Metabolic control theory: a structural approach

In the general framework of metabolic control theory, we describe a method of mathematical modelling that provides a way of analysing the sensitivity of a metabolic system to perturbations of the environment or of the internal state of this system. The method can be applied to any metabolic system, involving for instance conservation relationships, non-specific external parameters, etc., and leads in particular to a characterization of the control matrices and to a generalization of the summation and connectivity theorems. In this paper, we emphasize the structural characterizations and properties of the systems which depend only on the structure of the metabolic network, and not on the reaction kinetics. The advantage of this approach lies of course in the fact that the structure of the metabolic network is an invariant of the system which depends neither on the environment nor on the internal state of this system. The aim of this paper is to show the efficiency of such a structural approach.     

Metabolic pathway analysis of S. pneumoniae: an in silico approach towards drug-design

The emergence of multidrug resistant varieties of Streptococcus pneumoniae (S. pneumoniae) has led to a search for novel drug targets. An in silico comparative analysis of metabolic pathways of the host Homo sapiens (H. sapiens) and the pathogen S. pneumoniae have been performed. Enzymes from the biochemical pathways of S. pneumoniae from the KEGG metabolic pathway database were compared with proteins from the host H. sapiens, by performing a BLASTp search against the non-redundant database restricted to the H. sapiens subset. The e-value threshold cutoff was set to 0.005. Enzymes, which do not show similarity to any of the host proteins, below this threshold, were filtered out as potential drug targets. Five pathways unique to the pathogen S. pneumoniae when compared to the host H. sapiens have been identified. Potential drug targets from these pathways could be useful for the discovery of broad-spectrum drugs. Potential drug targets were also identified from pathways related to lipid metabolism, carbohydrate metabolism, amino acid metabolism, energy metabolism, vitamin and cofactor biosynthetic pathways and nucleotide metabolism. Of the 161 distinct targets identified from these pathways, many are in various stages of progress at the Microbial Genome Database. However, 44 of the targets are new and can be considered for rational drug design. The study was successful in listing out potential drug targets from the S. pneumoniae proteome involved in vital aspects of the pathogen's metabolism, persistence, virulence and cell wall biosynthesis. This systematic evaluation of metabolic pathways of host and pathogen through reliable and conventional bioinformatics approach can be extended to other pathogens of clinical interest.     

Metabolic flux analysis and fluxomics-driven determination of reaction free energy using multiple isotopes

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Metabolic pathway analysis and molecular docking analysis for identification of putative drug targets in Toxoplasma gondii: novel approach

Toxoplasma gondii is an obligate intracellular apicomplexan parasite that can infect a wide range of warm-blooded animals including humans. In humans and other intermediate hosts, toxoplasma develops into chronic infection that cannot be eliminated by host's immune response or by currently used drugs. In most cases, chronic infections are largely asymptomatic unless the host becomes immune compromised. Thus, toxoplasma is a global health problem and the situation has become more precarious due to the advent of HIV infections and poor toleration of drugs used to treat toxoplasma infection, having severe side effects and also resistance have been developed to the current generation of drugs. The emergence of these drug resistant varieties of T. gondii has led to a search for novel drug targets. We have performed a comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen T. gondii. The enzymes in the unique pathways of T. gondii, which do not show similarity to any protein from the host, represent attractive potential drug targets. We have listed out 11 such potential drug targets which are playing some important work in more than one pathway. Out of these, one important target is Glutamate dehydrogenase enzyme; it plays crucial part in oxidation reduction, metabolic process and amino acid metabolic process. As this is also present in the targets of tropical diseases of TDR (Tropical disease related Drug) target database and no PDB and MODBASE 3D structural model is available, homology models for Glutamate dehydrogenase enzyme were generated using MODELLER9v6. The model was further explored for the molecular dynamics simulation study with GROMACS, virtual screening and docking studies with suitable inhibitors against the NCI diversity subset molecules from ZINC database, by using AutoDock-Vina. The best ten docking solutions were selected (ZINC01690699, ZINC17465979, ZINC17465983, ZINC18141294_03, ZINC05462670, ZINC01572309, ZINC18055497_01, ZINC18141294, ZINC05462674 and ZINC13152284_01). Further the Complexes were analyzed through LIGPLOT. On the basis of Complex scoring and binding ability it is deciphered that these NCI diversity set II compounds, specifically ZINC01690699 (as it has minimum energy score and one of the highest number of interactions with the active site residue), could be promising inhibitors for T. gondii using Glutamate dehydrogenase as Drug target.     

Polymerase chain reaction: a Markov process approach

A probabilistic approach to the kinetics of the polymerase chain reaction (PCR) is developed. The approach treats the primer extension step of PCR as a microscopic Markov process in which the molecules of deoxy-nucleoside triphosphate (dNTP) are bound to the 3' end of the primer strand one at a time. The binding probability rates are prescribed by combinatorial rules in accord with the microscopic chemical kinetics. As an example, a simple model based on this approach is proposed and analysed, and an exact solution for the probability distribution of lengths of synthesized DNA strands is found by analytical means. Using this solution, it is demonstrated that the model is able to reproduce the main features of PCR, such as extreme sensitivity to the variation of control parameters and the existence of an amplification plateau. A multidimensional optimization technique is used to find numerically the optimum values of control parameters which maximize the yield of the target sequence for a given PCR run while minimizing the overall run time.     

Metabolic and contractile properties of rabbit muscles: a statistical approach

The metabolic and contractile properties of rabbit muscles representative of the three main muscle types have been studied. A statistical analysis of the results indicates the more discriminant variables for this characterization and shows in particular that the myosin light chains represent a very significant discriminant factor of the muscular type. Results also show that lactate dehydrogenase activity is strongly correlated with the percentage of lactate dehydrogenase M-subunit and with myosin light chains.     

Metabolic phenotyping of genetically modified mice: An NMR metabonomic approach

Monocyte chemoattractant protein-1 (MCP-1) plays a relevant role in macrophage migration but recent findings suggest an additional role in lipid and glucose metabolism. We report the use of ¹H NMR spectroscopy as a useful complementary method to assess the metabolic function of this gene in a comparative strategy. This metabonomic analysis was rapid, simple, quantitative and reproducible, and revealed a suggestive relationship between the expression of the MCP-1 gene and hepatic glucose and taurine concentrations. This approach should be considered in genetically modified mice when a metabolic alteration is suspected, or in routine assessment of metabolic phenotype.     

Metabolic control analysis: biological applications and insights

Metabolic control analysis provides a robust mathematical and theoretical framework for describing metabolic and signaling pathways and networks, and for quantifying the controls over these processes. Its application has already shed light on some of the principles underlying the regulation of metabolic pathways, and it is well suited to the analysis of the types of data emerging from genomic studies.     

Metabolic network sensitivity analysis

A linear sensitivity analysis of metabolic regulation in nonsteady states is described. This treatment considers the effects of enzymatic and nonenzymatic reactions and spontaneous rapid equilibria. Sensitivity coefficients summarizing the influence of metabolite concentrations on reaction rates and pathway net flux are defined, as are sensitivity coefficients summarizing the effects of enzymes on metabolite concentrations and net flux. The sensitivity analysis is implemented in an easily used set of computer programs. A four-enzyme test model was shown to be resistant to intuitive interpretation. Sensitivity analysis showed a shift of control from the end of the enzymic sequence to the beginning of the sequence with changing metabolic state. The homeostatic behavior of the test system was shown to depend on the nonenzymatic reactions as well as on the enzymes. Under certain conditions metabolic regulation is shared so intimately among enzymes and spontaneous reactions that separation of their effects is impossible.     

Green pathways: Metabolic network analysis of plant systems

Metabolic engineering of plants with enhanced crop yield and value-added compositional traits is particularly challenging as they probably exhibit the highest metabolic network complexity of all living organisms. Therefore, approaches of plant metabolic network analysis, which can provide systems-level understanding of plant physiology, appear valuable as guidance for plant metabolic engineers. Strongly supported by the sequencing of plant genomes, a number of different experimental and computational methods have emerged in recent years to study plant systems at various levels: from heterotrophic cell cultures to autotrophic entire plants. The present review presents a state-of-the-art toolbox for plant metabolic network analysis. Among the described approaches are different in silico modeling techniques, including flux balance analysis, elementary flux mode analysis and kinetic flux profiling, as well as different variants of experiments with plant systems which use radioactive and stable isotopes to determine in vivo plant metabolic fluxes. The fundamental principles of these techniques, the required data input and the obtained flux information are enriched by technical advices, specific to plants. In addition, pioneering and high-impacting findings of plant metabolic network analysis highlight the potential of the field.     

Metabolic engineering under uncertainty--II: analysis of yeast metabolism

Yeast metabolism has been used extensively in scientific investigations and industrial applications. Understanding the properties of the yeast metabolic network is crucial, yet unaccomplished due to its high complexity, the different culture conditions, and the uncertainty associated with kinetic parameters. We recently developed a computational and mathematical framework using Monte Carlo method in which parameter uncertainty can be addressed through large-scale sampling procedure. This framework was applied on the compartmentalized central carbon pathways of Saccharomyces cerevisiae metabolism considering the growth environment of batch and chemostat reactor and integrating information from metabolic flux analysis. Statistical analysis of the results indicates that yeast cells growing in batch culture condition exhibit dramatically different control schemes from those growing in a chemostat. The difference is mainly due to the feedback introduced by the constraints of the chemostat. The control of the enzymes on the rates of the substrate uptake, product excretion, and cell growth and its practical implication are discussed. Clustering of the reaction steps according to the similarity of their responses to enzyme activity perturbations reveals functional coupling of metabolic reactions.     

Metabolic flux analysis in plants: coping with complexity

Theory and experience in metabolic engineering both show that metabolism operates at the network level. In plants, this complexity is compounded by a high degree of compartmentation and the synthesis of a very wide array of secondary metabolic products. A further challenge to understanding and predicting plant metabolic function is posed by our ignorance about the structure of metabolic networks even in well-studied systems. Metabolic flux analysis (MFA) provides tools to measure and model the functioning of metabolism, and is making significant contributions to coping with their complexity.
This review gives an overview of different MFA approaches, the measurements required to implement them and the information they yield. The application of MFA methods to plant systems is then illustrated by several examples from the recent literature. Next, the challenges that plant metabolism poses for MFA are discussed together with ways that these can be addressed. Lastly, new developments in MFA are described that can be expected to improve the range and reliability of plant MFA in the coming years.     

Metabolic flux control analysis of branch points: an improved approach to obtain flux control coefficients from large perturbation data

An overview of published approaches for the metabolic flux control analysis of branch points revealed that often not all fundamental constraints on the flux control coefficients have been taken into account. This has led to contradictory statements in literature on the minimum number of large perturbation experiments required to estimate the complete set of flux control coefficients C(J) for a metabolic branch point. An improved calculation procedure, based on approximate Lin-log reaction kinetics, is proposed, providing explicit analytical solutions of steady state fluxes and metabolite concentrations as a function of large changes in enzyme levels. The obtained solutions allow direct calculation of elasticity ratios from experimental data and subsequently all C(J)-values from the unique relation between elasticity ratio's and flux control coefficients. This procedure ensures that the obtained C(J)-values satisfy all fundamental constraints. From these it follows that for a three enzyme branch point only one characterised or two uncharacterised large flux perturbations are sufficient to obtain all C(J)- values. The improved calculation procedure is illustrated with four experimental cases.     

Metabolic analysis in drug design

Biotechnology is often presented as if progress in the past two decades represented a major success, but the reality is quite different. For example, ten major classes of antibiotics were discovered between 1935 and 1963, but after 1963 there has been just one, the oxazolidones. To illustrate the possibilities of doing better by taking account of the real behaviour of metabolic systems, we can examine how one might modify the activity of an enzyme in the cell (for example by genetic manipulation, or by the action of an inhibitor, etc.) to satisfy a technological aim. For example, if the objective is to eliminate a pest, one might suppose that the effect of an inhibitor could be to depress an essential flux to a level insufficient for life, or to raise the concentration of an intermediate to a toxic level. The former may seem the more obvious, but the latter is easier to achieve in practice, and there are some excellent examples of industrial products that work in that way, such as the herbicide 'Roundup' and antimalarials of the quinine class. A study of glycolysis in the parasite Trypanosoma brucei (which causes African sleeping sickness) indicates that for this approach to work the selected target enzyme must have a substrate with a concentration that is not limited by stoichiometric constraints. That is not necessarily easy to find in a complicated system, and typically needs the metabolic network to be analysed in the computer.     

Metabolic flux analysis and visualization

One of the ultimate goals of systems biology research is to obtain a comprehensive understanding of the control mechanisms of complex cellular metabolisms. Metabolic Flux Analysis (MFA) is a important method for the quantitative estimation of intracellular metabolic flows through metabolic pathways and the elucidation of cellular physiology. The primary challenge in the use of MFA is that many biological networks are underdetermined systems; it is therefore difficult to narrow down the solution space from the stoichiometric constraints alone. In this tutorial, we present an overview of Flux Balance Analysis (FBA) and (13)C-Metabolic Flux Analysis ((13)C-MFA), both of which are frequently used to solve such underdetermined systems, and we demonstrate FBA and (13)C-MFA using the genome-scale model and the central carbon metabolism model, respectively. Furthermore, because such comprehensive study of intracellular fluxes is inherently complex, we subsequently introduce various pathway mapping and visualization tools to facilitate understanding of these data in the context of the pathways. Specific visualization of MFA results using the BioCyc Omics Viewer and Pathway Projector are shown as illustrative examples.     

Metabolic alteration of HepG2 in scaffold-based 3-D culture: proteomic approach

3-D cell culture models are important in cancer biology since they provide improved understanding of tumor microenvironment. We have established a 3-D culture model using HepG2 in natural collagen-based scaffold to mimic the development of small avascular tumor in vivo. Morphological characterization showed that HepG2 colonies grew within the interior of the scaffold and showed enhanced extracellular matrix deposition. High levels of cell proliferation in the outermost regions of the scaffold created a hypoxic microenvironment in the 3-D culture system, as indicated by hypoxia-inducible factor-1α stabilization, detectable by Western blotting and immunohistochemistry. Proteomic studies showed decreased expression of several mitochondrial proteins and increased expression of proteins in anaerobic glycolysis under 3-D culture compared to monolayer culture. Creatine kinase was also upregulated in 3-D culture, indicating its possible role as an important energy buffer system under hypoxic microenvironment. Increased levels of proteins in nucleotide metabolism may relate to cellular energy. Thus, our results suggest that HepG2 cells under 3-D culture adapt their energy metabolism in response to hypoxic conditions. Metabolic alterations in the 3-D culture model may relate to physiological changes relevant to development of small avascular tumor in vivo and their study may improve future therapeutic strategies.