Cortassa Sonia, Sollott Steven J, Aon Miguel A
National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Methods Mol Biol. 2018;1782:249-265. doi: 10.1007/978-1-4939-7831-1_14.
The advent of "big data" in biology (e.g., genomics, proteomics, metabolomics), holding the promise to reveal the nature of the formidable complexity in cellular and organ makeup and function, has highlighted the compelling need for analytical and integrative computational methods to interpret and make sense of the patterns and changes in those complex networks. Computational models need to be built on sound physicochemical mechanistic principles in order to integrate, interpret, and simulate high-throughput experimental data. Energy transduction processes have been traditionally studied with thermodynamic, kinetic, or thermo-kinetic models, with the latter proving superior to understand the control and regulation of mitochondrial energy metabolism and its interactions with cytoplasmic and other cellular compartments. In this work, we survey the methods to be followed to build a computational model of mitochondrial energetics in isolation or integrated into a network of cellular processes. We describe the use of analytical tools such as elementary flux modes, linear optimization of metabolic models, and control analysis, to help refine our grasp of biologically meaningful behaviors and model reliability. The use of these tools should improve the design, building, and interpretation of steady-state behaviors of computational models while assessing validation criteria and paving the way to prediction.
生物学领域“大数据”（如基因组学、蛋白质组学、代谢组学）的出现，有望揭示细胞和器官组成与功能中极其复杂的本质，这凸显了对分析和整合计算方法的迫切需求，以便解释和理解这些复杂网络中的模式与变化。计算模型需要建立在合理的物理化学机理原则之上，以便整合、解释和模拟高通量实验数据。传统上，能量转导过程是通过热力学、动力学或热动力学模型进行研究的，事实证明，后者在理解线粒体能量代谢的控制与调节及其与细胞质和其他细胞区室的相互作用方面更具优势。在这项工作中，我们探讨了构建孤立的线粒体能量学计算模型或整合到细胞过程网络中的计算模型所应遵循的方法。我们描述了诸如基本通量模式、代谢模型的线性优化和控制分析等分析工具的使用，以帮助我们更好地理解具有生物学意义的行为和模型可靠性。这些工具的使用应能改进计算模型稳态行为的设计、构建和解释，同时评估验证标准并为预测铺平道路。
