single compartment models. For multi-compartment models, the (GapFill) formulation is modified. First gaps in the cytosol are filled using the mechanisms described earlier for single compartment models. Specifically, the (GapFill) formulation is modified by replacing constraint (17) with constraints (11) and (12) reflecting the fact that no net production term can be imposed for metabolites present with compartments incapable of communicating directly with the extracellular space. The solution of formulation (GapFill) once for each no-production metabolite i* identifies one mechanism at a time for resolving connectivity problems in the model. It should be noted that through the use of integer cuts [In (GapFill), the objective function (13) minimizes the number of added reactions from the ger cuts  multipletraints 1 and (15)CDM conceived the study. VSK, MSD and CDM provided the mathematical formulations for (GapFind) and (GapFill). VSK implemented (GapFind) and (GapFill). VSK, MSD and CDM drafted the manuscript. All authors read and approved the final version of the manuscript.E. coliTestable hypotheses for . The abbreviations used are from the E. coli iJR904 model.Click here for fileS. cerevisiaeTestable hypotheses for . The abbreviations used are from the S. cerevisiae iND750 model.Click here for file(GapFind). The (GapFind) formulation in the GAMS modeling language.Click here for file(GapFill). The (GapFill) formulation in the GAMS modeling language.Click here for file