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Related Concept Videos

Enzyme Kinetics01:19

Enzyme Kinetics

Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
Scientists typically study enzyme kinetics with a fixed amount of enzyme in the controlled environment of a test tube. When more reactant, or substrate, is...
Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Nonlinear Pharmacokinetics: Michaelis-Menten Equation01:18

Nonlinear Pharmacokinetics: Michaelis-Menten Equation

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...
Measuring Reaction Rates03:09

Measuring Reaction Rates

Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical field in...

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Updated: Jun 5, 2026

The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

Simulation of cellular biochemical system kinetics.

Daniel A Beard1

  • 1Biotechnology and Bioengineering Center, Medical College of Wisconsin, Milwaukee, WI, USA. beardda@gmail.com

Wiley Interdisciplinary Reviews. Systems Biology and Medicine
|December 21, 2010
PubMed
Summary
This summary is machine-generated.

Simulating cellular biochemical processes requires a systematic approach using extensive data and physical chemical theories. Key elements include a theoretical foundation, accurate databases, validated enzyme models, and integrated software tools for large-scale systems simulation.

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Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
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Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

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Last Updated: Jun 5, 2026

The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
07:31

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Published on: September 1, 2023

Area of Science:

  • Biochemistry
  • Systems Biology
  • Computational Biology

Background:

  • Cellular function relies on complex biochemical processes.
  • Accurate simulation of these processes is crucial for biological research.
  • Current simulation methods face challenges in integrating diverse data and theories.

Purpose of the Study:

  • To outline a strategic approach for realistic and reliable simulation of cellular biochemical systems.
  • To identify key components necessary for advancing large-scale systems simulation.
  • To foster community cooperation for developing a robust simulation infrastructure.

Main Methods:

  • Utilizing a broad range of in vitro and in vivo data.
  • Ensuring consistency with established physical chemical theories.
  • Developing a self-consistent theoretical foundation for systems simulation.
  • Creating extensive databases of biochemical reaction thermodynamic properties.
  • Parameterizing and validating models of enzyme and transporter mechanisms.
  • Developing integrated software tools for cohesive representation of cellular systems.

Main Results:

  • A systematic approach integrating data and theory can achieve realistic cellular simulation.
  • Establishing a theoretical foundation, accurate databases, validated models, and software tools are essential.
  • Ongoing initiatives are preparing the groundwork for large-scale community cooperation.

Conclusions:

  • Realistic simulation of cellular biochemical processes is achievable through a systematic, data-driven, and theory-consistent approach.
  • The development of a strategic infrastructure for large-scale systems simulation requires community-wide collaboration.
  • Key components include theoretical frameworks, comprehensive data, validated models, and integrated software.