Systems biology: a way to make complex problems more understandable
Systems biology: a way to make complex problems more understandable
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系统生物学：一种使复杂问题更容易理解的方法
作者：
I. Lavrik;B. Zhivotovsky
尽管生物医学研究取得了重大进展，但由于各种疾病的复杂性及其变异性，仍然存在特殊的困难。许多疾病的发展及其治疗反应与细胞死亡途径的紊乱有关。虽然440年来我们对细胞死亡的认识仅限于细胞坏死和病理性坏死，但根据细胞死亡命名委员会的建议，目前有410种不同的细胞死亡方式被认可。1细胞死亡根据其形态学外观、酶学标准、功能方面或免疫学特征进行分类。2在该领域的密集工作，例如，到2014年初，PubMed上的4400000篇出版物与这一研究领域有关，导致了解各种细胞死亡模式的生化特征及其激活/发展/执行的一些分子机制。为了深入了解细胞死亡网络的复杂性，在过去的十年中，即将到来的系统生物学领域已经成功地应用。系统生物学是一个跨学科的研究领域，专注于生物结构内的复杂相互作用，采用整体视角方法进行生物和生物医学调查。系统生物学将理论和计算方法与定量实验数据相结合。在理论方面，使用了广泛的数学形式主义。他们的选择是基于建模所要回答的问题，可用的实验数据集和所考虑的信令网络的复杂性。布尔模型有效地用于表征大细胞死亡信号网络。在布尔建模中，蛋白质活动由可以关闭或打开的节点表示，并且不需要单个反应的定量特征的知识。相比之下，常微分方程（ODEs）描述信号网络的时间动力学，并需要知识的动力学参数的信号网络以及一组时间求解的实验数据。基于ODE的建模是细胞死亡网络分析中最常用的方法之一。常微分方程可能不足以对细胞内的时空过程进行建模，例如，涉及时空梯度的不同隔室内的易位。3在这种情况下，使用偏微分方程（PDE）进行建模。对由单细胞测量引起的细胞间变化进行建模需要随机模拟。此外，还采用Petri网、基于Agent的模型（ABMs）和贝叶斯模型对细胞死亡网络进行了分析。各种数学工具的组合允许定量描述主要的细胞死亡过程，并确定生物相关系统的属性，将在这个问题上强调。计算模型需要关于通路中分子的数量和相互作用常数的精确知识，这使得能够对复杂信号网络调节的分子机制进行独特的定量评估。这些强有力的定量需要国家的最先进的实验方法，包括定量生物化学，细胞生物学和质谱技术。经典的western blot和免疫沉淀技术是近年来发展起来的用于系统生物学研究的两种方法，可以在半定量和定量水平上产生时间分辨的群体数据。单细胞分析使用许多特殊工具，包括基于FRET和基于定位的半胱天冬酶活性探针，实现了对细胞死亡的有价值的见解。最后，质谱领域的进展是提出了基于AQUA和SILAC的技术，并且向离子阱质谱分析的发展为定量数据生成提供了另一个重要的技术进步。在过去的十年中，强大的系统生物学方法结合了高层次的数学和最先进的定量实验工作，帮助我们了解了细胞决定生存或死亡的许多方面，特别是死亡受体介导的细胞死亡途径和死亡受体介导的细胞死亡途径在系统水平上得到了前所未有的详细阐述和理解。4‐6
Despite major advances in biomedical research, exceptional difficulties remain arising from the complexity of various diseases and their variability. Development of many disorders and their therapeutic responses are associated with disturbances in cell death pathways. Although for 440 years ourknowledgeaboutcelldeathwasrestrictedtoapoptosisand pathological necrosis, nowadays based on Recommendations of the Nomenclature Committee of Cell Death, there are 410 different cell death modalities recognized. 1 Cell death was classified according to its morphological appearance, enzymological criteria, functional aspects or immunological characteristics. 2 Intensive work in the field, for example, by the beginning of 2014 4400000 publications in PubMed are related to this area of research, led to understanding the biochemical features of various modes of cell death and some of molecular mechanisms of their activation/development/ execution. To get the insight into the complexity of the cell death networks, the upcoming field of systems biology has been successfully employed over the past decade. Systems biology is an interdisciplinary field of research that focuses on complex interactions within biological structures using a holistic perspective approach to biological and biomedical investigations. Systems biology combines theoretical and computational approaches with quantitative experimental data. On the theoretical side, a wide spectrum of mathematical formalisms is used. Their choice is based on the question to be answered by the modeling, available experimental data sets and the intricacy of the signaling network under consideration. Boolean models are effectively used to characterize large cell death signaling networks. In Boolean modeling, protein activities are presented by nodes that can be either off or on, and no knowledge is required for the quantitative characteristics of the individual reactions. In contrast, ordinary differential equations (ODEs) describe temporal dynamics of signaling networks and require the knowledgeof kinetic parameters of thesystem as well as a set of temporally solved experimental data. ODE-based modeling is one of the most common approachesused in the analysis of the cell death networks. ODEs might not be sufficient for modeling spatiotemporal processes within the cell, for example, translocations within different compartments that involve spatiotemporal gradients. 3 In this case modeling is conducted using partial differential equations (PDEs). Modeling cell-to-cell variations arising from single-cell measurements requires stochastic simulations. In addition, Petri nets, agent-basedmodels(ABMs)andBayesianmodelshavebeen employed for the analysis of the cell death networks. The combination of various mathematical tools allowed to quantitatively describe the major cell death processes and to identify biologically relevant systems’ properties that will be highlighted in this issue. Computational models require the exact knowledge about the numbers and interaction constants of the molecules in the pathway that allows making unique quantitative assessments upon molecular mechanisms of the complex signaling network regulation. These vigorous quantifications require state-of-the-art experimental methodology that includes quantitative biochemistry, cell biology and mass spectrometry techniques. The classical western blot and immunoprecipitation approaches were recently developed in the systems biology studies to generate time-resolved population data on the semiquantitative and quantitative levels. Single-cell analysis has enabled valuable insights into cell death using a number of special tools, including FRET-based and localization-based caspase activity probes. Finally, progress in mass spectrometry field is coming up with AQUA- and SILAC-based technologies, and development toward singlecell mass spectrometry analysis provides yet another major technological advance essential for the quantitative data generation. During the past decade, the powerful methodology of systems biology combining high-level mathematics with the state-of-the-art quantitative experimental work helped us to understand many aspects of cell’s decision to live or to die, in particular, death receptor- and mitochondria-mediated cell death pathways were elucidated and understood on a systems level with the unprecedented level of detail. 4‐6