How Do We Study And Model The Kinetics And Thermodynamics Of Biochemical Reactions?

Introduction

Studying and modeling the kinetics and thermodynamics of biochemical reactions is crucial for understanding the dynamic behavior and regulation of biological systems. This involves developing mathematical models that accurately represent the rates of biochemical reactions and their thermodynamic constraints.

Key Insights

Thermodynamic-Kinetic Modeling (TKM) Formalism:
- TKM adapts concepts from irreversible thermodynamics to kinetic modeling, ensuring models observe detailed balance and thermodynamic feasibility .
In Vivo Data-Driven Approaches:
- Utilizing in vivo data to classify and quantify enzyme kinetics and determine apparent thermodynamic data can bridge the gap between model complexity and data availability.
Bond Graph Approach:
- This method focuses on energy flow and storage, ensuring models comply with thermodynamic laws. It is modular and scalable, making it suitable for large biochemical networks.
Irreversible Thermodynamics in Kinetic Modeling:
- Describing enzyme-catalyzed reactions using irreversible thermodynamics allows for consistent modeling of reactions approaching equilibrium, considering cytosolic conditions.
Stoichiometric and Kinetic Approaches:
- Traditional kinetic models often use rate laws like Michaelis-Menten, but newer approaches use power-law functions for more manageable representations of complex systems.
Thermodynamically Consistent Model Calibration (TCMC):
- TCMC ensures parameter values in kinetic models are thermodynamically feasible, improving model accuracy and reducing computational complexity.
Fractal-Like Kinetics in Crowded Environments:
- In vivo conditions often deviate from traditional kinetics due to macromolecular crowding, requiring modifications to conventional equations to account for fractal-like kinetics.
Time Series Data for Kinetic Mechanisms:
- Inferring kinetic mechanisms from time series data involves constructing global nonlinear models using elementary reactions as a basis.
Chemical Kinetics in Neuronal Signaling:
- Mechanistic mathematical models of neuronal signaling pathways can be developed using principles of chemical and enzyme kinetics, described by ordinary differential equations.

Conclusion

Modeling the kinetics and thermodynamics of biochemical reactions involves various approaches to ensure thermodynamic feasibility and accurate representation of reaction dynamics. Techniques such as thermodynamic-kinetic modeling, in vivo data-driven frameworks, bond graph approaches, and thermodynamically consistent model calibration are essential. Additionally, considering the unique conditions of intracellular environments and utilizing time series data can further refine these models. These methodologies collectively enhance our ability to understand and predict the behavior of complex biochemical systems.

How do we study and model the kinetics and thermodynamics of biochemical reactions?

Summary

Not a yes or no question

Introduction

Key Insights

Conclusion

Sources and full results

Thermodynamically feasible kinetic models of reaction networks.

An in vivo data-driven framework for classification and quantification of enzyme kinetics and determination of apparent thermodynamic data.

Energy-based analysis of biochemical cycles using bond graphs

Thermodynamics and Kinetics of Glycolytic Reactions. Part I: Kinetic Modeling Based on Irreversible Thermodynamics and Validation by Calorimetry

Biochemical Processes/Kinetics

Thermodynamic Constraints in Kinetic Modeling: Thermodynamic‐Kinetic Modeling in Comparison to Other Approaches

Thermodynamically consistent model calibration in chemical kinetics

Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws.

Extracting Biochemical Reaction Kinetics from Time Series Data

Using Chemical Kinetics to Model Neuronal Signalling Pathways

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