Exploring the diversity of complex metabolic networks

2005

Stoichiometrically exact and potentially feasible catalytic or metabolic pathways can be found by synthesizing the networks of plausible elementary or metabolic reactions constituting such pathways, respectively. The current contribution presents a mathematically exact algorithmic approach for carrying out the necessary synthesis, which is profoundly complex combinatorially. The approach is based on the unique graph-representation in terms of P-graphs (process graphs), a set of axioms, and a group of combinatorial algorithms. The inclusion or exclusion of a step of each elementary or metabolic reaction in the pathway of interest hinges on the general combinatorial properties of feasible reaction networks. At the outset, a brief overview is given of successful applications to date, followed by an outline of the methodology, on which the approach is based. The approach is illustrated by implementing it to three new examples comprising two catalytic reactions, catalytic combustion of hydrogen and reduction of nitrogen oxide, and one metabolic reaction, involved in the production of ethanol by yeast. The efficacy of the approach is discussed in light of the results obtained from these examples. Finally, a brief discourse is given of our current and future efforts.

Bioinformatics (Oxford, England), 2014

Several methods and computational tools have been developed to design novel metabolic pathways. A major challenge is evaluating the metabolic efficiency of the designed pathways in the host organism. Here we present FindPath, a unified system to predict and rank possible pathways according to their metabolic efficiency in the cellular system. This tool uses a chemical reaction database to generate possible metabolic pathways and exploits constraint-based models (CBMs) to identify the most efficient synthetic pathway to achieve the desired metabolic function in a given host microorganism. FindPath can be used with common tools for CBM manipulation and uses the standard SBML format for both input and output files. http://metasys.insa-toulouse.fr/software/findpath/. [email protected] Supplementary data are available at Bioinformatics online.

Lecture Notes in Computer Science, 2005

Recent small-world studies of the global structure of metabolic networks have been based on the shortest-path distance. In this paper, we propose new distance measures that are based on the structure of feasible metabolic pathways between metabolites. We argue that these distances capture the complexity of the underlying biochemical processes more accurately than the shortest-path distance. To test our approach in practice, we calculated our distances and shortest-path distances in two microbial organisms, S. cerevisiae and E. coli. The results show that metabolite interconversion is significantly more complex than was suggested in previous small-world studies. We also studied the effect of reaction removals (gene knock-outs) on the connectivity of the S. cerevisiae network and found out that the network is not particularly robust against such mutations.

Journal of Biotechnology, 1999

Metabolic networks comprise a multitude of enzymatic reactions carrying out various functions related to cell growth and product formation. Although such reactions are occasionally organized into biochemical pathways, a formal procedure is desired to identify the independent pathways in a bioreaction network and the degree of engagement of each individual reaction in these pathways. We present a procedure for the identification of the independent pathways of bioreaction networks of any size and complexity. The method makes use of the steady-state internal metabolite stoichiometry matrix and defines the independent pathways through the reaction membership of its kernel matrix. Examples from the aromatic amino acid biosynthetic pathway and central carbon metabolism of cells in culture are provided to illustrate the method. Applications to the analysis of the control structure of bioreaction networks are also discussed.

Journal of Chemical Information and Modeling, 2008

The development of metabolomics has resulted in the discovery of an increasing number of orphan metabolites, which are defined as compounds that are known to be present in living organisms but whose synthetic/ degradation pathways are unknown. In this paper, we describe a procedure for identifying possible products and/or precursors of such orphan metabolites and for suggesting complete reaction equations and the corresponding EC (Enzyme Commission) number simultaneously. Chemical structure comparison is performed for a pair of compounds consisting of a reported substrate and its corresponding product and also for pairs of randomly selected compounds. Possible combinations of compounds registered in the KEGG database were used for generating putative enzyme reaction equations, which resulted in 77% of the reported equations being generated, as most of the remainder represent classes of compounds, rather than specific compounds, or contain Markush structures. The quality was checked using chemical structure comparison and the randomtree method, which gave 98% accuracy in suggesting EC subsubclasses for reported equations in crossvalidation tests. The equations generated in this study can be seen using the Web-based program GREP (Generator of Reaction Equations & Pathways; http://bisscat.org/GREP/). The usefulness of our method for constructing possible metabolic pathways was demonstrated by mapping the generated equations for several groups of compounds, such as the betalain alkaloids. The possible development of our method so that alternative substrates for reported enzymes can be found and for annotating enzyme functions in genomic research is also discussed.

BMC Biochemistry, 2011

Background: The systematic, complete and correct reconstruction of genome-scale metabolic networks or metabolic pathways is one of the most challenging tasks in systems biology research. An essential requirement is the access to the complete biochemical knowledge -especially on the biochemical reactions. This knowledge is extracted from the scientific literature and collected in biological databases. Since the available databases differ in the number of biochemical reactions and the annotation of the reactions, an integrated knowledge resource would be of great value. Results: We developed a comprehensive non-redundant reaction database containing known enzyme-catalyzed and spontaneous reactions. Currently, it comprises 18,172 unique biochemical reactions. As source databases the biochemical databases BRENDA, KEGG, and MetaCyc were used. Reactions of these databases were matched and integrated by aligning substrates and products. For the latter a two-step comparison using their structures (via InChIs) and names was performed. Each biochemical reaction given as a reaction equation occurring in at least one of the databases was included. Conclusions: An integrated non-redundant reaction database has been developed and is made available to users. The database can significantly facilitate and accelerate the construction of accurate biochemical models.

Journal of Computational Chemistry, 1994

MECHEM is a computer aid for elucidation of reaction pathways that was developed over the last 5 years. The program searches systematically and comprehensively for the simplest multistep reaction pathways (or mechanisms) that are consistent with the experimental constraints formulated by the experimentalist, any ad hoc assumptions, and the program's internal theory. Previous articles have reported the basic pathway-generation algorithm and another algorithm that tests the structural soundness of individual steps. This article introduces an algorithm to solve another basic problem: Given a multistep pathway containing a mixture of molecular structures and formulas, assign possible structures to the formulas while obeying (and exploiting) the constraint imposed by the pathway steps. With this new algorithm, MECHEM is now approaching competence as an interactive tool for elucidating some catalytic reaction pathways, which is the current chemical focus. & Sons, Inc.

Nature Biotechnology, 2000

A set of linear pathways often does not capture the full range of behaviors of a metabolic network. The concept of 'elementary flux modes' provides a mathematical tool to define and comprehensively describe all metabolic routes that are both stoichiometrically and thermodynamically feasible for a group of enzymes. We have used this concept to analyze the interplay between the pentose phosphate pathway (PPP) and glycolysis. The set of elementary modes for this system involves conventional glycolysis, a futile cycle, all the modes of PPP function described in biochemistry textbooks, and additional modes that are a priori equally entitled to pathway status. Applications include maximizing product yield in amino acid and antibiotic synthesis, reconstruction and consistency checks of metabolism from genome data, analysis of enzyme deficiencies, and drug target identification in metabolic networks.

PLOS Computational Biology, 2017

In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.

Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Sub-strate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations. Examples of reaction centre detection in Metabolite TM are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10 −1 to 10 −5 and their order of magnitude reflected the known and experimentally observed metabolites. SPORCalc depends entirely on the level of detail from isoform-or species-specific reaction classes in Metabolite TM. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure–activity and physiochemical information. Crown

Nucleic Acids Research, 2005

Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H 2 O, NADP and H 1). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).

In metabolism research, thermodynamics is usually used to determine the directionality of a reaction or the feasibility of a pathway. However, the relationship between thermodynamic potentials and fluxes is not limited to questions of directionality: thermodynamics also affects the kinetics of reactions through the flux-force relationship, which states that the logarithm of the ratio between the forward and reverse fluxes is directly proportional to the change in Gibbs energy due to a reaction (D r G9). Accordingly, if an enzyme catalyzes a reaction with a D r G9 of-5.7 kJ/mol then the forward flux will be roughly ten times the reverse flux. As D r G9 approaches equilibrium (D r G9 = 0 kJ/mol), exponentially more enzyme counterproductively catalyzes the reverse reaction, reducing the net rate at which the reaction proceeds. Thus, the enzyme level required to achieve a given flux increases dramatically near equilibrium. Here, we develop a framework for quantifying the degree to which pathways suffer these thermodynamic limitations on flux. For each pathway, we calculate a single thermodynamically-derived metric (the Max-min Driving Force, MDF), which enables objective ranking of pathways by the degree to which their flux is constrained by low thermodynamic driving force. Our framework accounts for the effect of pH, ionic strength and metabolite concentration ranges and allows us to quantify how alterations to the pathway structure affect the pathway's thermodynamics. Applying this methodology to pathways of central metabolism sheds light on some of their features, including metabolic bypasses (e.g., fermentation pathways bypassing substrate-level phosphorylation), substrate channeling (e.g., of oxaloacetate from malate dehydrogenase to citrate synthase), and use of alternative cofactors (e.g., quinone as an electron acceptor instead of NAD). The methods presented here place another arrow in metabolic engineers' quiver, providing a simple means of evaluating the thermodynamic and kinetic quality of different pathway chemistries that produce the same molecules.

Computer methods and programs in biomedicine, 2015

Metabolic Engineering (ME) aims to design microbial cell factories towards the production of valuable compounds. In this endeavor, one important task relates to the search for the most suitable heterologous pathway(s) to add to the selected host. Different algorithms have been developed in the past towards this goal, following distinct approaches spanning constraint-based modeling, graph-based methods and knowledge-based systems based on chemical rules. While some of these methods search for pathways optimizing specific objective functions, here the focus will be on methods that address the enumeration of pathways that are able to convert a set of source compounds into desired targets and their posterior evaluation according to different criteria. Two pathway enumeration algorithms based on (hyper)graph-based representations are selected as the most promising ones and are analyzed in more detail: the Solution Structure Generation and the Find Path algorithms. Their capabilities and ...

Bioinformatics, 2003

Motivation: Automated methods for biochemical pathway inference are becoming increasingly important for understanding biological processes in living and synthetic systems. With the availability of data on complete genomes and increasing information about enzyme-catalyzed biochemistry it is becoming feasible to approach this problem computationally. In this paper we present PathMiner, a system for automatic metabolic pathway inference. PathMiner predicts metabolic routes by reasoning over transformations using chemical and biological information. Results: We build a biochemical state-space using data from known enzyme-catalyzed transformations in Ligand, including, 2917 unique transformations between 3890 different compounds. To predict metabolic pathways we explore this state-space by developing an informed search algorithm. For this purpose we develop a chemically motivated heuristic to guide the search. Since the algorithm does not depend on predefined pathways, it can efficiently identify plausible routes using known biochemical transformations.

Inferring genome-scale metabolic networks in emerging model organisms is challenging because of incomplete biochemical knowledge and incomplete conservation of biochemical pathways during evolution. This limits the possibility to automatically transfer knowledge from well-established model organisms. Therefore, specific bioinformatic tools are necessary to infer new biochemical reactions and new metabolic structures that can be checked experimentally. Using an integrative approach combining both genomic and metabolomic data in the red algal model Chondrus crispus, we show that, even metabolic pathways considered as conserved, like sterol or mycosporine-like amino acids (MAA) synthesis pathways, undergo substantial turnover. This phenomenon, which we formally define as "metabolic pathway drift", is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We...