Beard Daniel A, Liang Shou-dan, Qian Hong
Department of Bioengineering, University of Washington, Seattle, Washington 98915, USA.
Biophys J. 2002 Jul;83(1):79-86. doi: 10.1016/S0006-3495(02)75150-3.
Predicting behavior of large-scale biochemical networks represents one of the greatest challenges of bioinformatics and computational biology. Computational tools for predicting fluxes in biochemical networks are applied in the fields of integrated and systems biology, bioinformatics, and genomics, and to aid in drug discovery and identification of potential drug targets. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are promising tools for the analysis of large complex networks. Here we introduce energy balance analysis (EBA)--the theory and methodology for enforcing the laws of thermodynamics in such simulations--making the results more physically realistic and revealing greater insight into the regulatory and control mechanisms operating in complex large-scale systems. We show that EBA eliminates thermodynamically infeasible results associated with FBA.
预测大规模生化网络的行为是生物信息学和计算生物学面临的最大挑战之一。用于预测生化网络通量的计算工具应用于整合生物学、系统生物学、生物信息学和基因组学领域，以辅助药物发现和潜在药物靶点的识别。通量平衡分析（FBA）等方法，在考虑反应网络已知化学计量的同时避免详细反应动力学的实施，是分析大型复杂网络的有前途的工具。在此，我们引入能量平衡分析（EBA）——在此类模拟中实施热力学定律的理论和方法——使结果更符合物理实际，并更深入地揭示复杂大规模系统中运行的调节和控制机制。我们表明，EBA消除了与FBA相关的热力学上不可行的结果。
