NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code  is available at http://freescience.org/cs/NIBBS.Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use . However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search ( Genetic engineers often seek to modify the physical traits of microorganisms used in industrial processes in order to improve the efficiency of the overall process. The genes targeted for modification in these cases are typically identified by searching for genes whose presence in an organism is correlated with the presence of the physical trait. In the last few years, however, it has become understood that the physical traits of an organism are often the result of a coordinated set of interactions between multiple genes that make up a biological subsystem. This gives rise to a computational tractability problem, since the number of possible sets of genes is exponentially larger than the number of genes in an organism. Here, we use biological networks to limit the search space to sets of genes known to interact. The presence of the biological subsystems identified by this approach are shown to be significantly correlated to the presence of the phenotype. The results show that this framework can provide potential genetic targets for modifying the expression of a given phenotype. If a set of interacting metabolic reactions are important for expressing the target phenotype, then there likely exists an evolutionary pressure to conserve the set as a whole, or to have them co-present together, in multiple organisms This is the motivation behind network alignment and phylogenetic profiling approaches proposed to-date. The former However, neither network alignment nor phylogenetic profiling approaches can alone identify phenotype-related metabolic systems. Network alignment algorithms can identify metabolic systems present in all or most of a given set of organisms; such a set is typically small, e.g., less than 10 networks. However, even if the set of organisms exhibit a common phenotype, current network alignment approaches cannot distinguish phenotype-related metabolic systems from other common metabolic systems. Additionally, network alignment approaches would likely not identify a metabolic system if it is only common to a subset of the organisms being compared.Phylogenetic profiling approaches can identify phenotype-related enzymes that are specific to phenotype-expressing organisms. However, it is possible that enzymes that are part of a phenotype-related metabolic system will not be specific to phenotype-expressing organisms; therefore, these approaches will likely miss them. Additionally, it would be computationally intractable to compare the presence of every possible set of enzymes to the presence of the phenotype.earch ) algorithm .In order to address these and other limitations of existing methods, in this paper, we introduce the Network Instance-Based Biased Subgraph Search –is introduced to model phenotype-related metabolic systems in a set of metabolic networks derived for dozens or even hundreds of organisms. To assess NIBBS-Search 's accuracy, we first present the Maximally-Biased Subgraph Enumeration ( MBS-Enum ) method that exactly enumerates all MPBS s in a given set of networks; these subgraphs are then used for comparison with the NIBBS-Search results. To overcome MBS-Enum's computational complexity, NIBBS-Search heuristically approximates the set of MPBS s in the set of networks. NIBBS-Search runs orders of magnitude faster than MBS-Enum , while identifying with high sensitivity subgraphs that are statistically significant approximations of the set of MPBS s. Also, the NIBBS-Search -predicted systems contain known phenotype-related enzymes and pathways, including those that only exist in a subset of the phenotype-expressing organisms.A network structure–a phenotypically-biased edges from a metabolic map called the seeds and then expands each seed into a maximally, phenotypically-biased metabolic system. The method requires a set of organisms that express the phenotype of interest and ones that do not. A phenotype-profile vector is built for the organism set  expression, rTCA (reverse TCA) expression, hydrogen production, and acid-tolerance. We also looked at three sub-phenotypes of hydrogen production: dark fermentation, light fermentation, and bio-photolysis. The summary of the organisms used for each experiment is listed in For this study, we selected sets of completely sequenced genomes representative of both phenotype and phenotype non-expressing microorganisms. Genomic information for each organism within the dataset was obtained from the KEGG database and then incorporated into the NIBBS search algorithm. For each phenotype, an extensive literature review of published papers and microbial databases was conducted to identify representative microorganisms. Examples of microbial databases searched include the Department of Energy's (DOE's) Joint Genome Institute (JGI) and the National Center for Biotechnology Information (NCBI) database. To ensure our results captured biochemical processes related to the phenotype in question and not of a specific genus, each data set contained a diverse group of microorganisms representative of various taxa. The only exception is the acid-tolerant phenotype. In this case, the organism list consisted mainly of Firmicutes. The entire list of organisms used in the various experiments is available in earch algorithm to identify phenotype-related metabolic processes involved in the production of biological hydrogen. In addition to hydrogen production, we included the acid-tolerant phenotype to our studies to identify potential acid-tolerant response systems. For hydrogen producers, the presence of these systems is important in respect to acidogenesis. During acidogenesis, organic acids e.g., butyrate and acetate are produced, resulting in the lowering of pH within the environment In the following sections, we demonstrate the applicability of the NIBBS-Search algorithm's ability to predict phenotype-related metabolic processes , we selected the aerobic, anaerobic, TCA, and rTCA expressing phenotypes. The aerobic and anaerobic phenotypes are both well-characterized, thus we can validate through literature known phenotype-related biochemical processes. The TCA and rTCA expressing phenoyptes were selected to demonstrate the ability of the NIBBS-Search algorithm to identify phenotype-related enzymes within pathways that contain common enzymes. While these two studies do not directly relate to hydrogen production, they do serve to demonstrate the sensitivity of the algorithm.To further validate the NIBBS-SProduction of biological hydrogen is a potentially important sustainable technology for generation of alternative energy and fuels. The continuously growing number of naturally occurring microorganisms able to utilize various metabolic processes and organic substrates to generate hydrogen gas makes bio-hydrogen production a feasible option for development of bio-energy technologiesTo generate hydrogen gas, hydrogen producers utilize one of three main metabolic processes. They are light fermentation, dark fermentation of organic matter, and decomposition of water by photosynthesizing microorganisms (bio-photolysis) Clostridium acetobutylicum as a model organism for dark fermenting hydrogen producers, the key metabolic pathways for hydrogen production, shown in Using C. acetobutylicum. Whereas the T-Test only identified E.C. 2.3.1.8, all seven enzymes active in the butyrate pathway were found by the NIBBS method. The component enzymes for this pathway are butyryl-CoA dehydrogenase (E.C. 1.3.99.2), phosphate butyryltransferase (E.C. 2.3.1.19), butyrate kinase (E.C. 2.7.2.7), 3-hydroxybutyryl-CoA dehydrogenase (E.C. 1.1.1.157), acetyl-CoA C-acetyltransferase (E.C. 2.3.1.9), pyruvate formate lyase (E.C. 2.3.1.54), and crotonase (E.C. 4.2.1.55). Among these, only three were found by the T-Test.C. acetobutylicum utilizes a formate pathway, it is possible that C.acetobutylicum may contain genes encoding some enzymes necessary for formate production. Of the three key enzymes described in In addition to the above pathways, the formate pathway was also reviewed. A general overview of formate production is shown in Other enzymes identified using the NIBBS algorithm, include those involved in glycolysis and nitrogen fixation. In this study, a large number of enzymes involved in glycolysis were predicted as conserved across hydrogen producing organisms but not conserved across hydrogen non-producing organisms From analysis of the enzymes identified in the previous section, the NIBBS algorithm was able to identify the most relevant metabolic pathways for dark fermentative hydrogen production. While these pathways are important for hydrogen production, additional metabolic pathways present within organisms may also play an important role in impacting hydrogen yields.C. acetobutylicum  is vital for enhanced hydrogen production.C. acetobutylicum in both the phenotype and its sub-phenotype–hydrogen producing organisms and dark fermentative hydrogen producing organisms, respectively. The presence of this pathway in both categories suggests that fatty acid biosynthesis may play a key role in regulating metabolic routes for hydrogen formation, specifically, in dark fermentation. Findings in this study are similar to previous reports on the role of fatty acids in acetate and butyrate formation. In a study by Huang et al.Analysis of results showed that fatty acid biosynthesis was the highest ranking metabolic pathway for Purine Metabolism: Purines are nucleotide bases that can be found either in free forms or attached to ribose 5-phosphate to form nucleotides and nucleic acids Clostridium ljungdahliiC. ljungdahlii, purine compounds are degraded to form intermediates, such as glycine and betaine. These intermediates in turn are reduced, resulting in acetate formation by the enzyme acetate kinase C. acetobutylicum, we can predict that purine metabolism (degradation and synthesis) plays a minor role in generation of acetate in dark fermentative bacteria.Depending on the respiration requirement of the organisms , the degradation pathway used by microorganisms will vary. In our study, we selected dark fermentative hydrogen producers and within this phenotype, we include both facultative anaerobic and anaerobic bacteria. As such, an extensive review of metabolic reactions is necessary to determine which degradation pathways, if any are utilized. However, based on the high ranking of this pathway in our study for Arginine and Proline Metabolism: L-Proline and L-arginine are two amino acids commonly found within both eukaryotic and prokaryotic organisms Escherichia coli, proline and proline betaine have been linked to increased osmotolerance and protection in cells L-arginine is also an important precursor in nitrogen metabolism and protein synthesis in bacterial cells C. acetobutylicum with respect to both hydrogen producing organisms and the sub-phenotype, dark fermentative hydrogen production. In addition to identifying arginine and proline metabolism in individual species, evaluation of hydrogen production related enzymes shows that this pathway is significant and likely related to hydrogen production.In this study, arginine and proline metabolism was identified as a potentially important pathway for Cysteine and Methionine Metabolism: Methionine is a sulfur-containing amino acid that is used for biosynthesis of cysteine Clostridium, are capable of utilizing nitrogenase enzymes C. acetobutylicum.C. acetobutylicum and in the set of organisms expressing the phenotype hydrogen production , a known enzyme involved in acid-resistance in some microorganisms including Clostridium perfringens, a known hydrogen producer. In Escherichia coli, C. perfringens, and some Lactobacilli the internal pH can be neutralized by a decarboxylase system–glutamate and arginine decarboxylase Lactobacilli, glutamate decarboxylase converts glutamate to Analysis of the predicted enzymes for Lactobacillus plantarum JDM1, Lactobacillus plantarum WCFS1, and Clostridium perfringens ATCC 13124. Prediction of glutamate decarboxylase by NIBBS was due to the presence of the enzyme in a small subset of organisms within our dataset and the absence of the enzyme in phenotype non-expressing organisms. Based on the absence of glutamate decarboxylase in many of our organisms, including hydrogen producing C. acetobutylicum and C. beijerinckii, we can classify glutamate decarboxylase as not specific for, but rather related to acid-tolerance. The presence in C. perfringens and absence within other Clostridium species do not necessarily indicate that C. acetobutylicum is not capable of similar mechanisms. In fact, incorporation of a decarboxylase system similar to that of C. perfringens and L. plantarum into hydrogen producers, such as C. acetobutylicum may be necessary to maintain hydrogen production and acidogenesis.Glutamate decarboxylase was only present in three of our ten acid-tolerant organisms . They arFirmicutes. As such, results reflect metabolic pathways present to a small group of bacteria capable of acid-tolerance rather than across a diverse set of organisms capable of expressing the acid-tolerant phenotype.Metabolic pathways related to expression of acid-tolerance, vary across organisms and sub-sets of organisms, as shown by analysis of phenotype-related enzymes. This is particularly true between Gram negative and Gram positive organisms earch algorithm, seven enriched pathways (Using the NIBBS-Slue0.05)  were idePurine Metabolism: NIBBS-Search algorithm predicted purine metabolism as a potentially significant pathway for organisms expressing acid-tolerance.deoB and guaA, that encode for phosphopentomutase and GMP synthase, respectively, in assisting with acid-tolerance Lactobacillus species, organisms can utilize nucleobases, such as guanine and adenine, generated during DNA and RNA degradation to synthesize nucleotides Purine metabolism encompasses biosynthesis, degradation, and salvage of purines within microorganisms. Together these pathways are necessary for survival and growth of organisms. Purines, along with pyrimidines, make-up vital components of nucleic acids , and are involved in synthesis of many vitamins and coenzymes  Arginine and Proline Metabolism: In hydrogen producing organisms, decarboxylation and deamination of amino acids  have been linked to osmotolerance and protection of cells in the presence of environmental stress Lactobacilli. In this process, arginine is decarboxylated, then the decarboxylated product is removed and another arginine product is transported into the cell C. acetobutylicum. However, key enzymes involved in these pathways for C. perfringens were shown as present, thus suggesting these pathways may be utilized by this organism in response to acid stress. For the first pathway, the NIBBS algorithm was only able to predict the presence of glutamate decarboxylase (E.C.4.1.1.15) and did not identify arginine decarboxylase. This suggests that C. perfringens may not utilize this route for acid-tolerance.From the predicted NIBBS results, the presence of the ADI or decarboxylation pathways was not predicted in our model organism, C. pefringens may utilize this enzyme in arginine metabolism in response to acid stress. While it does not appear that C. acetobutylicum utilizes these two pathways, there have been reports suggesting that it is capable of utilizing similar mechanisms through activation of homologous genes C. pefringens can be used to provide clues to expression of acid-tolerance.For the ADI pathway, only two of the three essential enzymes associated with this pathway were identified. They are arginine deiminase (E.C. 3.5.3.6) and ornithine transcarbamylase (E.C. 2.1.3.3). In addition, we noted the presence of agmatine deiminase (E.C 3.5.3.12), an enzyme responsible for conversion of agmatine to N-carbamoylputrescine and ammonia. Based on the presence of agmatine deiminase, we predict that Two experiments were performed to measure the ability of the NIBBS algorithm to identify enzymes and potential subpathways related to organisms capable of expressing specific pathways. In order to assess the ability of both approaches to identify phenotype-related enzymes, 36 aerobic organisms and 36 anaerobic organisms were selected. Analysis of the NIBBS enzymes shows 86% and 75% recall, respectively, when one or the other are used as positive instances. The results showed that NIBBS enzymes for aerobic respiration contained 261 enzymes and for anaerobic respiration contained 93 enzymes, while the Student's T-Test identified 131 enzymes for aerobic respiration and 64 enzymes for anaerobic respiration.earch shows that they are typically present in most of the phenotype-expressing and non-expressing organisms. The reason some enzymes are identified as phenotype-related by the statistical analysis is due to the fact that they typically have a higher copy number in phenotype-expressing organisms. Since NIBBS-Search uses binary data , these enzymes are not identified by NIBBS-Search as biased. In addition, because the NIBBS algorithm does not rely on the enzyme distributions across entire sets of organisms, it is capable of identifying subgroups of organisms among the list of given species. As such, it is not expected that NIBBS will contain identical sets of enzymes as those identified with the Student's T-Test approach.Examination of the enzymes found by the Student's T-Test but missed by NIBBS-SEvaluation of phenotype-related enzymes identified for aerobic organisms show that the NIBBS algorithm was able to discover a small set of known enzymes associated with pathways commonly associated with the phenotypes of aerobic and autotrophic carbon fixation. In Other enzymes identified as phenotype-related are present due to phenotype associations with sub-groups of organisms in our dataset. These include organisms with similar fatty acid metabolism, amino acid metabolism, and photosynthetic organisms. Enzymes predicted as related to anaerobic organisms included 2-oxoglutarate synthase and ATP-dependent citrate lyase, which are related to the reductive TCA (rTCA) cycle . The enzearch algorithm to predict phenotype-related enzymes through the prediction of phenotype-related metabolic systems, the algorithm is capable of distinguishing between pathways that contain common enzymes. To demonstrate this feature of NIBBS-Search, two experiments were conducted comparing the two well-characterized metabolic networks, tricarboxylic acid (TCA) cycle and the reverse TCA (rTCA) cycle.Due to the ability of the NIBBS-SSets of organisms known to utilize the TCA and rTCA cycle were selected and analyzed . SelectiUsing sixteen organisms that utilize the TCA cycle and six organisms that utilize the rTCA cycle, NIBBS algorithm was able to identify all but one TCA enzyme, malate dehydrogenase (EC 1.1.1.37), among the top ranking systems . Malate Similar results are obtained in the rTCA experiment , when rTIn the rTCA experiment, systems identified by NIBBS include two enzymes, citrate synthase (EC 2.3.3.1) and succinate dehydrogenase (EC 1.3.99.1) that are typically associated with the TCA pathway The presence of these TCA-related enzymes in rTCA related systems does not indicate an additional functionality, but rather that succinate dehydrogenase found by KEGG might actually be acting as a fumarate reductase. Being that the two enzymes are evolutionarily related to each other, fumarate reductase and succinate dehydrogenase are difficult to distinguish based on sequence alone To assess the ability of NIBBS algorithm to identity phenotype-related enzymes and pathways, we compared NIBB's seed generation to the Mutual Information method described by Pyruvate Synthase and Formate C-acetyltransferase: Pyruvate synthase, which is also known as pyruvate: ferredoxin oxidoreductase (PFOR), is the key enzyme for acetyl-CoA formation in many sulfate-reducing, methanogenic, dark fermentative hydrogen-producing bacteria C. acetobutylicum, acetyl-CoA pathway is the main route for acetate and hydrogen production. In this pathway, glucose or other sugar molecule is transformed through a series of reactions to generate pyruvate. Pyruvate generated can then be converted to acetyl-CoA by PFOR for synthesis of acetate In our study, PFOR was identified by NIBBS as a hydrogen-related enzyme but was missed by Mutual Information. Lack of identification by Mutual Information may be due partly to the fact that two different routes can be utilized by hydrogen producing bacteria. In our experiment, hydrogen producing bacteria were representative of both anaerobic and facultative anaerobic respiration. As such, the presence of multiple phenotypes being expressed may have resulted in inaccuracies by Mutual Information. However, the NIBBS algorithm was able to distinguish the importance of these two enzymes, thus predicting the role of PFOR and PFL in acetate and hydrogen formation.Lactate Dehydrogenase: While identification of enzymes and pathways involved in production of hydrogen is important, one must also understand which pathways may greatly reduce hydrogen yields. One such pathway is the formation of lactate from pyruvate by the enzyme lactate dehydrogenase seeds using its seed generation algorithm, which are then expanded to phenotype-biased metabolic systems. However, NIBBS can also take as input, seeds obtained using other methods like literature search and statistical tests  T-Test is another statistical method that can identify phenotype-related enzymes. Each enzyme's phylogenetic profile is used to calculate the earch can truely identify phenotype-related subsystems, then it should be able to identify the subsystems related to these metabolic pathways as significant. In fact, we found that all the 13 pathways were significantly present in the discovered subsystems.In this method we desribe an experiment that evaluates the accuracy of our method using some specialized metabolic pathway information. For this experiment we chose a group of 13 specialized metabolic pathways  to act amaximum bias (maximum ), and (3) the maximum size of the seed set  the percentage of the positive organisms the resulting subsystem (expanded seed set) should be be present .Even using the efficient MULE algorithm, the runtime of MBS-Enum is intractable for the large-scale networks in this experiment. earch and the complete set of maximally-biased subgraphs produced by MBS-Enum  number To satisfy these requirements, we adapt the method of modeling organism-specific networks introduced by Koyutürk organism-specific networks describe the metabolic network that exists in a given organism. Specifically, every edge in such a network is associated with an EC number of the enzyme that is known or predicted to be present in the organism. We obtain the organism-specific networks from the reference maps by retaining only those reactions that are catalyzed by an enzyme present in the organism, i.e, by retaining only those edges whose edge labels represent enzymes present in the organism.While metabolic reference maps capture every known, biochemically feasible metabolic process, A subgraph is said to exist in an organism-specific network, if the edge lables, i.e., the enzymes are present in the organism. Thus, we do not solve any subgraph isomorphism problem. In addition, with this model, the set of all possible metabolic systems can be defined as the set of subgraphs of the reference map. Moreover, only connected subgraphs need to be considered, because metabolic systems are defined as a series of metabolic reactions, where the product metabolites of one reaction are used as the substrate metabolites of the next reaction.The introduced To calculate the Given the number of organism-specific networks , the number of positive networks , the number of networks that the system exists in , and the number of positive networks the system exists in , the phenotype-bias metric  is defined according to the cumulative hypergeometic probability distribution:Because In order to predict phenotype-related metabolic systems, this approach searches the set of organism-specific networks for maximally-biased subgraphs.A maximally-biased subgraph is a subgraph that satisfies the following two criteria:It has no subgraph whose  is less than its own's.It has no supergraph whose  is less than or equal to its own's.The first criterion comes from the assumption that the entire phenotype-related system is at least as biased as its smaller part. The second criteria is the one that makes the reported subgraphs maximal. According to the second criteria, only allowing those subgraphs that have no larger subgraph with equal or smaller bias are reported.earch ) algorithms that respectively enumerate the exact and the approximate set of maximally-biased subgraphs as models of phenotype-related metabolic systems. While being exact, MBS-Enum becomes computationally intractable for genome-scale networks. In contrast, NIBBS-Search is a fast heuristic, suitable for hundreds of genome-scale networks; yet, it produces a statistically close approximation of the full set when empirically tested against MBS-Enum results generated for small-scale networks.This section presents the Maximally-Biased Subgraph Enumeration (MBS-Enum) and the Network Instance Based Biased Subgraph Search  enumerates all maximally-biased subgraphs for a set of network instances MBS-Enum enumerates all maximally-biased subgraphs if and only if every maximally-biased subgraph is also a maximal frequent subgraph for some threshold If If where Theorem:A maximally-biased subgraph  for given positive  and negative  sets of networks is a maximal frequent subgraph for the thresholdProof: Let earch algorithm is given in Algorithm 1 in A general overview of the NIBBS-Sseed sets correspond to significant subsets of edges from the network map; they differentiate between common subgraphs that model phenotype-related systems and those that model phenotype un-related systems, and they improve the NIBBS-Search efficiency by determining the subset of organisms that are predicted to contain the entire phenotype-related system. The motivation behind seed set generation stems from the following observation. The phylogenetic profile of a phenotype-related metabolic system, such as the tricarboxylic acid (TCA) and reverse TCA (rTCA) cycle is often the same as the phylogenetic profile of a small subset of its constituent enzymes , which are the edges whose addition to the seed set decreases its The procedure implemented in the NIBBS-STwo methods of selecting the candidate edges are defined. The first ensures that the seed set forms a connected subgraph. The second does not require that the seed set be connected but ensures that the seed set be part of a connected subgraph after the expansion process. The first method is achieved by only considering edges that are adjacent with one of the edges currently in the seed set. The second method considers any edge in the network map as a candidate edge as long as the two edges are connected after the expansion process. To ensure that the two edges are connected, the method determines if there exists a path between the edge and one of the edges in the seed set that is present in every positive network instance that the new seed set would be present in.The user chooses a threshold earch algorithm expands the seed sets. To ensure that the expansion edges belong to the same metabolic system as the seed edges, the expansion process requires that the expansion edges be present in most if not all of the metabolic networks of phenotype-expressing organisms that also contain the seed edges. The addition of expansion edges to a seed set to form the subgraphs output by the algorithm is called the seed expansion process. During the process, an expansion edge is selected from a set of candidate edges. These candidate edges are determined by two criteria checked in the GenerateExpansionCandidates function (Line 1 and Line 5):The seed set of edges is unlikely to represent the entire phenotype-related metabolic system. Seed sets are typically small, containing between one and five edges and, depending on the method used to construct them, may form a disconnected subgraph. A metabolic system is likely to form a connected subgraph in a metabolic network containing many more edges They are adjacent to a seed edge or an expansion edge is already in the edge set.If added to the current edge set, the resulting edge set will be present in at least The first criterion ensures that the final edge set will form a connected subgraph. The second criterion allows for noise in the data, while requiring that the final edge set still be present in most if not all of the same positive network instances as the seed set. The algorithm for expanding the seed set to form the final edge sets is given in Algorithm 3 in The order in which candidate edges are added to the edge set will determine the make-up of the output edge set unless earch run uses the maximum seed set size earch with those parameters identifies subgraphs that most closely approximate maximally-biased subgraphs.Every NIBBS-SClostridium acetobutylicum as follows. The edges in all the subsystems are combined into one list reference pathway map form the population organism-specific pathway map of The hypergeometric test is utilized to identify the pathways enriched by the metabolic subsystems identified by NIBBS for the hydrogen production, dark fermentation, and acid tolerance phenotypes. The enriched pathways are identified for Table S1Organisms used in the experiments. This file consists of information regarding the organisms utilized in the various experiments.(XLS)Click here for additional data file.Table S2earch results for the hydrogen production phenotype.NIBBS-S This file consists of NIBBS-Search results for using hydrogren production as target phenotype.(TXT)Click here for additional data file.Table S3earch results for the dark fermentation, hydrogen production phenotype versus bio-photolysis, hydrogen production phenotype.NIBBS-S This file consists for NIBBS-Search results when using dark fermentation as target phenotype and bio-photolysis organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S4earch results for the dark fermentation, hydrogen production phenotype versus light fermentation, hydrogen production phenotype.NIBBS-S This file consists for NIBBS-Search results when using dark fermentation as target phenotype and light fermentation organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S5earch results for the dark fermentation, hydrogen production phenotype versus hydrogen non-production organisms.NIBBS-S This file consists for NIBBS-Search results when using dark fermentation as target phenotype and hydrogen non-producing organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S6earch results for the light fermentation, hydrogen production phenotype versus bio-photolysis, hydrogen production phenotype.NIBBS-S This file consists for NIBBS-Search results when using light fermentation as target phenotype and bio-photolysis organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S7earch results for the light fermentation, hydrogen production phenotype versus dark fermentation, hydrogen production phenotype.NIBBS-S This file consists for NIBBS-Search results when using light fermentation as target phenotype and dark fermentation organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S8earch results for the light fermentation, hydrogen production phenotype versus hydrogen non-production organisms.NIBBS-S This file consists for NIBBS-Search results when using light fermentation as target phenotype and hydrogen non-producing organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S9earch results for the bio-photolysis, hydrogen production phenotype versus dark-fermentation, hydrogen production phenotype.NIBBS-S This file consists for NIBBS-Search results when using bio-photolysis as target phenotype and dark fermentation organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S10earch results for the bio-photolysis, hydrogen production phenotype versus light fermentation, hydrogen production phenotype.NIBBS-S This file consists for NIBBS-Search results when using bio-photolysis as target phenotype and light fermentation organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S11earch results for the bio-photolysis, hydrogen production phenotype versus hydrogen non-production organisms.NIBBS-S This file consists for NIBBS-Search results when using bio-photolysis as target phenotype and hydrogen non-producing organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S12Metabolic pathways of Clostridium acetobutylicumearch for the dark fermentation, hydrogen production phenotype. enriched by the subsystems identified by NIBBS-S This file has the results described in section Pathways Related to Dark Fermentative Hydrogen Production.(XLS)Click here for additional data file.Table S13earch results for the acid-tolerent phenotype.NIBBS-S This file has the NIBBS-Search results for utlizing acid-tolerence as target phenotype.(TXT)Click here for additional data file.Table S14Enzymes related to acid-tolerent phenotype. This file consists of a comparison between enzymes in acid-tolerant organisms and alkaliphilic (non-acid-tolerant) organisms, in acid-tolerant experiments. Each row represents enzymes identified by NIBBS-Search and their corresponding pathways they are present in. The results are discussed in section Acid-tolerant Enzymes.(PDF)Click here for additional data file.Table S15Metabolic pathways of Clostridium acetobutylicumearch for the acid-tolerent phenotype. enriched by the subsystems identified by NIBBS-S This file consists of results described in section Acid-tolerant Pathways.(XLS)Click here for additional data file.Table S16Enzymes related to TCA/rTCA expression. This file consists of a comparison, presence (+) or absence (−) of enzymes across the set of organisms used in the TCA and rTCA experiments.(PDF)Click here for additional data file.Table S17earch results for TCA versus rTCA expression.NIBBS-S This file consists for NIBBS-Search results when using TCA expression as target phenotype and rTCA expressing organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S18earch results for rTCA versus TCA expression.NIBBS-S This file consists for NIBBS-Search results when using rTCA expression as target phenotype and TCA expressing organisms set was used as the “negative set.”(TXT)Click here for additional data file.Table S19earch seed generation algorithm to other seed generation algorithms for the dark fermentation, hydrogen production phenotype.Comparison of NIBBS-S This file consists a comparative analysis between the enzymes identified for dark fermenatation, hydrogen production phenotype by NIBBS to the enzymes identified for the same target phenotype by T-Test and Mutual information.(XLS)Click here for additional data file.Table S20Runtime comparisons. This file consists of the results of runtime comparison between NIBBS-Search and other algorithms.(XLSX)Click here for additional data file.Table S21earch and MBS-Enum.Correspondence between outputs of NIBBS-S This file consists of the results of the experiment evaluating the corresspondence of subgraphs output by NIBBS to the set of complete set of maximally-biased subgraphs output by MBS-Enum.(XLSX)Click here for additional data file.Text S1Systematic validation. This file contains the result of the experiments relating to the accuracy evalutaion of NIBBS-Search.(BZ2)Click here for additional data file.Text S2Parameter evaluation. This file contains the results of the experiments performed to analyze the effects of input parameters on the results.(BZ2)Click here for additional data file.Text S3earch Algorithm Pseudocode.NIBBS-S This file contains more details on the NIBBS-Search algorithm.(PDF)Click here for additional data file.


The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering. Saccharomyces cerevisiae and Aspergillus niger have a long history of commercial use in natural fermentation processes to produce chemicals such as ethanol and citric acid. Traditional bioprocess engineering entails the design and optimization of the equipment and procedures necessary to efficiently manufacture these and other biologically derived products. The development of recombinant DNA technologies enabled the direct manipulation and expansion of the metabolic capabilities of S. cerevisiae and A. niger (as well as other organisms such as Escherichia coli and Bacillus subtilis), which resulted in the emergence of metabolic engineering as a field distinct from bioprocess engineering [Organisms such as ineering . Metabolineering ,10,11,12S. cerevisiae [E. coli [Clostridium acetobutylicum [Frequently, metabolic engineering studies use targeted analysis of a few carefully selected intracellular or secreted extracellular compounds to drive or assess the progress of their efforts ,22,23,24revisiae ,21,23, E[E. coli ,19, Closutylicum ,18, and utylicum .13C labels) are leveraged to calculate fluxes – the rate at which material is processed through a metabolic pathway – from knowledge of carbon-carbon transitions in each reaction and the measured isotopomer distribution in each metabolite [13C protocols and analytical platforms [Another technique used to characterize metabolic pathways during metabolic engineering is Metabolic Flux Analysis (MFA). MFA provides more information than measurement of just a few metabolites, and is a staple technique of many who work in metabolic engineering ,30,31,32latforms ,34,35,36latforms , use of latforms , and stulatforms ,40.Metabolic engineering seeks to maximize the production of selected metabolites in a cell, whether produced by the organisms’ natural metabolic activities or by entire exogenous pathways introduced through genetic engineering. Strategic, small-scale measurements and flux calculations have to date been indispensable tools for metabolic engineering. However, the development of systems-level analyses – precipitated by whole-genome sequencing and the rapid accumulation of data on RNA, protein and metabolite levels – has provided new opportunities to more completely understand the effects of strain manipulations. Genetic modifications often have additional effects outside the immediately targeted pathway, and a better understanding of the nature and extent of these perturbations would lead to more effective strategies for redesigning strains, as well as improved ability to understand why a proposed design may fail to achieve its predicted performance.Aided by recent advancements in analytical platforms that allow for the simultaneous measurement of a wide spectrum of metabolites, metabolomics  is approaching the level of maturity of preceding “global analysis” fields like proteomics and transcriptomics ,42. MetaHere, we review examples of recent strategies to integrate metabolomics datasets into metabolic engineering. First, we briefly cover the fundamentals of metabolomics. We then discuss strategies for assessing metabolic engineering strain designs, and how metabolomics methods can extend these strategies. We follow with discussion of computational tools for metabolic engineering, with an emphasis on how these methods are used to design strains and predict their performance as well as how metabolomics datasets are currently applied to computational modeling. We conclude with a brief summary of the state of the field and the potential that integrating metabolomics presents.The development of metabolomics, the newest of the global analysis methods, has much in common with its predecessor fields of genomics, transcriptomics, and proteomics ,42. The One of the primary difficulties facing the development of metabolomics has been the staggering diversity of metabolites. Metabolites are substantially more chemically diverse than the subunit-based chemistries of DNA, RNA, and proteins, impeding the progress of metabolomics as a truly “omics” field that measures all metabolites. The entire genome and transcriptome can be  surveyed using single platforms, from simple PCR to more exhaustive sequencing and microarrays, whereas metabolomics requires multiple analytical platforms to achieve complete coverage of all metabolites.et al. [Common approaches involve coupling of a chromatographic separation to mass spectrometry, including gas chromatography-mass spectrometry (GC-MS) ,56,57,58et al. . As the youngest of the global analysis methods, metabolomics has drawn heavily from the data analysis techniques developed for transcriptomics and proteomics. Like these two fields, the datasets generated by metabolomics suffer from a “curse of dimensionality,” where there are far more variables than there are samples. Methods taken from transcriptomics and proteomics, as well as some derived from the field of chemometrics, have been used extensively to analyze metabolomics data as a result ,42 some. Though One of the most prominent methods for analysis of metabolomics data is Principal Components Analysis (PCA) b. This tS. cerevisiae to very high gravity (VHG) fermentations [S. cerevisiae [Pseudomonas putida growth on various carbon sources [A few examples of using PCA to reveal underlying patterns in metabolomics datasets include the characterization of extracellular culture conditions in Chinese Hamster Ovary (CHO) cell batch cultures , a studyntations , comparirevisiae ,48,68,82 sources ,81. In tet al. performed PCA on a flux balance analysis solution space to identify a lower-dimension set of key reactions that form the underlying basis of the solution space [Much of the value of PCA comes from its dimensional reduction capabilities: typically the first few components contain biologically relevant information, and higher components contain variance due to noise or biological variability. The number of components that are “significant” is an open question, and depends predominantly on the dataset or even the specific downstream processing and applications . Since ton space .et al. used PLS-DA to identify a set of compounds that could discriminate between different beers by their origin [et al. used OPLS-DA, a variant of PLS-DA that constructs distinct predictive and orthogonal components that describe between-class and within-class variance, respectively, [S. cerevisiae [Partial Least Squares (PLS) regression and discriminant analysis PLS-DA, d-f are ar origin . Kamei ectively, ,90, to arevisiae . They foet al. touches upon the importance of multiple hypothesis testing corrections in metabolomics studies, such as Bonferroni or false discovery rate corrections [Complete and effective use of a metabolomics dataset necessitates not only careful design of experiment and data processing methods, but also a thorough validation of conclusions from data analysis . For example, discussion of p-value distributions by Hojer-Pedersen rections ,92. As arections . Statistrections ,80,81, arections , chromatrections  and datarections .et al. provide a more comprehensive review of the applications of metabolomics to cancer biomarker discovery [S. cerevisiae and E. coli have been studied extensively, providing a rich biological context in which the metabolome of strains derived from both rational design and directed evolution strategies can be interpreted and understood. Nonetheless, the use of metabolomics in metabolic engineering is not as prevalent as one might expect.Metabolomics continues to be exploited for numerous biomedical applications, ranging from the study of differences between clinically isolated and industrial yeast strains , to blooiscovery ). MetaboThe simplest and most direct use of metabolomics datasets is as an extension of existing small-scale metabolite analyses; metabolomics inherently enables a more comprehensive assessment of a strain than a handful of narrowly selected measurements. Studies employing this approach typically either compare strains and culture conditions, or seek to monitor the time-course evolution of many metabolite concentrations in parallel. These studies use a combination of measured growth and production parameters in conjunction with direct examination of the metabolomics data (e.g. significant increases or decreases in metabolite levels) in the context of known biochemical pathways to determine the effects of mutations and culture conditions. For example, if one overexpresses the enzyme that is the first step in a linear biosynthetic pathway and finds that the first few metabolites accumulate significantly but subsequent metabolites do not, this may suggest a rate-limiting step further down the pathway that needs to be upregulated. Broader knowledge of metabolite levels beyond the target pathway can serve to determine the wider-ranging effects of a given metabolic engineering perturbation and can suggest candidate supplementary perturbations . E. coli by Toya et al., which compared parent and mutant responses to aerobic, anaerobic and nitrate-rich media conditions [13C MFA-derived fluxes, they found significant differences in tricarboxylic acid (TCA) cycle metabolism and ATP production among conditions. Similarly, Christen et al. compared the metabolomic profiles of seven yeast species to assess differences in aerobic fermentation on glucose [13C MFA suggested differences between TCA cycle fluxes and consistent flux through glycolysis, there was a much wider variation of metabolite levels across species – especially in amino acid pool compositions. They also found that across species, these values correlated poorly with fluxes.One example of a strain- or condition-comparison approach is a study of an arcA mutant in nditions . Through glucose . While 1et al. studied the effects of acetic and formic acid, chemicals commonly found in lignocellulosic hydrolysates, on xylose-utilizing strains of S. cerevisiae [In an example of time-course analysis, Hasunuma revisiae . They enrevisiae .S. cerevisiae has investigated the transient effects of redox perturbations [S. cerevisiae and Pichia pastoris [A. niger [Penicillium chrysogenum.Other work with rbations  or relierbations ,100, as pastoris . HoweverA. niger  to the eA. niger  and low A. niger  on penicThe simple approaches used to exploit the results of targeted measurements can be scaled up to metabolomics datasets, but they often do not take full advantage of structures or patterns in the data at the systems level. Many in the field of metabolic engineering have used multivariate techniques to interrogate metabolomics datasets on more complicated questions about strain performance and metabolite allocation. Due to the complexity of biological systems, the answers to these questions are often non-intuitive and increasingly difficult to identify without taking such a systems-scale approach. While rational design approaches were the original driving force in metabolic engineering, directed evolution and high throughput screens of mutant libraries have since become increasingly commonplace ,108,109.et al. used PCA and clustering analysis of metabolomics data, supplemented with transcriptional data, to investigate strains of S. cerevisiae that had been selected via directed evolution for improved galactose uptake [Common techniques employed in such approaches include HCA, PCA, and PLS-DA. These methods generate clusters or loadings that identify key metabolite differences, which in turn suggest what genetic changes may have been selected for. For example, Hong e uptake . They idet al. examined metabolic differences between S. cerevisiae and Saccharomyces pastorianus in regards to SO2 and H2S production [S. cerevisiae that they subsequently hypothesized to be rate-limiting fluxes for desired production of H2S and SO2. They verified their prediction by exposing S. pastorianus to known inhibitors for the corresponding enzymes, which recapitulated this effect. They used this knowledge to engineer and test S. cerevisiae strains that relieved these bottlenecks, and then developed a mutant with similar properties via a directed evolution process for commercial use.A study by Yoshida oduction . They geet al. investigated a xylose-utilizing strain of S. cerevisiae developed by introduction of L-arabinose pathway genes to an existing xylose-utilizing strain, followed by directed evolution to improve L-arabinose utilization [Similarly, Wisselink lization . They usS. cerevisiae [Other examples of using metabolomics for after-the-fact assessment of engineered strains include studying the effects of repeated exposure to vacuum fermentation conditions on revisiae , comparirevisiae , and iderevisiae . While the goal of metabolic engineering is to introduce a change on the metabolic level, many of these changes are necessarily implemented by introducing genetic modifications to affect transcriptional levels. As such, analysis of biological layers beyond the metabolome, such as the transcriptome and proteome, can provide further, and sometimes crucial, insight into the wide-reaching effects of an alteration.A number of techniques widely used in metabolomics (such as PCA and HCA) are also well-established for many of these other “omics” datasets, though there are a number of other techniques that until recently were more specific to transcriptional or proteomic analyses. One of the most prominent examples of this is enrichment analysis, originally developed for transcriptomics datasets. Enrichment analysis uses information about the frequency of occurrence or the ranking of sets of gene names or functions in a given list of genes to examine the biological relevance of observed changes . For exaE. coli [E. coli carbon storage regulation [P. chrysogenum [S. cerevisiae [S. cerevisiae [In metabolic engineering, use of metabolomics is comparatively much less common than using other global analysis approaches, perhaps attributable to the maturity of fields like transcriptomics and proteomics compared to metabolomics. Proteomics, transcriptomics and genomics have frequently been combined with small-scale metabolite measurements for metabolic engineering purposes. Examples of this include functional genomics with targeted metabolite measurements for isoprenoid production in E. coli , as wellgulation , penicilysogenum , glucoserevisiae , and relrevisiae .Corynebacterium glutamicum during different stages of batch culture [13C MFA to understand metabolic behavior in A. niger [13C MFA-derived fluxes [C. acetobutylicum in response to stresses in bioreactors, and analysis of both systems-level MFA calculations [S. cerevisiae.Flux measurements have also been commonly combined with other “omics” datasets, such as with the transcriptome and proteome in an analysis of lysine-producing  culture . Other eA. niger , transcrA. niger  and 13C d fluxes  of C. aculations  and Elemulations  in tandeThe above examples at most used small-scale metabolite measurements, but a handful of studies have combined analysis of full metabolomics datasets with other “omics” datasets. Previously described analysis of adaptations from directed evolution generally fit this category: transcriptional measurements using microarrays ,111,112 et al. assessed high gravity beer brewing conditions to determine the effect of the protease enzyme Flavourzyme on the free amino nitrogen content of the wort, and the resulting metabolomic and transcriptomic differences in a strain of brewer’s yeast [et al. investigated the growth characteristics of two strains of S. cerevisiae under two standard growth conditions. [et al. examined the combined metabolomic and transcriptomic response of S. cerevisiae to transient perturbations in glucose and ammonium concentrations [In other applications, Piddock ’s yeast . A colladitions. . Datasettrations . Their wassessment of the results of metabolic engineering strategies, rather than on systematic methods of designing strains to begin with. While some of these studies have used the insight gained from their evaluations to in turn design new strains, more systematic approaches to strain design are being developed – some of which are now capable of exploiting metabolomics datasets.The emergence of genome-scale investigations has led to a deluge of information about all molecular layers in the cell. This in turn has provided a broader context in which metabolic engineering strategies can be evaluated. However, we note that many of the techniques discussed so far have focused on systematic in silico before taking the time and expense to execute them in vivo, and they can close the loop on experimental design by producing hypotheses that, when tested, can be used iteratively to refine broader biochemical knowledge and models. This in turn leads to improved predictive power for subsequent rounds of metabolic engineering design.One of the difficulties in applying metabolomics datasets to strain design is the volume of data produced in a metabolomics experiment. Computational approaches are well suited to systematically integrate large volumes of biochemical knowledge and data. As shown in One of the most powerful ways to extend this concept would be to include metabolomic data in the design and fitting of computational models. While there are many well-developed metabolic modeling strategies, most of these approaches have not yet been adapted to effectively leverage the additional information that metabolomics can offer. Nonetheless, these strategies have made substantial contributions to metabolic engineering. We discuss these computational approaches to establish how they have been used to date in metabolic engineering, to suggest how metabolomics can contribute to their effectiveness, and to highlight current efforts to integrate the two.The most basic models for metabolic engineering use simplified equations for bioreactor kinetics to empirically fit relationships between characteristics such as metabolite uptake or secretion and specific growth rate. While these models are useful as tools for investigating specific behaviors of existing strains, their small-scale and coarse-grained nature precludes broader application to directing engineering strategies, as well as the possibility of substantively integrating metabolomics data even when available .in vitro for specific organisms . Adurements .To attempt to overcome these issues, “constraint-based” approaches that calculate metabolic fluxes primarily from stoichiometry were developed. This change of focus from dynamic metabolite levels to fluxes made sense, as the idea of optimizing and controlling metabolic fluxes has long been a fundamental part of metabolic engineering. These approaches allow flux calculations without the difficulties arising from parametric uncertainty by predicting flux distributions from the structure of the biochemical network and constraints on the feasible range of fluxes ,125.Flux Balance Analysis (FBA) is, in short, a modeling technique that uses metabolic network stoichiometry, a set of feasible flux ranges, and a cellular objective function to calculate an optimal flux distribution for a metabolic network ,125. To 13C MFA [E. coli [E. coli metabolism comparing growth in aerobic and anaerobic culture by Chen et al. highlights the complementary nature of FBA and MFA [A few examples of basic FBA for metabolic engineering include estimation of flux distributions from MFA measurements ,126, pre13C MFA , systema[E. coli , and eva[E. coli  and tran[E. coli . A study and MFA . They no and MFA , CellNet and MFA , the Sys and MFA , and Opt and MFA  are avaiet al. [A prerequisite step in FBA is the reconstruction of genome-scale metabolic networks for the organism of interest. Reviews by Fiest et al.  and by Tet al.  describeet al. ,139,140.A. niger [C. acetobutylicum [Clostridium beijerinckii [E. coli [S. cerevisiae [Mycoplasma genitalium [et al. [E. coli as a possible next step. Network reconstructions for several dozen species are publicly available, and programs such as MetRxn have been developed to aid comparison across model reconstructions [A few recent examples particularly relevant to metabolic engineering and metabolomics include models of A. niger , C. acetutylicum , and Cloerinckii , updated[E. coli ,144, andrevisiae . Notablynitalium  has rece [et al. , who havructions . Some exThe original FBA framework has been supplemented with dozens of refinements broadly referred to as constraint-based models. While these models retain the optimization problem framework based on stoichiometric constraints, the flux constraints or objective function are altered. We direct the reader to reviews on the topic of FBA by Lee, Gianchanani, and others for more complete discussion of these methods ,149, thoThe most basic refinements are straightforward extensions of FBA, from adding a simplified representation of transcriptional regulatory constraints, to integrating uptake/effluxes and comparing against extracellular concentration profiles. Examples from this family include regulatory FBA (rFBA) ,151,152,E. coli strains compared to the original model predictions. The GrowMatch algorithm uses growth and lethality data to correct a network reconstruction [S. cerevisiae led to improved knockout lethality predictions. [Another class of refinements to FBA comprises methods intended to better reduce the discrepancy between model predictions and experimental observations. Optimal metabolic network identification (OMNI) is used to identify discrepancies between measured and predicted fluxes, and then determine changes that need to be made to the model to better match the measurements . As a prtruction , and itsictions. . A numbeictions. ,161) metet al. compares the results of an evolved strain against an OptKnock prediction [et al. to design a strain exhibiting improved sesquiterpene production in S. cerevisiae [E. coli, it also identified several other successful and nonintuitive strategies. Again, while none of these methods integrate metabolomics data into their calculations, they could potentially be improved significantly if they could harness such data.More directly relevant to metabolic engineering applications is a class of refinements focused on predicting the result of metabolic network alterations. An early and well-known example of this is Minimization of Metabolic Adjustment (MOMA), which formulates a quadratic programming (QP) problem to find the feasible flux distribution nearest to the original FBA solution in response to a gene knockout . OptKnocediction ). OptGenediction , and wasrevisiae . Anotherrevisiae . In addiAs reviewed above, the majority of constraint-based modeling strategies make negligible use of systems-scale metabolite data in their calculations. The requirement that organisms adhere not only to stoichiometric mass conservation, but also to thermodynamic restrictions on energy and entropy, provides one means of introducing metabolite concentrations into the constraints. Several constraint-based model techniques make use of metabolite or metabolomics data in this fashion.Network-embedded Thermodynamic (NET) Analysis combines pre-determined flux directions with quantitative metabolomics datasets and the metabolite Gibbs energy of formations to determine the feasible ranges of Gibbs free energy of reaction throughout the system . This meS. cerevisiae revealed a thermodynamic inconsistency in whole-cell NAD/NADH ratio, which led Canelas et al. to engineer a strain with a bacterial mannitol-1-phosphate 5-dehydrogenase to allow them to determine cytosolic levels from measurements of cytosolic pH, fructose-6-phosphate, and mannitol-1-phosphate [NET analysis of a metabolomics dataset for hosphate . They fohosphate ,59,176.et al. developed Thermodynamic Metabolic Flux Analysis (TMFA), a constraint-based modeling approach similar to NET analysis [et al. also discuss the use of group contribution methods to calculate Gibbs energy of formation and the effect of the associated estimation uncertainties on TMFA results [Henry analysis . While banalysis ,179). He results . Recent  results ,181.et al. applied TMFA to 13C MFA calculations to produce thermodynamically consistent fluxes [E. coli network reconstruction published by Fiest et al. includes thermodynamic information, and the manuscript includes an assessment of thermodynamic consistency using TMFA [et al. used TMFA as one criterion for identifying near-equilibrium reactions in an effort to use metabolomics datasets to determine in vivo thermodynamic (and kinetic) parameters [Garg t fluxes . The E. ing TMFA . Canelasrameters .et al. developed a constraint-based model based on Ziegler’s principle for the maximization of entropy production that uses non-equilibrium thermodynamics to identify flux bottlenecks; they identified reactions that impeded previous efforts to improve glycolytic flux via overexpression of glycolysis enzymes in S. cerevisiae [et al. designed a constraint-based model that combines thermodynamic constraints similar to TMFA and NET analysis with a penalty function for deviations from concentration measurements. The minimization of both the overall flux and the penalty function are used as the optimization criteria. In applying this new approach, they found that increased network size leads to increased sensitivity of the resulting flux distribution to metabolite concentrations and Gibbs free energy changes [While TMFA and NET analysis have been the prevailing approaches used to incorporate metabolite concentrations and thermodynamic constraints into constraint-based modeling approaches, several other methods have been developed. For example, Bordel revisiae . Hoppe e changes . Constraint-based models have successfully directed numerous metabolic engineering projects. However, by construction they often ignore or have trouble dealing with dynamic metabolite behaviors that may have significant impact on final product titers, and in general they only indirectly make use of metabolite concentration measurements. Improved knowledge of network structures and strategies for dealing with parametric uncertainty have made ordinary differential equation (ODE) based models of metabolic kinetics increasingly viable tools for strain design. These methods explicitly model intracellular concentrations, making them attractive and convenient frameworks for integrating metabolomics datasets. Kinetic models are built around explicit mathematical descriptions of enzyme-metabolite interactions. Natural choices for kinetic rate laws are mass-action kinetics and Michaelis-Menten kinetics, but a review by Heijnen highlights several approximate rate laws that require fewer parameters and are relevant to metabolic engineering applications . IncludeS. cerevisiae using a local linearization method [E. coli to compare lin-log kinetics, convenience kinetics, power law kinetics, and Michaelis-Menten kinetics [per se, they demonstrate the principles underlying the effective implementation of kinetic modeling for those purposes.In light of recent genome-scale reconstruction efforts, several investigators have sought to assess the properties of several of these approximate forms in the context of metabolic networks. Examples of this approach include study of the glycolytic pathway in n method  and lin-n method ,192, as kinetics . This laet al. constructed a dynamical model of cellular redox state in S. cerevisiae to assess response to oxidative stress [et al. constructed a kinetic model of central carbon metabolism in E. coli in conjunction with MCA [et al. constructed a model of lactic acid fermentation in Lactococcus lactis, and the subsequent results were also used for MCA [Independent of metabolomics, kinetic models have already been applied in several recent metabolic engineering contexts. Rasler e stress . Literatwith MCA . Their m for MCA .et al. developed a model for central metabolism in E. coli as a proof of concept [et al. that predicted the effect of gene knockouts on the production of aromatic compounds in E. coli [et al. to predict flux data in L-lysine-producing E. coli [Ensemble approaches are also promising, and are in part a response to issues of parametric “sloppiness” which can preclude precise determination of kinetic parameters . These e concept . Startin E. coli , and a m E. coli . While tet al. used published kinetic parameters with time-course metabolomics measurements to assess metabolic control in xylose-fermenting S. cerevisiae [E. coli developed by Yuan et al. used kinetic parameters from the literature together with a genetic algorithm to fit undefined parameters to metabolomics data [None of the aforementioned ODE models explicitly look to integrate metabolomics data into their analyses, due in part to the only recent development of more quantitative metabolomics techniques. While scarce, there have been some efforts to use metabolomics data to improve or exploit ODE-based models of metabolism. For example, Klimacek revisiae . Similarics data . Proteomet al. developed a constraint-based modeling approach they referred to as integrative “omics”-metabolic analysis (IOMA), which solves a QP problem in a constraint-based model to fit a flux distribution to proteomic and metabolomics data [E. coli, IOMA demonstrated significantly improved recall, precision, and accuracy.Two recent examples have attempted to apply metabolomics measurements to models of an entire metabolic network. Yizhak ics data . Their aet al. follows a different scheme, by fitting thermodynamic equilibrium constants to a measured metabolomics dataset and a calculated flux distribution [A mass action stoichiometric simulation (MASS) modeling strategy described by Jamshidi ribution . The kinNotably, these last two methods both take advantage of constraint-based modeling strategies, but result in ODE-based kinetic models that can subsequently be used for strain design. This reflects the complementary nature of metabolite fluxes and concentrations, especially when faced with system-wide parametric uncertainty. However, to fully capture the wide-ranging dynamics that directly and indirectly contribute to the often subtle and nonintuitive behaviors exhibited in engineered strains, additional model detail is necessary. More advanced modeling strategies will need to find ways to integrate additional information, including proteomics and transcriptomics, to meet this need.Metabolomics is the global analysis of the metabolic content of a living system. While it has found increasing application in fundamental biological research and in fields of clinical interest (e.g. disease biomarker discovery), there is surprisingly little use of metabolomics approaches to drive metabolic engineering efforts. Existing experimental approaches to supplement rational metabolic engineering efforts typically focus instead on the determination of flux with MFA techniques, or the use of enzyme assays and analytical platforms such as HPLC for highly targeted metabolite measurements. While global analysis methods have been used to better predict and assess the effects of metabolic engineering modifications, the techniques most typically used have been transcriptomic or proteomic analyses – not metabolomics. While this may have previously been due to the relative immaturity of metabolomics techniques, the current technology in the field should allow for easy integration of metabolomics into metabolic engineering workflows.Direct applications of metabolomics datasets to metabolic engineering include expanding the existing narrowly targeted analysis methods to a broader scope, identifying non-intuitive mutations in strains produced by directed evolution, and adding direct metabolic context to other global analysis datasets. Computational approaches have also begun to integrate metabolomics datasets through thermodynamic constraints in constraint-based models, or even more directly in the case of some kinetic models.in vivo parameter estimation. Ensemble models are proving to be one effective method of addressing issues of parametric uncertainty and model “sloppiness”, and metabolomics provides substantial data to better constrain feasible parameter sets. With proper alterations and structural changes, constraint-based models may be able to more explicitly incorporate metabolite concentrations into constraints to capture effects such as allosteric regulation. We expect that these and other recently introduced approaches to integrate metabolomics data will yield substantial improvements in the efficiency and accuracy of prospective strain designs, both accelerating the pace and expanding the scope of developments in metabolic engineering. Nonetheless, it is clear that the depth and breadth of metabolic insight afforded by metabolomics is currently not being sufficiently exploited, and it is likely in such advances that we will make our most significant next steps as metabolic engineers.However, long-term strategies will need to find novel ways of incorporating the system-wide perspective provided by metabolomics and other global analysis methods. Such approaches will facilitate strain design based on increasingly detailed mechanistic descriptions and enable us to engineer strains towards any arbitrary product, not just those well-suited to high-throughput screens and directed evolution. Computational methods have a great deal of potential here. In the case of kinetic models, combining the metabolome and proteome can help address issues of


It has been shown in experimental and theoretical work that covalently modified signaling cascades naturally exhibit bidirectional signal propagation via a phenomenon known as retroactivity. An important consequence of retroactivity, which arises due to enzyme sequestration in covalently modified signaling cascades, is that a downstream perturbation can produce a response in a component upstream of the perturbation without the need for explicit feedback connections. Retroactivity may, therefore, play an important role in the cellular response to a targeted therapy. Kinase inhibitors are a class of targeted therapies designed to interfere with a specific kinase molecule in a dysregulated signaling pathway. While extremely promising as anti-cancer agents, kinase inhibitors may produce undesirable off-target effects by non-specific interactions or pathway cross-talk. We hypothesize that targeted therapies such as kinase inhibitors can produce off-target effects as a consequence of retroactivity alone.We used a computational model and a series of simple signaling motifs to test the hypothesis. Our results indicate that within physiologically and therapeutically relevant ranges for all parameters, a targeted inhibitor can naturally induce an off-target effect via retroactivity. The kinetics governing covalent modification cycles in a signaling network were more important for propagating an upstream off-target effect in our models than the kinetics governing the targeted therapy itself. Our results also reveal the surprising and crucial result that kinase inhibitors have the capacity to turn "on" an otherwise "off" parallel cascade when two cascades share an upstream activator.A proper and detailed characterization of a pathway's structure is important for identifying the optimal protein to target as well as what concentration of the targeted therapy is required to modulate the pathway in a safe and effective manner. We believe our results support the position that such characterizations should consider retroactivity as a robust potential source of off-target effects induced by kinase inhibitors and other targeted therapies. Cells propagate information through protein signaling pathways that are part of complex signal transduction networks . The simTargeted therapies are used to modulate disease progression by inhibiting a specific protein within a dysregulated signaling pathway . Kinase Recent theoretical and experimental studies have demonstrated that covalently modified cascades also exhibit bidirectional signal propagation via a phenomenon termed retroactivity -9. This While retroactivity is naturally present in covalently modified cascades, signaling pathways likely have evolved to propagate signals in an optimized downstream manner. An important consequence of retroactivity, however, is that a downstream perturbation in a signaling cascade can produce an upstream effect without the need for explicit negative feedback connections . RetroacVentura, Sepulchre, and Merajver  demonstrOff-target effects associated with targeted therapies are often attributed to crosstalk, which refers to inter-pathway molecular interactions arising because of explicit regulatory feedback connections between two pathways or because two pathways share one or more molecular components. It is well accepted that two pathways sharing one or more components can exhibit cross-talk with respect to a stimulation or perturbation above the shared component(s). If an upstream perturbation occurs in one of the pathways, the perturbation may affect the other pathway via the shared downstream component(s). Such a scenario could lead to specificity problems . Here weTo test our hypothesis, we created a computational model that tested the application of a kinase inhibitor in a series of simple signaling networks. The objective of the model was to probe the effect of a targeted inhibitor on retroactive signaling and to test whether retroactivity is likely to contribute to measurable off-target effects under physiological conditions. Specifically, the model simulated the targeted inhibition of a specific kinase in a series of multi-cycle networks. In all networks, at least two cascades were activated by the same upstream cycle with no explicit feedback connections between them. Our results indicate that within physiologically and therapeutically relevant ranges for all parameters, a targeted inhibitor can naturally induce a steady state off-target effect via retroactivity. Our results also reveal the surprising and crucial result that a downstream kinase inhibitor has the capacity to turn "on" an otherwise "off" parallel cascade when two cascades share an upstream activator.i contained the active (phosphorylated) and inactive (unphosphorylated) forms of protein iY, where the active form was denoted by We designed simple signaling networks to test whether a measurable off-target effect in one cascade can occur when a protein in another cascade is selectively inhibited. In each network studied, cycle Y1* served as the activating kinase for all cascades. Cycle 2 and cycle n were always in distinct cascades  and the substrate concentration necessary to achieve Subscripts containing i:(1) total enzyme to substrate ratio of the kinase and phosphatase reaction, respectively, in cycle m of the kinase and phosphataste reaction, respectively, in cycle i:(2) normalized Kwhere max ratio of the kinase and phosphatase reactions in cycle i:(3) Vwhere n*Y:(4) normalized disassociation constant of the inhibitor binding to i Eand i E'values less than 1 indicate that the enzyme is less abundant than the substrate. i Kand i K'values less than 1 indicate that the total available substrate exceeds the concentration needed to reach Km and, consequently, the enzymatic reaction operates close to or in the zero order regime [i Kand i K'values greater than 1 indicate that an insufficient amount of substrate exists to reach Km and the enzymatic reaction operates in the linear regime [i Pvalues greater than 1 indicate that the Vmax of the kinase reaction exceeds the Vmax of the phosphatase reaction and, consequently, the cycle tends toward the activation reaction. Likewise, i Pvalues less than 1 indicate that the cycle tends toward the deactivation reaction.r regime . In contr regime . Pi valuiY, D were normalized as follows:The concentrations of species y2 and I was varied from 10-4 to 104. If a change in the steady state value of y2 or I was intentionally larger than needed. For a given parameter set, it was numerically more efficient to simulate with a small (10-4) and a large (104) value for I and then check for a change in the steady state values of y2 and I. In fact, the majority of off-target effects in our model were observed as I was varied from 0.1 to 10.To determine if a detectable off-target effect occurred for a specific set of parameters, changes in the steady values of n = 3 network, we obtained the same results when we used either I = 0.0000 or I = 0.0001 (10-4) as the minimum drug concentration. For this reason , we effectively considered I = 10-4 to represent the absence of the drug in the system.When we tested the I, we first solved the system by setting the ODEs equal to zero and generating a system of steady state equations. As described in Appendix A, the model in this form was the basis for the non-dimensionalization of model parameters.For each network tested, a system of ordinary differential equations (ODEs) was used to model the rate of change of the reactants. Because we were only interested in changes in steady state values as a function of i yand i* yvariable values were substituted into the analytical steady state equations listed in Appendix A. Matlab source code was compiled as a C program and run on Intel Nehalem/i7 Core processors.For numerical reasons, it was more efficient to solve the ODEs over a very long time period rather than solving the system of steady state equations directly. After randomly selecting a set of non-dimensional parameters, the selected values were mapped to corresponding dimensional parameter values  to provide an efficient and even sampling distribution across the range of allowed values in the parameter space -16. Eachk Figure . The varnY*. Traditional biochemical sensitivity analysis [A modified perturbation method was used to probe which model parameters were most important for producing an off-target effect as a result of the inhibition of analysis  with theiE, iE', iK, iK', and B Kparameters had a default initial range of 0.01 - 100.0  0.10 - 1.0 and (ii) 1.0 - 10.0.The reduced ranges used to perturb each parameter were arrived at by partitioning the default range established for each parameter type in Table n = 3 network . Three of the parameters  had 2 perturbation sub-ranges each and the remaining 13 parameters had 4 perturbation sub-ranges each. In this example, the analysis consisted of a total of 59 sets of 5000 simulations (58 sets for each parameter sub-range and 1 set to establish the baseline percentage of off-target effects in the parameter space prior to perturbation).A complete numeric perturbation analysis of a parameter space consisted of determining the percentage of off-target effects in 5000 randomly selected parameter sets for each parameter's sub-ranges. In the k Figure  there wen, was perturbed by an inhibitor. An off-target effect occurred in the model if, after increasing I  from 10-4 to 104, a change in the steady state concentration of Y2 and/or Y2* occurred that was at least 0.10 of the total Y2 protein pool. For example, a change of 0.25 in Y2 and 0.08 in Y2* would indicate that the steady state values of Y2 and Y2* changed by 25% and 8% of the total Y2 protein pool, respectively, and that a detectable off-target effect occurred in Y2.The question we wanted to answer with our models was whether a targeted inhibitor is likely to induce a measurable off-target effect due to retroactivity in a non-targeted cascade under physiological conditions. In each network, cycle n = 3 network  had the greatest effect on off-target effects in this network  also affected the percentage of off-target effects but to a lesser degree than K3  also appeared to exert a large degree of control over off-target effects .s Figure . These rs Figure . When P3K2 . K2 values between 0.01 and 0.10 are expected to produce an efficient kinase reaction because K2 was restricted to values in this sub-range, a small percentage of off-target effects was observed   Figure . Larger e Figure . The revs Figure . TogetheBK, the normalized drug disassociation constant, had a very slight effect on the percentage of off-target effects. In general, B Kvalues greater than 1 produced a slight decrease in the percentage of off-target effects relative to the baseline , this result suggests when more total protein exists in cycle 1 compared to cycle 2, off-target effects in cycle 2 are more likely in this network.A second numerical perturbation analysis was performed using this new restricted s Figure . In the n = 3 parameter space. Only parameters in cycle 3 had the ability to effectively eliminate  the percentage of off-target effects within specific reduced ranges. Values between 10 and 100 for E3, E'3, K3 and K'3 produced off-target effect percentages of 0%, 3.24%, 0% and 3.20%, respectively. In addition, P3 values greater than 1 produced off-target effects in 18.24% of sampled parameter sets which, compared to the baseline of 73.9%, represents a large decrease in off-target effects . In each parameter set, either E2 or K3 was varied, while all other parameter values were fixed to the values listed in Table We used stimulus response curves to examine how a change in a single parameter value may affect the size of an off-target effect in n Figure . A randoPK model  were useY2* . In this parameter set the original E2 value was 32.56 and the original K3 value was 0.04  and decreasing K3 to 0.004 increased the response in Y2* to 0.26 . In this parameter set the original E2 value was 0.0025 and the original K3 value was 0.25  were outside the limits of parameter ranges allowed in our random parameter space explorations . This allowed us to keep the size of the parameter space fixed so that 5000 parameter sets remained a reasonable number to sample from each network's parameter space.We next investigated networks with more than 3 cycles by randomly exploring the parameter spaces of the vertical Figure  and laten = 5 and n = 7 networks were 27.92% and 13.50%, respectively  on the partially restricted parameter space depicted in Additional File To identify the parameters that were most important for amplifying an off-target effect from cycle 2 to cycle 4 in the n in a series of simple signaling networks  favored the activation reaction and its kinase reaction operated in or near the zero order regime.The binding affinity of the inhibitor for its target did not play a substantial role in the promotion of off-target effects in our model. Instead, the kinetics of the component cycles in the network were more important for increasing the likelihood of off-target effects  and that the Vmax of the deactivation reaction was generally larger than the Vmax of the activation reaction. Thus, off-target effects were promoted when cycle 2 was "off" and not consuming significant amounts of the shared upstream activator, 1*Y.If cycle 2's cascade was extended to include cycle 4 Figure , which wn Eand nE', respectively) were less than 1. In the n = 3 network, this meant that there was less total protein in cycle 1 than in cycle 3 because E3 < 1 implies Y1T <Y3T. The reason for this is that the smaller the Y1T/Y3T ratio, the stronger the sequestration of Y1* will be. The impact of this ratio increases if cycle 3 favors the deactivation reaction such that a large fraction of Y3T is in the inactive Y3 form, promoting the binding of Y3 to Y1*.The results also indicate that off-target effects were more likely when the total kinase to substrate and the total phosphatase to substrate ratios in the inhibited cycle  experimental and theoretical investigations are warranted. A proper characterization of a pathway's structure is important for identifying the optimal protein to target as well as what concentration of the targeted therapy is required to modulate the pathway in a safe and effective manner. We believe our results strongly support the position that such characterizations should consider retroactivity as a potential source of off-target effects induced by kinase inhibitors and other targeted therapies. This work has also provided an initial roadmap for how to assess the likelihood of off-target effects in a signaling network.While our approach does not definitively establish that the predicted responses will occur ACV and SDM conceived the study. ACV and JAS created the general model. ACV and MLW designed the experiments and analyzed results. MLW, HJG, and ACV wrote codes for numerical simulations. MLW wrote the manuscript and SDM, ACV, and JAS edited the manuscript. All authors read and approved the final manuscript.n = 3 network. The dimensionless parameters of the n = 5 and n = 7 vertical and lateral networks' were obtained in a similar manner.In order to reduce the complexity of the networks studied, model parameters were non-dimensionalized. The following explains the non-dimensionalization of the n = 3 network  at steady state are:The ODEs and conservation laws governing the Dimensionless ParametersDimensionless VariablesAlgebraic rearrangement and substitution yield the following dimensionless steady state equations:(1) (2) (3) (4) (5) (6) Mapping dimensionless parameters to dimensional parameters. This file describes how randomly sampled dimensionless parameter values were mapped to dimensional parameter values prior to numeric simulation.Click here for fileParameter space sampling to estimate the probability of off-target effects. This file describes how the parameter space of a network was sampled to provide an estimate of the probability of off-target effects due to retroactivity alone.Click here for filen = 3 and extended n = 3 networksAdditional analysis of the . This file provides additional results from the numeric perturbation analyses of the n = 3 and extended n = 3 networks.Click here for fileXenopus MAPK Model Parameters [Xenopus model parameters listed in Table rameters ,27,28. TClick here for file