WinBEST-KIT: Windows-based biochemical reaction simulator for metabolic pathways

We have implemented an efficient, user-friendly biochemical reaction simulator called Web-based BEST-KIT (Biochemical Engineering System analyzing Tool-KIT) for analyzing large-scale nonlinear networks such as metabolic pathways. Users can easily design and analyze an arbitrary reaction scheme through the Internet and an efficient graphical user interface without considering the mathematical equations. The reaction scheme can include several reaction types, which are represented by both the mass action law (mass balance) and approximated velocity functions of enzyme kinetics at steady state, such as Michaelis-Menten, Hill cooperative, Competitive inhibition. However, since all modules in Web-based BEST-KIT have been developed in Java applet style, users cannot optionally make use of original mathematical equations in addition to the prepared equations. In the present study, we have developed a new version of BEST-KIT (for Microsoft Windows called WinBEST-KIT) to allow users to define original mathematical equations and to customize these equations very easily as user-defined reaction symbols. The following powerful system-analytical methods are prepared for system analysis: time-course calculation, parameter scanning, estimation of the values of unknown kinetic parameters based on experimentally observed time-course data of reactants, dynamic response of reactants against virtual external perturbations, and real-time simulation (Virtual Dry Lab).     

Kinetic modelling of plant metabolic pathways

This paper provides a review of kinetic modelling of plant metabolic pathways as a tool for analysing their control and regulation. An overview of different modelling strategies is presented, starting with those approaches that only require a knowledge of the network stoichiometry; these are referred to as structural. Flux-balance analysis, metabolic flux analysis using isotope labelling, and elementary mode analysis are briefly mentioned as three representative examples. The main focus of this paper, however, is a discussion of kinetic modelling, which requires, in addition to the stoichiometry, a knowledge of the kinetic properties of the constituent pathway enzymes. The different types of kinetic modelling analysis, namely time-course simulation, steady-state analysis, and metabolic control analysis, are explained in some detail. An overview is presented of strategies for obtaining model parameters, as well as software tools available for simulation of such models. The kinetic modelling approach is exemplified with discussion of three models from the general plant physiology literature. With the aid of kinetic modelling it is possible to perform a control analysis of a plant metabolic system, to identify potential targets for biotechnological manipulation, as well as to ascertain the regulatory importance of different enzymes (including isoforms of the same enzyme) in a pathway. Finally, a framework is presented for extending metabolic models to the whole-plant scale by linking biochemical reactions with diffusion and advective flow through the phloem. Future challenges include explicit modelling of subcellular compartments, as well as the integration of kinetic models on the different levels of the cellular and organizational hierarchy.     

Steady-state analysis of metabolic pathways: comparing the double modulation method and the lin-log approach

The increasing interest in studying enzyme kinetics under in vivo conditions requires practical methods to estimate control parameters from experimental data. In contrast to currently established approaches of dynamic modelling, this paper addresses the steady-state analysis of metabolic pathways. Within the framework of metabolic control analysis (MCA), elasticity coefficients are used to describe the control properties of a local enzyme reaction. The double modulation method is one of the first experimental approaches to estimate elasticity coefficients from measurements of steady-state flux rates and metabolite concentrations. We propose a generalized form of the double modulation method and compare it to the recently developed linear-logarithmic approach.     

METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer

METAMOD, a BBC microcomputer-based software package for steady-statemodelling and control analysis of model metabolic pathways,is described, The package consists of two programs. METADEFallows the user to define the pathway in terms of reactions,rate equations and initial concentrations of metabolites. METACALuses one of two algorithms to calculate the steady-state concentrationsand fluxes. One algorithm uses the current ratio of productionand consumption rates of variable metabolites to adjust iterativelytheir concentrations in such a way that they converge towardsthe steady state. The other algorithm solves the roots of thesystem equations by means of a quasi-Newtonian procedure. Controlanalysis allows the calculation of elasticity, control and responsecoefficients, by means of finite difference approximation. METAMODis interactive and easy to use, and suitable for teaching andresearch purposes. Received on January 17, 1986; accepted on June 2, 1986     

A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles

Background  

In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally, non-linear curve-fitting algorithms have been used for modelling, because of the non-linear properties of parameter estimation from time series. However, the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by linearization of S-systems.     

Increasing the flux in metabolic pathways: A metabolic control analysis perspective

The problems of engineering increased flux in metabolic pathways are analyzed in terms of the understanding provided by metabolic control analysis. Over-expression of a single enzyme is unlikely to be effective unless it is known to have a high flux control coefficient, which can be used as an approximate predictive tool. This is likely to rule out enzymes subject to feedback inhibition, because it transfers control downstream from the inhibited enzyme to the enzymes utilizing the feedback metabolite. Although abolishing feedback inhibition can restore flux control to an enzyme, it is also likely to cause large increases in the concentrations of metabolic intermediates. Simultaneous and coordinated over-expression of most of the enzymes in a pathway can, in principle, produce substantial flux increases without changes in metabolite levels, though technically it may be difficult to achieve. It is, however, closer to the method used by cells to change flux levels, where coordinated changes in the level of activity of pathway enzymes are the norm. Another option is to increase the demand for the pathway product, perhaps by increasing its rate of excretion or removal. Copyright 1998 John Wiley & Sons, Inc.     

A markov classification model for metabolic pathways

Background  

This paper considers the problem of identifying pathways through metabolic networks that relate to a specific biological response. Our proposed model, HME3M, first identifies frequently traversed network paths using a Markov mixture model. Then by employing a hierarchical mixture of experts, separate classifiers are built using information specific to each path and combined into an ensemble prediction for the response.     

A graph layout algorithm for drawing metabolic pathways

MOTIVATION: A large amount of data on metabolic pathways is available in databases. The ability to visualise the complex data dynamically would be useful for building more powerful research tools to access the databases. Metabolic pathways are typically modelled as graphs in which nodes represent chemical compounds, and edges represent chemical reactions between compounds. Thus, the problem of visualising pathways can be formulated as a graph layout problem. Currently available visual interfaces to biochemical databases either use static images or cannot cope well with more complex, non-standard pathways. RESULTS: This paper presents a new algorithm for drawing pathways which uses a combination of circular, hierarchic and force-directed graph layout algorithms to compute positions of the graph elements representing main compounds and reactions. The algorithm is particularly designed for cyclic or partially cyclic pathways or for combinations of complex pathways. It has been tested on five sample pathways with promising results.     

Extrapolation of metabolic pathways as an aid to modelling completely sequenced nonSaccharomyces yeasts

Mathematical models of biological processes for the model yeast Saccharomyces cerevisiae are the subject of intensive effort and are available in increasing numbers. An open question is whether such models are informative for related yeasts of biotechnological and medical interest that will not themselves benefit from an equivalent effort. In this study, we assess a method for extrapolating reference models to other completely sequenced yeasts, using a combination of graph-theoretic analysis and reliable identification of homologous genes using Génolevures protein families. In this first assessment, we focus on subtractive modeling, identified through the correlated loss of input and output ports in metabolic pathways. We confirm that the major, highly connected, pathways of central metabolism are conserved and might be universal. In 60-80% of our results, further analysis is not required to determine whether the pathway is lost or conserved, so that our method can be systematically applied as a first step in developing species-specific models.     

ScrumPy: metabolic modelling with Python

ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy.     

Reconstruction of metabolic pathways for the cattle genome

Background  

Metabolic reconstruction of microbial, plant and animal genomes is a necessary step toward understanding the evolutionary origins of metabolism and species-specific adaptive traits. The aims of this study were to reconstruct conserved metabolic pathways in the cattle genome and to identify metabolic pathways with missing genes and proteins. The MetaCyc database and PathwayTools software suite were chosen for this work because they are widely used and easy to implement.     

Simplified modelling of metabolic pathways for flux prediction and optimization: lessons from an in vitro reconstruction of the upper part of glycolysis

Explicit modelling of metabolic networks relies on well-known mathematical tools and specialized computer programs. However, identifying and estimating the values of the very numerous enzyme parameters inherent to the models remain a tedious and difficult task, and the rate equations of the reactions are usually not known in sufficient detail. A way to circumvent this problem is to use 'non-mechanistic' models, which may account for the behaviour of the systems with a limited number of parameters. Working on the first part of glycolysis reconstituted in vitro, we showed how to derive, from titration experiments, values of effective enzyme activity parameters that do not include explicitly any of the classical kinetic constants. With a maximum of only two parameters per enzyme, this approach produced very good estimates for the flux values, and enabled us to determine the optimization conditions of the system, i.e. to calculate the set of enzyme concentrations that maximizes the flux. This fast and easy method should be valuable in the context of integrative biology or for metabolic engineering, where the challenge is to deal with the dramatic increase in the number of parameters when the systems become complex.     

A genomics-guided approach for discovering and expressing cryptic metabolic pathways

Genome analysis of actinomycetes has revealed the presence of numerous cryptic gene clusters encoding putative natural products. These loci remain dormant until appropriate chemical or physical signals induce their expression. Here we demonstrate the use of a high-throughput genome scanning method to detect and analyze gene clusters involved in natural-product biosynthesis. This method was applied to uncover biosynthetic pathways encoding enediyne antitumor antibiotics in a variety of actinomycetes. Comparative analysis of five biosynthetic loci representative of the major structural classes of enediynes reveals the presence of a conserved cassette of five genes that includes a novel family of polyketide synthase (PKS). The enediyne PKS (PKSE) is proposed to be involved in the formation of the highly reactive chromophore ring structure (or "warhead") found in all enediynes. Genome scanning analysis indicates that the enediyne warhead cassette is widely dispersed among actinomycetes. We show that selective growth conditions can induce the expression of these loci, suggesting that the range of enediyne natural products may be much greater than previously thought. This technology can be used to increase the scope and diversity of natural-product discovery.     

Virtual mitochondria: metabolic modelling and control

Inside the eukaryotic cell, mitochondria are internal organelles of prokaryotic origin, responsible for energy supply in the cell. The control of the mitochondrial ATP production is a complex problem with different patterns according to different tissues and organs.Our aim is to continue to develop the modelling of oxidative phosphorylation in different tissues, to model other parts of mitochondrial metabolism and to include this virtual mitochondria in a virtual cell.In constructing the complete metabolic map of mitochondria, we will take advantage of the sequenced genomes of eukaryotic organism (10–15% of the yeast genome concerns mitochondria).     

A mathematical model of metabolic insulin signaling pathways

We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-zeta is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling.     

A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks

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.