Animal models of idiosyncratic drug reactions

Idiosyncratic drug reactions represent a major problem. In most cases the mechanisms of these reactions are unknown, but circumstantial evidence points to the involvement of reactive metabolites and the characteristics of the reactions suggest involvement of the immune system. If progress is to be made in dealing with these adverse reactions it is essential that we have a better understanding of their mechanisms, and it is hard to imagine testing mechanistic hypotheses without good animal models. Unfortunately, idiosyncratic reactions are also idiosyncratic in animals so few good models exist. The best models, in which a rodent develops a clinical syndrome similar to that which occurs in humans, appear to be penicillamine-induced autoimmunity in Brown Norway rats and nevirapine-induced skin rash in rats. Sulfamethoxazole-induced hypersensitivity in dogs and propylthiouracil-induced autoimmunity in cats are also similar to adverse reactions that occur in people, but they have practical limitations. Halothane-induced liver toxicity in guinea pigs and amodiaquine-induced bone marrow and liver toxicity in rats represent models in which there is an immune response and mild, reversible toxicity. It is possible that the development of immune tolerance is what limits the toxicity in these models, and if this is true, interventions that prevent tolerance might lead to good models. Although the history of developing animal models of idiosyncratic drug reactions is mostly one of failure, such models are essential. A better understanding of immune tolerance may greatly facilitate the development of better models; transgenic technology may also provide an important tool.     

On the approaches applied in formulation of a kinetic model of photosystem II: Different approaches lead to different simulations of the chlorophyll a fluorescence transients

Chlorophyll a fluorescence rise (O-J-I-P transient) was in literature simulated using models describing reactions occurring solely in photosystem II (PSII) and plastoquinone (PQ) pool as well as using complex models which described, in addition to the above, also subsequent electron transport occurring beyond the PQ pool. However, there is no consistency in general approach how to formulate a kinetic model and how to describe particular reactions occurring even in PSII only. In this work, simple kinetic PSII models are considered always with the same electron carriers and same type of reactions but some reactions are approached in different ways: oxygen evolving complex is considered bound to PSII or “virtually” separated from PSII; exchange of doubly reduced secondary quinone PSII electron acceptor, Q_B, with PQ molecule from the PQ pool is described by one second order reaction or by two subsequent reactions; and all possible reactions or only those which follow in logical order are considered. By combining all these approaches, eight PSII models are formulated which are used for simulations of the chlorophyll a fluorescence transients. It is shown that the different approaches can lead to qualitatively different results. The approaches are compared with other models found elsewhere in the literature and therefore this work can help the readers to better understand the other models and their results.     

Oscillating reactions in open systems

The probable existence of oscillating chemical reactions has been attracting some interest in recent years for their possible role in explaining certain biological phenomena. Perhaps the simplest model of oscillating reactions is that of Lotka (1910), which consists of a chain of autocatalytic reactions. Two “reactor systems” in which such a chain of reactions could take place are considered in this work and are called homogeneous and compartmental models, respectively. The differential equations governing the temporal behavior of the reacting species are solved on an analog computer, and the conditions under which sustained oscillations occur are obtained and discussed. Comparisons of the solution obtained in the two models are discussed.     

Non-linear reduction for kinetic models of metabolic reaction networks

Kinetic models of metabolic networks are essential for predicting and optimizing the transient behavior of cells in culture. However, such models are inherently high dimensional and stiff due to the large number of species and reactions involved and to kinetic rate constants of widely different orders of magnitude. In this paper we address the problem of deriving non-stiff, reduced-order non-linear models of the dominant dynamics of metabolic networks with fast and slow reactions. We present a method, based on singular perturbation analysis, which allows the systematic identification of quasi-steady-state conditions for the fast reactions, and the derivation of explicit non-linear models of the slow dynamics independent of the fast reaction rate expressions. The method is successfully applied to detailed models of metabolism in human erythrocytes and Saccharomyces cerevisiae.     

Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach

Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.     

Assessment and parameter identification of simplified models to describe the kinetics of semi-continuous biomethane production from anaerobic digestion of green and food waste

Biochemical reactions occurring during anaerobic digestion have been modelled using reaction kinetic equations such as first-order, Contois and Monod which are then combined to form mechanistic models. This work considers models which include between one and three biochemical reactions to investigate if the choice of the reaction rate equation, complexity of the model structure as well as the inclusion of inhibition plays a key role in the ability of the model to describe the methane production from the semi-continuous anaerobic digestion of green waste (GW) and food waste (FW). A parameter estimation method was used to investigate the most important phenomena influencing the biogas production process. Experimental data were used to numerically estimate the model parameters and the quality of fit was quantified. Results obtained reveal that the model structure (i.e. number of reactions, inhibition) has a much stronger influence on the quality of fit compared with the choice of kinetic rate equations. In the case of GW there was only a marginal improvement when moving from a one to two reaction model, and none with inclusion of inhibition or three reactions. However, the behaviour of FW digestion was more complex and required either a two or three reaction model with inhibition functions for both ammonia and volatile fatty acids. Parameter values for the best fitting models are given for use by other authors.     

Reactions of pyrzdoxal 5'-phosphate, 6-aminocaproic acid, cysteine, and penicilamine. Models for reactions of Schiff base linkages in pyridoxal 5'-phosphate-requiring enymes.

Schiff base formation, transaldimination, and reaction with aminothiols are important reactions which occur on the surface of pyridoxal-P-requiring enzymes. As a first step in assessing the role of the protein in these reactions, models for these reactions were studied in the absence of enzyme at 25 degrees, gamma/2 0.28. The reaction of 6-aminocaproic acid with pyridoxal-P to form the Schiff base N6-(P-pyridoxylidene)-aminocaproic acid was studied as a model for formation of Schiff base on the holoenzyme...     

Stochastic Models of Gene Expression with Delayed Degradation

In many biochemical reactions occurring in living cells, number of various molecules might be low which results in significant stochastic fluctuations. In addition, most reactions are not instantaneous, there exist natural time delays in the evolution of cell states. It is a challenge to develop a systematic and rigorous treatment of stochastic dynamics with time delays and to investigate combined effects of stochasticity and delays in concrete models.We propose a new methodology to deal with time delays in biological systems and apply it to simple models of gene expression with delayed degradation. We show that time delay of protein degradation does not cause oscillations as it was recently argued. It follows from our rigorous analysis that one should look for different mechanisms responsible for oscillations observed in biological experiments.We develop a systematic analytical treatment of stochastic models of time delays. Specifically we take into account that some reactions, for example degradation, are consuming, that is: once molecules start to degrade they cannot be part in other degradation processes.We introduce an auxiliary stochastic process and calculate analytically the variance and the autocorrelation function of the number of protein molecules in stationary states in basic models of delayed protein degradation.     

MRAD: Metabolic reaction analysis database--an entity-relationship approach

The Metabolic Reaction Analysis Database (MRAD) is a relational database based on the Entity-Relationship (ER) model which combines information about organisms, biochemical pathways, reactions, enzymes, substrates, products and genes. It describes 244,596 genes in 79 organisms, 6,552 enzymes, and 3,552 reactions, 3,100 substrates, 2,866 products and 118 metabolic pathways. The MRAD graphical user interface allows for the identification of metabolic reactions which are similar and dissimilar in multiple organisms, reactions in a pathway which are missing in an organism and using any combination between one to six of the biological entities of organisms, genes, pathways, enzymes, substrates and products to determine metabolic reactions. MRAD provides a powerful and efficient tool for the construction of flux balance models for metabolic engineering applications.     

A microscopic model of enzyme kinetics.

Many in vivo enzymatic processes, such as those of the tissue factor pathway of blood coagulation, occur in environments with facilitated substrate delivery or enzymes bound to cellular or lipid surfaces, which are quite different from the ideal fluid environment for which the Michaelis-Menten equation was derived. To describe the kinetics of such reactions, we propose a microscopic model that focuses on the kinetics of a single-enzyme molecule. This model provides the foundation for macroscopic models of the system kinetics of reactions occurring in both ideal and nonideal environments. For ideal reaction systems, the corresponding macroscopic models thus derived are consistent with the Michaelis-Menten equation. It is shown that the apparent Km is in fact a function of the mechanism of substrate delivery and should be interpreted as the substrate level at which the enzyme vacancy time equals the residence time of ES-complexes; it is suggested that our microscopic model parameters characterize more accurately an enzyme and its catalytic efficiency than does the classical Km. This model can also be incorporated into computer simulations of more complex reactions as an alternative to explicit analytical formulation of a macroscopic model.     

Validation of fractal-like kinetic models by time-resolved binding kinetics of dansylamide and carbonic anhydrase in crowded media

Kinetic studies of biochemical reactions are typically carried out in a dilute solution that rarely contains anything more than reactants, products, and buffers. In such studies, mass-action-based kinetic models are used to analyze the progress curves. However, intracellular compartments are crowded by macromolecules. Therefore, we investigated the adequacy of the proposed generalizations of the mass-action model, which are meant to describe reactions in crowded media. To validate these models, we measured time-resolved kinetics for dansylamide binding to carbonic anhydrase in solutions crowded with polyethylene glycol and Ficoll. The measured progress curves clearly show the effects of crowding. The fractal-like model proposed by Savageau was used to fit these curves. In this model, the association rate coefficient k_a allometrically depends on concentrations of reactants. We also considered the fractal kinetic model proposed by Schnell and Turner, in which k_a depends on time according to a Zipf-Mandelbrot distribution, and some generalizations of these models. We found that the generalization of the mass-action model, in which association and dissociation rate coefficients are concentration-dependent, represents the preferred model. Other models based on time-dependent rate coefficients were inadequate or not preferred by model selection criteria.     

The efficiency of reactant site sampling in network-free simulation of rule-based models for biochemical systems

Rule-based models, which are typically formulated to represent cell signaling systems, can now be simulated via various network-free simulation methods. In a network-free method, reaction rates are calculated for rules that characterize molecular interactions, and these rule rates, which each correspond to the cumulative rate of all reactions implied by a rule, are used to perform a stochastic simulation of reaction kinetics. Network-free methods, which can be viewed as generalizations of Gillespie's method, are so named because these methods do not require that a list of individual reactions implied by a set of rules be explicitly generated, which is a requirement of other methods for simulating rule-based models. This requirement is impractical for rule sets that imply large reaction networks (i.e. long lists of individual reactions), as reaction network generation is expensive. Here, we compare the network-free simulation methods implemented in RuleMonkey and NFsim, general-purpose software tools for simulating rule-based models encoded in the BioNetGen language. The method implemented in NFsim uses rejection sampling to correct overestimates of rule rates, which introduces null events (i.e. time steps that do not change the state of the system being simulated). The method implemented in RuleMonkey uses iterative updates to track rule rates exactly, which avoids null events. To ensure a fair comparison of the two methods, we developed implementations of the rejection and rejection-free methods specific to a particular class of kinetic models for multivalent ligand-receptor interactions. These implementations were written with the intention of making them as much alike as possible, minimizing the contribution of irrelevant coding differences to efficiency differences. Simulation results show that performance of the rejection method is equal to or better than that of the rejection-free method over wide parameter ranges. However, when parameter values are such that ligand-induced aggregation of receptors yields a large connected receptor cluster, the rejection-free method is more efficient.     

Microdosimetry model for boron neutron capture therapy: I. Determination of microscopic quantities of heavy particles on a cellular scale

Due to the limitations of existing microdosimetry models, a new model called MICOR has been developed to analyze the spatial distribution of microscopic energy deposition for boron neutron capture therapy (BNCT). As in most existing models, the reactions independent of the incident neutron energy such as the boron and the nitrogen capture reactions can be considered. While other models do not include reactions that are dependent on the neutron energy such as the proton recoil reaction, the present model is designed so that the energy deposition resulting from these reactions is included. The model MICOR has been extended to enable the determination of the biological effects of BNCT, which cannot be done with the existing models. The present paper describes the determination of several microscopic quantities such as the number of hits, the energy deposition in the cell nucleus, and the distribution of lineal and specific energy deposition. The companion paper (Radiat. Res. 155, 000-000 2001) deals with the conversion of these microscopic quantities into biological effects. The model is used to analyze the results of a radiobiological experiment performed at the HB11 facility in the HFR in Petten. This analysis shows the value of the model in determining the dose depositions on a cellular scale and the importance of the extension to the energy deposition of the proton recoil.     

Deducing Underlying Mechanisms from Protein Recruitment Data

By using fluorescent labelling techniques, the distribution and dynamics of proteins can be measured within living cells, allowing to study in vivo the response of cells to a triggering event, such as DNA damage. In order to evaluate the reaction rate constants and to identify the proteins and reactions that are essential for the investigated process, mechanistic models are used, which often contain many proteins and associated parameters and are therefore underdetermined by the data. In order to establish criteria for assessing the significance of a model, we present here a systematic investigation of the information that can be reliably deduced from protein recruitment data, assuming that the complete set of reactions that affect the data of the considered protein species is not known. To this purpose, we study in detail models where one or two proteins that influence each other are recruited to a substrate. We show that in many cases the kind of interaction between the proteins can be deduced by analyzing the shape of the recruitment curves of one protein. Furthermore, we discuss in general in which cases it is possible to discriminate between different models and in which cases it is impossible based on the data. Finally, we argue that if different models fit experimental data equally well, conducting experiments with different protein concentrations would allow discrimination between the alternative models in many cases.     

Analysis of Reaction-Diffusion Systems with Anomalous Subdiffusion

Reaction-diffusion equations are the cornerstone of modeling biochemical systems with spatial gradients, which are relevant to biological processes such as signal transduction. Implicit in the formulation of these equations is the assumption of Fick's law, which states that the local diffusive flux of species i is proportional to its concentration gradient; however, in the context of complex fluids such as cytoplasm and cell membranes, the use of Fick's law is based on empiricism, whereas evidence has been mounting that such media foster anomalous subdiffusion (with mean-squared displacement increasing less than linearly with time) over certain length scales. Particularly when modeling diffusion-controlled reactions and other systems where the spatial domain is considered semi-infinite, assuming Fickian diffusion might not be appropriate. In this article, two simple, conceptually extreme models of anomalous subdiffusion are used in the framework of Green's functions to demonstrate the solution of four reaction-diffusion problems that are well known in the biophysical context of signal transduction: fluorescence recovery after photobleaching, the Smolochowski limit for diffusion-controlled reactions in solution, the spatial range of a diffusing molecule with finite lifetime, and the collision coupling mechanism of diffusion-controlled reactions in two dimensions. In each case, there are only subtle differences between the two subdiffusion models, suggesting how measurements of mean-squared displacement versus time might generally inform models of reactive systems with partial diffusion control.     

Soil Carbon Storage Response to Temperature: an Hypothesis

Recently, global and some regional observations of soil carbonstocks and turnover times have implied that warming may notdeplete soil carbon as much as predicted by ecosystem models.The proposed explanation is that microbial respiration of carbonin ‘old’ mineral pools is accelerated less by warmingthan ecosystem models currently assume. Data on the sensitivityof soil respiration to temperature are currently conflicting.An alternative or additional explanation is that warming increasesthe rate of physico-chemical processes which transfer organiccarbon to ‘protected’, more stable, soil carbonpools. These processes include adsorption reactions, some ofwhich are known to have positive activation energies. Theoretically,physico-chemical reactions may be expected to respond more towarming than enzyme-mediated microbial reactions. A simple analyticalmodel and a complex multi-pool soil carbon model are presented,which separate transfers between pools due to physico-chemicalreactions from those associated with microbial respiration.In the short-term, warming depletes soil carbon. But in thelong-term, carbon losses by accelerated microbial respirationare offset by increases in carbon input to the soil (net production)and any acceleration of soil physico-chemical ‘stabilization’reactions. In the models, if net production rates are increasedin response to notional warming by a factor of 1.3, and microbialrespiration (in all pools) by 1.5, then soil carbon at equilibriumremains unchanged if physico-chemical reactions are acceleratedby a factor of about 2.2 (50% more than microbial reactions).Equilibrium soil carbon increases if physico-chemical reactionsare over 50% more sensitive to warming than soil respiration.Copyright 2001 Annals of Botany Company Soil organic matter, carbon, respiration, temperature, stabilization, decomposition, model     

Kinetic studies of mobilization of copper(II) from human serum albumin with chelating agents

The kinetics of the mobilizing reactions of five chelating agents for human serum albumin (HSA)-bound copper(II) [Cu(II)] have been studied spectrophotometrically. The decreasing sequence of reaction rate has been determined to be EDTA greater than DTPA greater than EGTA greater than NTA greater than IDA. A group of mathematical models were established to define the mechanisms of the competitive reactions between low-molecular-weight ligand and macromolecular ligand. All reactions of the five chelating agents follow a process involving the intermediate ternary complexes: (formula; see text) The reactions of DTPA and EDTA were found to be different from those of EGTA, NTA, and IDA. In the former cases, the reactions are likely following an overlapping mechanism in which the rate constant k1 was closed to k2. The reactions involving the other three chelators are different in k1 much greater than k2.     

Interplay of organic and biological chemistry in understanding coenzyme mechanisms: example of thiamin diphosphate-dependent decarboxylations of 2-oxo acids.

With the publication of the three-dimensional structures of several thiamin diphosphate-dependent enzymes, the chemical mechanism of their non-oxidative and oxidative decarboxylation reactions is better understood. Chemical models for these reactions serve a useful purpose to help evaluate the additional catalytic rate acceleration provided by the protein component. The ability to generate, and spectroscopically observe, the two key zwitterionic intermediates invoked in such reactions allowed progress to be made in elucidating the rates and mechanisms of the elementary steps leading to and from these intermediates. The need remains to develop chemical models, which accurately reflect the enzyme-bound conformation of this coenzyme.     

Steric constraints in intramolecular reactions at sp3 carbon implications for methylase mechanisms

A series of methyl sulfonium compounds, containing appropriately positioned nucleophilic moieties, has been synthesized and studied as models for methylase enzymes in which the methyl sulfonium compound, S-adenosyl-l-methionine, serves as the methyl donor. The results of these studies show that there is a strict requirement for a linear transition state in intramolecular transmethylation reactions, and even slight deviations from this linear transition state are not permitted. These conclusions are pertinent in understanding the steric controls which appear to be operative in enzyme-catalyzed transmethylation reactions.