Energy-based analysis of biochemical cycles using bond graphs
Energy-based analysis of biochemical cycles using bond graphs
复制标题
DOI：
10.1098/rspa.2014.0459
复制
发表时间：
2014-11-08
影响因子：
3.5
通讯作者：
Crampin, Edmund J.
中科院分区：
文献类型：
--
作者：
Gawthrop, Peter J.;Crampin, Edmund J.
化学反应的热力学方面在物理化学文献中有着悠久的历史。特别是，生物化学循环需要能量来源才能发挥作用。然而，尽管化学势和吉布自由能在生化系统分析中的作用是基本的，但往往被忽视，导致模型在物理上是不可能的。键合图方法是为工程系统建模而开发的，其中能源的产生、储存和传输是基本的。该方法关注的是功率如何在组件之间流动，以及能量如何在组件内存储、传输或消散。基于网络热力学的早期思想，我们将这种方法应用于生化系统，以生成自动遵守热力学定律的模型。我们用生化循环的例子来说明这种方法。我们发现，使用这种方法可以很容易地开发简单生化循环的热力学兼容模型。特别是，化学计量信息和模拟模型都可以直接从键图中开发出来。此外，在保留结构和热力学性质的同时，促进了模型的简化和近似。由于键图方法也是模块化和可扩展的，我们相信它为构建大型生化网络的热力学兼容模型提供了一个安全的基础。
Thermodynamic aspects of chemical reactions have a long history in the physical chemistry literature. In particular, biochemical cycles 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 the 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.