Mechanistic understanding of P-gp mediated transport and inhibition kinetics across a confluent monolayer of MDCKII-hMDR1 cells using a mass action kinetic model

2021 ◽  
Author(s):  
Annie Albin Melba Lumine Lumen
Keyword(s):  
Kinetic Model ◽  
Mass Action ◽  
Inhibition Kinetics ◽  
Confluent Monolayer ◽  
Mass Action Kinetic ◽  
Mechanistic Understanding

scholarly journals Mass Action Kinetic Model of Apoptosis by TRAIL-Functionalized Leukocytes

2018 ◽  
Vol 8 ◽  
Author(s):  
Emily E. Lederman ◽  
Jacob M. Hope ◽  
Michael R. King
Keyword(s):  
Kinetic Model ◽  
Mass Action ◽  
Mass Action Kinetic

scholarly journals Three-dimensional stochastic model of actin–myosin binding in the sarcomere lattice

2016 ◽  
Vol 148 (6) ◽  
pp. 459-488 ◽  
Author(s):  
Srboljub M. Mijailovich ◽  
Oliver Kayser-Herold ◽  
Boban Stojanovic ◽  
Djordje Nedic ◽  
Thomas C. Irving ◽  
...  
Keyword(s):  
Kinetic Models ◽  
Striated Muscle ◽  
Three Dimensional ◽  
Limited Range ◽  
Mass Action ◽  
Myosin Filament ◽  
Mass Action Kinetic ◽  
Cross Bridge ◽  
Myosin Binding ◽  
Cross Bridges

The effect of molecule tethering in three-dimensional (3-D) space on bimolecular binding kinetics is rarely addressed and only occasionally incorporated into models of cell motility. The simplest system that can quantitatively determine this effect is the 3-D sarcomere lattice of the striated muscle, where tethered myosin in thick filaments can only bind to a relatively small number of available sites on the actin filament, positioned within a limited range of thermal movement of the myosin head. Here we implement spatially explicit actomyosin interactions into the multiscale Monte Carlo platform MUSICO, specifically defining how geometrical constraints on tethered myosins can modulate state transition rates in the actomyosin cycle. The simulations provide the distribution of myosin bound to sites on actin, ensure conservation of the number of interacting myosins and actin monomers, and most importantly, the departure in behavior of tethered myosin molecules from unconstrained myosin interactions with actin. In addition, MUSICO determines the number of cross-bridges in each actomyosin cycle state, the force and number of attached cross-bridges per myosin filament, the range of cross-bridge forces and accounts for energy consumption. At the macroscopic scale, MUSICO simulations show large differences in predicted force-velocity curves and in the response during early force recovery phase after a step change in length comparing to the two simplest mass action kinetic models. The origin of these differences is rooted in the different fluxes of myosin binding and corresponding instantaneous cross-bridge distributions and quantitatively reflects a major flaw of the mathematical description in all mass action kinetic models. Consequently, this new approach shows that accurate recapitulation of experimental data requires significantly different binding rates, number of actomyosin states, and cross-bridge elasticity than typically used in mass action kinetic models to correctly describe the biochemical reactions of tethered molecules and their interaction energetics.

Inhibition Mechanism and Kinetic Model for Ozagrel on Mushroom Tyrosinase

2011 ◽  
Vol 236-238 ◽  
pp. 2913-2916
Author(s):  
Bai Li Shu ◽  
Ya Juan Fan ◽  
Qi Meng Zhang ◽  
Ying Li Liu ◽  
Yue Gu
Keyword(s):  
Kinetic Model ◽  
Mixed Type ◽  
Kinetic Method ◽  
Inhibition Mechanism ◽  
Mushroom Tyrosinase ◽  
Inhibition Kinetics ◽  
Antithrombotic Drug ◽  
Substrate Complex ◽  
Mixed Type Inhibitor

This investigation shows that ozagrel, an antithrombotic drug, can inhibit mushroom tyrosinase well. The IC50 value was 3.45 mmol/L. Ozagrel was estimated to be a reversible mixed-type inhibitor of mushroom tyrosinase by Lineweaver-Burk plots. The inhibition kinetics has been studied by using the kinetic method of the substrate reaction described by Tsou. The constants of tyrosinase and tyrosinase-substrate complex inhibited by 1.0 mmol/L ozagrel have been determined to be 87.28 and 66.07 µmol/L, respectively.

scholarly journals Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis

2018 ◽  
Author(s):  
Carl D. Christensen ◽  
Jan-Hendrik S. Hofmeyr ◽  
Johann M. Rohwer
Keyword(s):  
Reaction Rates ◽  
Mass Action ◽  
Control Analysis ◽  
Metabolic System ◽  
Flux Control ◽  
Symbolic Control ◽  
Mechanistic Understanding ◽  
The Individual ◽  
High Level ◽  
Molecular Components

AbstractHigh-level behaviour of metabolic systems results from the properties of, and interactions between, numerous molecular components. Reaching a complete understanding of metabolic behaviour based on the system’s components is therefore a difficult task. This problem can be tackled by constructing and subsequently analysing kinetic models of metabolic pathways since such models aim to capture all the relevant properties of the system components and their interactions.Symbolic control analysis is a framework for analysing pathway models in order to reach a mechanistic understanding of their behaviour. By providing algebraic expressions for the sensitivities of system properties, such as metabolic fluxor steady-state concentrations, in terms of the properties of individual reactions it allows one to trace the high level behaviour back to these low level components. Here we apply this method to a model of pyruvate branch metabolism inLactococcus lactisin order to explain a previously observed negative flux response towards an increase in substrate concentration. With this method we are able to show, first, that the sensitivity of flux towards changes in reaction rates (represented by flux control coefficients) is determined by the individual metabolic branches of the pathway, and second, how the sensitivities of individual reaction rates towards their substrates (represented by elasticity coefficients) contribute to this flux control. We also quantify the contributions of enzyme binding and mass-action to enzyme elasticity separately, which allows for an even finer-grained understanding of flux control.These analytical tools allow us to analyse the control properties of a metabolic model and to arrive at a mechanistic understanding of the quantitative contributions of each of the enzymes to this control. Our analysis provides an example of the descriptive power of the general principles of symbolic control analysis.Author summaryMetabolic networks are complex systems consisting of numerous individual molecular components. The properties of these components, together with their non-linear interactions, give rise to high-level observed behaviour of the system in which they reside. Therefore, in order to fully understand the behaviour of a metabolic system, one has to consider the properties of all of its components. The analysis of computer models that capture and represent these systems and their components simplifies this task by allowing for an easy way to isolate the effects of each individual component. In this paper we use the framework of symbolic control analysis to investigate the sensitivity of the rate of flow of matter through one of the branches in a particular metabolic pathway towards changes in the rates of individual reactions. Here we are able to quantify how certain chains of reactions, individual reactions, and even thermodynamic and kinetic aspects of individual reactions contribute to the overall sensitivity of the rate of matter-flow. Thus, we are able to trace the behaviour of the system as a whole in a mechanistic way to the properties of the individual molecular components.

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