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PubMedDate not supplied

Some approaches to the pharmacology of multisubstrate enzyme systems.

Analytical and exploratory in vitro, in situ and in vivo, physio-pharmacotoxicology, from enzymology to population epidemiology, now embraces those approaches that correlate complex dynamic multisubstrate kinetics through conventional and more recent non-invasive quantitative methodologies. Basically, substrates may be classed as pertaining to fundamental energy turnovers (first-order cellular metabolic pathways or networks) and to iso- vs allosteric modulator systems (second-order metabolic control network). Pairs of substrates and cofactors set-up the third-order multienzyme-receptor patterns, which in intact, native in vivo structures establish and maintain the compartmentalized, dynamically superimposed overall coordination of local redox and phosphate potentials. Perturbations of the various levels of the metabolic hierarchy induced by drugs, as well their relaxations, can be readily submitted to non-invasive kinetic analysis. Both indirect and direct titrations of substrate levels, their modelling and statistical ad hoc evaluations of their interrelations can lead to the identification of the multiple sites involved in drug effects as structured at the different orders/levels of concomitant functional variations. Fractal geometries contribute towards defining the space- and time-related events.

Animals↗
PubMed2002

Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae.

MOTIVATION: Reconstructing and analyzing the metabolic map of microorganisms is an important challenge in bioinformatics. Pathway analysis of large metabolic networks meets with the problem of combinatorial explosion of pathways. Therefore, appropriate algorithms for an automated decomposition of these networks into smaller subsystems are needed. RESULTS: A decomposition algorithm for metabolic networks based on the local connectivity of metabolites is presented. Interrelations of this algorithm with alternative methods proposed in the literature and the theory of small world networks are discussed. The applicability of our method is illustrated by an analysis of the metabolism of Mycoplasma pneumoniae, which is an organism of considerable medical interest. The decomposition gives rise to 19 subnetworks. Three of these are here discussed in biochemical terms: arginine degradation, the tetrahydrofolate system, and nucleotide metabolism. The interrelations of pathway analysis of biochemical networks with Petri net theory are outlined.

Algorithms↗
PubMed1964

ALPHA-KETOGLUTARIC SEMIALDEHYDE: A METABOLIC INTERMEDIATE.

alpha-Ketoglutaric semialdehyde has been obtained as a product of hydroxyproline through reactions catalyzed by purified enzymes from Pseudomonas. It has been characterized both by chemical and enzymatic derivatives and by comparison with the chemically synthesized compound. This reactive compound, not previously known as a product of biological reactions, may participate in other metabolic pathways.

Aldehydes↗
PubMedDate not supplied

Heterozygosity: an expanding role in proteomics.

The human genome sequence provides the framework for understanding the biology of human cell function. The next step is to intensify the investigation of protein function in the context of complex biological systems. Cellular functions are carried out by molecular complexes acting in concert rather than by single molecules or single reactions. Parallels have been drawn between scale-free nonbiologic networks and functionally interconnected metabolic pathways in the cell. Modeling of metabolic networks, in which functional modules or subnetworks represent individual related pathways, will lead to the prediction of protein function in the larger context of a complex system. Depending on the robustness of these metabolic networks, single-gene defects alone or in combination with other gene defects and the environment have the potential for invoking a spectrum of alterations in the integrity of a given network. The overall purpose of this review is to highlight the importance of simple heterozygosity for one pathogenic mutation or combinatorial heterozygosity for two or more mutations within or between individual genes in altering the stability of metabolic networks. Several forms of heterozygosity are considered, e.g., intra- and interallelic heterozygosity and double heterozygosity. The concepts of synergistic heterozygosity, loss of heterozygosity, and mitochondrial DNA heteroplasmy also are discussed in relation to the quantitative effects of coexisting mutations on the phenotypic expression of disease.

Animals↗
PubMed2005

A combined approach to drug metabolism and toxicity assessment.

The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology, we have seen the growth, in recent years, of computational quantitative structure-activity relationships (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks, and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for aprepitant, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.

Drug-Related Side Effects and Adverse Reactions↗