Department of Chemical Engineering and Biotechnology, Millennium Institute for Cell Dynamics and Biotechnology: a Centre for Systems Biology, University of Chile, Beauchef 850, Santiago, Chile,
Adv Biochem Eng Biotechnol. 2012;127:71-108. doi: 10.1007/10_2011_120.
Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.
在过去的几十年中，大量关于细胞过程和反应速率的信息的可用性，以及对这些过程中涉及的复杂机制的日益增加的了解，改变了我们理解细胞过程的方式。我们不能再仅仅依靠我们的直觉来解释实验数据和评估新的假设，因为要分析的信息变得越来越复杂。细胞系统分析的范例已经从关注单个过程转变为综合的全局数学描述，这些描述考虑了代谢、基因组和信号网络的相互作用。分析和模拟用于通过反驳或验证关于复杂系统的新假设来检验我们的知识，这可能导致具有预测能力的更好的实验设计。可以根据特定系统可用的信息的类型和数量，使用不同类型的模型来实现这一目标。基于代谢结构的代谢模型允许探索网络中的稳态分布。详细的动力学模型提供了系统动态的描述，它们涉及大量具有不同动力学特性的反应，需要大量参数。基于统计信息的模型提供了对系统的描述，而不涉及涉及的网络的结构和相互作用的信息。由于哺乳动物系统的内在复杂性以及实验信息和适当建模工具的有限可用性，最近才开始更加强烈地开发用于哺乳动物细胞代谢的详细模型。在这项工作中，我们综述了哺乳动物细胞的策略、工具、当前进展和最近的模型，主要集中在代谢方面，但也讨论了应用于其他类型网络的方法。
