Energy-based Analysis of Biochemical Cycles using Bond Graphs

TL;DR

This paper introduces a bond graph modeling approach for biochemical cycles that ensures thermodynamic compliance, enabling accurate, scalable, and modular models of biochemical systems based on energy flow principles.

Contribution

The paper applies bond graph methodology to biochemical systems, ensuring models obey thermodynamic laws and facilitating model reduction and scalability.

Findings

Thermodynamically compliant models can be easily developed using bond graphs.

Both stoichiometric and simulation models are derivable from bond graphs.

The approach supports modular and scalable modeling of large biochemical networks.

Abstract

Thermodynamic aspects of chemical reactions have a long history in the Physical Chemistry literature. In particular, biochemical cycles - the building-blocks of biochemical systems - require a source of energy to function. However, although fundamental, the role of chemical potential and Gibb's free energy in the analysis of biochemical systems is often overlooked leading to models which are physically impossible. The bond graph approach was developed for modelling engineering systems where energy generation, storage and transmission are fundamental. The method focuses on how power flows between components and how energy is stored, transmitted or dissipated within components. Based on early ideas of network thermodynamics, we have applied this approach to biochemical systems to generate models which automatically obey the laws of thermodynamics. We illustrate the method with examples of biochemical cycles. We have found that thermodynamically compliant models of simple biochemical cycles can easily be developed using this approach. In particular, both stoichiometric information and simulation models can be developed directly from the bond graph. Furthermore, model reduction and approximation while retaining structural and thermodynamic properties is facilitated. Because the bond graph approach is also modular and scaleable, we believe that it provides a secure foundation for building thermodynamically compliant models of large biochemical networks.