Related Experiment Video
Updated: Jul 9, 2026
06:24
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
Published on: December 15, 2017
Proximate parameter tuning for biochemical networks with uncertain kinetic parameters
Stephen J Wilkinson1, Neil Benson, Douglas B Kell
1School of Chemistry, Princess St, Manchester, UK. stephen.wilkinson@manchester.ac.uk
Molecular Biosystems
|December 14, 2007
Summary
This study introduces a new method for biochemical modeling parameter estimation. Proximate parameter tuning effectively identifies model parameters within defined bounds, improving system identification accuracy.
Area of Science:
- Biochemical Modeling
- Systems Biology
- Computational Chemistry
Background:
- Biochemical models often have known structure but unknown parameters.
- System identification is crucial for parameter estimation in these models.
- Underdetermined problems allow multiple parameter sets to fit data, complicating identification.
Purpose of the Study:
- To develop a deterministic algorithm for biochemical model parameter tuning.
- To address underdetermined system identification problems by incorporating parameter constraints.
- To improve the accuracy and reliability of parameter estimation in complex models.
Main Methods:
- Introduced 'proximate parameter tuning' with parameter bounds and nominal value proximity.
- Applied the algorithm to biochemical models including p38 signaling and yeast glycolysis.
- Validated the method on a benchmark dataset for thermal isomerization of alpha-pinene.
Main Results:
- The proximate parameter tuning algorithm demonstrated exceptional effectiveness.
- Constraining parameters within bounds and near nominal values resolved underdetermined issues.
- Successful application across diverse biological and chemical benchmark systems.
Conclusions:
- Proximate parameter tuning is a powerful and effective method for biochemical model system identification.
- The algorithm successfully overcomes challenges posed by underdetermined parameter spaces.
- This approach enhances the reliability of parameter estimation in various scientific modeling contexts.
Related Concept Videos
01:29Reaction Mechanisms: Rate-limiting Step Approximation
The rate-determining step, or RDS, in a chemical reaction is the slowest step that determines the overall reaction rate. It is identified by using the observed rate law and typically involves approximation methods like the RDS approximation or the steady-state approximation.In the RDS approximation, also known as the rate-limiting-step or equilibrium approximation, the reaction mechanism consists of one or more reversible reactions near equilibrium, followed by a slower RDS, and then one or...
01:19Introduction 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...
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...
01:20Determination of Michaelis Constant and Maximum Elimination Rate
The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
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...
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...
01:26Pharmacokinetic 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.
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.
01:01Dosage Regimens: Partial Pharmacokinetic Parameters
It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...