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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Accelerating Fluids01:17

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When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Nonlinear Pharmacokinetics: Michaelis-Menten Equation01:18

Nonlinear Pharmacokinetics: Michaelis-Menten Equation

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The Michaelis–Menten equation is a fundamental model for describing capacity-limited kinetics in drug metabolism. It offers insights into the rate of decline of plasma drug concentration Cp over time, with Vmax and KM as pivotal parameters.
Vmax represents the maximum achievable process rate, while KM, known as the Michaelis constant, signifies the drug concentration at which the process rate reaches half its maximum. This relationship between Vmax, KM, and Cp gives rise to three distinct...
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Velocity and Acceleration in Steady and Unsteady Flow01:11

Velocity and Acceleration in Steady and Unsteady Flow

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In fluid mechanics, velocity and acceleration are key concepts for analyzing particle motion in both steady and unsteady flow. Consider a fluid particle moving along a pathline, where its velocity depends on its position and time. The particle's acceleration is obtained by differentiating the velocity with respect to time.
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data.

Saratram Gopalakrishnan1, Satyakam Dash1, Costas Maranas1

  • 1Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.

Metabolic Engineering
|March 17, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces the Kinetics-based Fluxomics Integration Tool (K-FIT) to efficiently parameterize kinetic models. K-FIT overcomes previous limitations, enabling faster and more accurate predictions of metabolic networks under genetic perturbations.

Keywords:
E. coliKinetic models of metabolismMetabolic engineeringParameterization

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Kinetic models are crucial for predicting metabolic flows by linking metabolite concentrations and enzyme levels to reaction fluxes.
  • Parameterizing large-scale kinetic models to accurately reflect genetic or environmental changes is a significant challenge due to algorithm intractability.

Purpose of the Study:

  • To introduce a robust workflow, the Kinetics-based Fluxomics Integration Tool (K-FIT), for kinetic parameterization of metabolic models.
  • To enable accurate prediction of metabolic network behavior under various genetic perturbations.

Main Methods:

  • K-FIT employs a novel decomposition approach to identify steady-state fluxes in response to genetic perturbations.
  • It utilizes a gradient-based update of kinetic parameters to ensure model predictions align with fluxomic data across perturbed networks.
  • The workflow was validated on an expanded kinetic model of E. coli, incorporating 307 reactions and 258 metabolites, using data from six mutants.

Main Results:

  • K-FIT successfully parameterized a large-scale kinetic model of E. coli.
  • The tool demonstrated a thousand-fold speed-up compared to traditional meta-heuristic approaches.
  • This significant acceleration enables more robust inference analyses and optimized experimental design.

Conclusions:

  • K-FIT provides a computationally efficient and robust method for kinetic parameterization of metabolic models.
  • The tool's speed and accuracy facilitate advancements in metabolic engineering strategies and systems biology research.
  • This approach overcomes key limitations in current kinetic modeling, paving the way for more predictive biological network simulations.