MOTIVATION: The analysis of high-throughput molecular data in the context of metabolic pathways is essential to uncover their underlying functional structure. Among different metabolic pathway concepts in systems biology, elementary flux modes (EFMs) hold a predominant place, as they naturally capture the complexity and plasticity of cellular metabolism and go beyond predefined metabolic maps. However, their use to interpret high-throughput data has been limited so far, mainly because their computation in genome-scale metabolic networks has been unfeasible. To face this issue, different optimization-based techniques have been recently introduced and their application to human metabolism is promising. RESULTS: In this article, we exploit and generalize the K-shortest EFM algorithm to determine a subset of EFMs in a human genome-scale metabolic network. This subset of EFMs involves a wide number of reported human metabolic pathways, as well as potential novel routes, and constitutes a valuable database where high-throughput data can be mapped and contextualized from a metabolic perspective. To illustrate this, we took expression data of 10 healthy human tissues from a previous study and predicted their characteristic EFMs based on enrichment analysis. We used a multivariate hypergeometric test and showed that it leads to more biologically meaningful results than standard hypergeometric. Finally, a biological discussion on the characteristic EFMs obtained in liver is conducted, finding a high level of agreement when compared with the literature.

BACKGROUND: Cheminformaticians have to routinely process and analyse libraries of small molecules. Among other things, that includes the standardization of molecules, calculation of various descriptors, visualisation of molecular structures, and downstream analysis. For this purpose, scientific workflow platforms such as the Konstanz Information Miner can be used if provided with the right plug-in. A workflow-based cheminformatics tool provides the advantage of ease-of-use and interoperability between complementary cheminformatics packages within the same framework, hence facilitating the analysis process. RESULTS: KNIME-CDK comprises functions for molecule conversion to/from common formats, generation of signatures, fingerprints, and molecular properties. It is based on the Chemistry Development Toolkit and uses the Chemical Markup Language for persistence. A comparison with the cheminformatics plug-in RDKit shows that KNIME-CDK supports a similar range of chemical classes and adds new functionality to the framework. We describe the design and integration of the plug-in, and demonstrate the usage of the nodes on ChEBI, a library of small molecules of biological interest. CONCLUSIONS: KNIME-CDK is an open-source plug-in for the Konstanz Information Miner, a free workflow platform. KNIME-CDK is build on top of the open-source Chemistry Development Toolkit and allows for efficient cross-vendor structural cheminformatics. Its ease-of-use and modularity enables researchers to automate routine tasks and data analysis, bringing complimentary cheminformatics functionality to the workflow environment.

MOTIVATION: The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. RESULTS: In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. AVAILABILITY: The source code in C++ is available upon request.

The question whether fatty acids can be converted into glucose in humans has a long standing tradition in biochemistry, and the expected answer is "No". Using recent advances in Systems Biology in the form of large-scale metabolic reconstructions, we reassessed this question by performing a global investigation of a genome-scale human metabolic network, which had been reconstructed on the basis of experimental results. By elementary flux pattern analysis, we found numerous pathways on which gluconeogenesis from fatty acids is feasible in humans. On these pathways, four moles of acetyl-CoA are converted into one mole of glucose and two moles of CO₂. Analyzing the detected pathways in detail we found that their energetic requirements potentially limit their capacity. This study has many other biochemical implications: effect of starvation, sports physiology, practically carbohydrate-free diets of inuit, as well as survival of hibernating animals and embryos of egg-laying animals. Moreover, the energetic loss associated to the usage of gluconeogenesis from fatty acids can help explain the efficiency of carbohydrate reduced and ketogenic diets such as the Atkins diet.

MOTIVATION: Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. RESULTS: In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Motivation: In recent years, several methods have been proposed for determining metabolic pathways in an automated way based on network topology. The aim of this work is to analyse these methods by tackling a concrete example relevant in biochemistry. It concerns the question whether even-chain fatty acids, being the most important constituents of lipids, can be converted into sugars at steady state. It was proved five decades ago that this conversion using the Krebs cycle is impossible unless the enzymes of the glyoxylate shunt (or alternative bypasses) are present in the system. Using this example, we can compare the various methods in pathway analysis. Results: Elementary modes analysis (EMA) of a set of enzymes corresponding to the Krebs cycle, glycolysis and gluconeogenesis supports the scientific evidence showing that there is no pathway capable of converting acetyl-CoA to glucose at steady state. This conversion is possible after the addition of isocitrate lyase and malate synthase (forming the glyoxylate shunt) to the system. Dealing with the same example, we compare EMA with two tools based on graph theory available online, PathFinding and Pathway Hunter Tool. These automated network generating tools do not succeed in predicting the conversions known from experiment. They sometimes generate unbalanced paths and reveal problems identifying side metabolites that are not responsible for the carbon net flux. This shows that, for metabolic pathway analysis, it is important to consider the topology (including bimolecular reactions) and stoichiometry of metabolic systems, as is done in EMA. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Contact:[email protected]; [email protected] The main points of criticism by Faust et al. (2009) concerning the work in de Figueiredo et al., (2009a) are the following: the cases presented are biased; different tools should be supplied with the same input networks; the number of study cases should be representative; and tools should be evaluated by neutral assessors. We think that the models in de Figueiredo et al. (2009a) illustrate concrete biological problems that are very well documented. In particular, the conversion of fatty acids into sugars was not so trivial to be answered and is historically relevant (cf. Weinman et al., 1957). Today, many biochemistry textbooks dedicate at least one paragraph explaining why there is no net conversion of acetyl-CoA to glucose via the tricarboxylic acid cycle in vertebrates (cf. Nelson and Cox, 2000; Stryer, 1995; Voet and Voet, 2004). Thus, new tools for metabolic pathway prediction have to be able to answer these problems correctly. Analysing the same network used in (de Figueiredo et al., 2009a) by Path Finding (Croes et al., 2006), paths converting acetyl-CoA into glucose are computed, even though this is impossible for humans in the network studied. Thus, the critique of Faust et al. (2009) does not bring anything new to what was discussed in de Figueiredo et al. (2009a). It is often said that the size of the input network limits the computation of elementary flux modes (EFMs; Faust et al., 2009; Papin et al., 2003, 2004). Indeed, the enumeration of all EFMs in genome-scale models with the existing methods is difficult (Klamt et al., 2007; Schwarz et al., 2005; Terzer and Stelling, 2008; von Kamp and Schuster, 2006). However, there are approaches to compute at least a subset of EFMs in such models (Acuña et al., 2009; de Figueiredo et al., 2009b; Kaleta et al., 2009), for example, the shortest EFMs (de Figueiredo et al., 2009b). Regarding the number of test cases presented in (de Figueiredo et al., 2009a), we are sure they are representative of the issue that is discussed in that article. More cases exist, for example, the conversion of hypoxanthine into ATP in human erythrocytes, for which EFM analysis can be performed at the cell level (Schuster and Kenanov, 2005). Of course, a comparison between tools is preferably made by neutral assessors. However, it is usual in bioinformatics that authors who have established a new tool compare their method with others (Klamt et al., 2007; Urbanczik and Wagner, 2005; Wagner and Urbanczik, 2005). In addition, one co-author (C.K.) has written an article where EFMs are compared with chemical organizations (Kaleta et al., 2006). In the letter by Faust et al. (2009), it is argued that an incorrect definition of internal metabolites in EFM analysis, can generate wrong pathway predictions. The study of any biochemical system requires the definition of the system's boundary (see, e.g. Schilling and Palsson, 1998). Thus, the definition of internal and external metabolites in EFM analysis is nothing more than the definition of the boundary conditions found in many other modelling methods. Faust et al. (2009) say that the steady-state constraint is not always an appropriate assumption. Although, in some cases, this assumption is not justified, in many other cases, it is. Accordingly, it is used in many approaches such as Metabolic Control and Flux Balance Analyses. Faust et al. (2009) cite the work of Teusink et al. (2000) to support their statement. However, that work shows that the experimental system does reach a steady state. Additionally, we do not think that the study summarized in Table 1 of Faust et al. (2009) is exhaustive enough with respect to EFMs (cf. Trinh et al., 2009, for a review). Nevertheless, the communication from Faust et al. (2009) raises an important point concerning the comparison of tools for metabolic pathway prediction. It is suggested to follow a CASP-like protocol to evaluate the methods for metabolic pathway prediction and that this task should be performed by an independent committee. The validation process of some of the new tools for metabolic pathway prediction has been performed using the pathway information present in metabolic pathway databases (Blum and Kohlbacher, 2008; Croes et al., 2006). A CASP-like protocol, to be developed, has to take into account the fact that these databases contain errors (Likić, 2006; Poolman et al., 2006). On the other hand, only very well-documented pathways are stored in these databases. Many pathways are missing due to the lack of information or simply due to the fact that this classification is performed manually. Furthermore, these databases do not contain all the functional modes of a pathway within a metabolic network, e.g. all the five functional modes of the pentose phosphate pathway in conjunction with glycolysis (Schuster et al., 2000; Stryer, 1995). Moreover, EFM analysis has been successful in predicting relevant, hitherto unknown pathways, for example, the catabolic PEP-glyoxylate cycle in Escherichia coli (Schuster et al., 1999), which was later found in experiment (Fischer and Sauer, 2003) and that, to our knowledge, is not present in KEGG nor in MetaCyc (Caspi et al., 2008; Kanehisa et al., 2008). In conclusion, it is a challenge to represent the combinatorial multitude of biochemical pathways in metabolic databases (Sauer, 2006). Funding: PhD grant SFRH/BD/32961/2006 (to L.F.F.); by the following Portuguese entities: Fundaçãao Calouste Gulbenkian, Fundação para a Ciência e a Tecnologia (FCT) and Siemens SA Portugal (to L.F.F.); Biotechnology and Biosciences Research Council (BB/·E00203X/1 to D.A.F.); German Ministry for Research and Education (BMBF) within the Forsys Partner initiative (to C.K.). Conflict of Interest: none declared.

MOTIVATION: In recent years, several methods have been proposed for determining metabolic pathways in an automated way based on network topology. The aim of this work is to analyse these methods by tackling a concrete example relevant in biochemistry. It concerns the question whether even-chain fatty acids, being the most important constituents of lipids, can be converted into sugars at steady state. It was proved five decades ago that this conversion using the Krebs cycle is impossible unless the enzymes of the glyoxylate shunt (or alternative bypasses) are present in the system. Using this example, we can compare the various methods in pathway analysis. RESULTS: Elementary modes analysis (EMA) of a set of enzymes corresponding to the Krebs cycle, glycolysis and gluconeogenesis supports the scientific evidence showing that there is no pathway capable of converting acetyl-CoA to glucose at steady state. This conversion is possible after the addition of isocitrate lyase and malate synthase (forming the glyoxylate shunt) to the system. Dealing with the same example, we compare EMA with two tools based on graph theory available online, PathFinding and Pathway Hunter Tool. These automated network generating tools do not succeed in predicting the conversions known from experiment. They sometimes generate unbalanced paths and reveal problems identifying side metabolites that are not responsible for the carbon net flux. This shows that, for metabolic pathway analysis, it is important to consider the topology (including bimolecular reactions) and stoichiometry of metabolic systems, as is done in EMA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.