How can biological modeling help cell biology?
How can biological modeling help cell biology?
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DOI：
10.1080/21592799.2017.1404780
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发表时间：
2017-10
期刊：
影响因子：
--
通讯作者：
E. Sztul
中科院分区：
文献类型：
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作者：
E. Sztul
《细胞物流》与无数其他期刊一起，一直是权威研究的出版商，这些研究描述了控制细胞内过程的分子机制，几乎都是通过实验方法发现的。的确，细胞生物学这门学科是建立在对细胞事件从组织到原子的不同尺度的经验观察之上的。在绝大多数这样的研究中，分析一个或最多几个参数（无论它们是什么），并描述它们的行为和/或它们之间的关系。虽然在讨论这类研究时通常会尝试将这类分析的细节扩展到其他分子和/或过程，但将任何特定的分析整合到细胞行为的总体中仍然是不可能的。经典细胞生物学根本没有能力“把所有的东西放在一起”，也没有能力把我们在过去几十年里积累的所有分子知识统一起来，以形成一个关于细胞功能的全面观点。似乎向前发展并将细胞生物学进化为全面的理解将需要新的方法和新的工具。计算细胞生物学领域涵盖了将实验数据整合到蜂窝网络的综合图像中所必需的可用工具。不管我们喜欢与否，越来越清楚的是，下一个重大的科学突破将发生在实验方法与数学建模和模拟相结合的水平上。最近，不同类型的计算方法被用于开发各种细胞过程的模型，包括细胞周期、信号回路、膜运输和细胞骨架组装，并使我们对这些事件有了系统级的理解。生物事件建模的最大困难在于生物系统的巨大复杂性。细胞过程发生在多个和完全不同的时空尺度上，从原子相互作用到细胞器运动等等。理解原子/分子反应的整体细胞行为是如何产生的是非常困难的，但是建模和计算模型构建工具的改进可能最终简化复杂网络的行为。似乎特别令人兴奋的最终目标是，一旦我们理解了单个机器或细胞器甚至网络的行为以及它们之间的关系，可能会出现一种新的现象，这种现象如果没有建模是无法理解的，而且由于它太大，有太多的参数，仅靠实验是无法获得的。这种大规模的行为必须被理解才能发展
Cellular Logistics, together with a myriad of other journals, has been the proud publisher of definitive studies describing the molecular mechanisms governing intracellular processes, almost all uncovered by experimental approaches. Indeed, the very discipline of Cell Biology is built on empirical observations of cellular events at different scales, form the tissue to the atomic. In the vast majority of such studies, one or maximally few parameters (whatever they may be) are analyzed, and their behavior and/or relationship between them is described. And while some attempt is usually made in the Discussion of such studies to extend the particulars of such analyses to other molecules and/or processes, it remains impossible to integrate any particular analysis into the totality of cellular behavior. Classical Cell Biology simply does not have the ability to “put it all together” and unite all the molecular knowledge that we have amassed over the last decades to develop a comprehensive view of how cells function.It seems that to move forward and evolve cell biology into comprehensive understanding will require new approaches and new tools. The available tools, essential for such integration of experimental data into a comprehensive picture of cellular networks, are encompassed by the field of Computational Cell Biology. Whether we like it or not, it is becoming clear that the next major scientific breakthroughs will occur at the level of combining experimental methods with mathematical modelling and simulations. Recently, different types of computational approaches have been used to develop models of various cellular processes including cell cycle, signaling circuits, membrane trafficking and cytoskeletal assembly, and move us towards a system-level understanding of such events. The largest difficulty in modeling biological events lies in the vast complexity of biological systems. Cellular processes occur on multiple and drastically different spatio-temporal scales, from atomic interactions to organellar motility and beyond. Understanding how overall cellular behaviors arise for atomic/molecular reactions is extremely difficult, but improvements in modeling and computational model building tools may eventually simplify the behaviors of complex networks. The ultimate goal that seems to be particularly exciting is the possibility that once we understand the behaviors of single machines or organelles or even networks and the relationships between them, a new phenomenon may emerge that was unfathomable without the modeling and could not be obtained by experimentation alone since it’s too vast and has too many parameters. Such large-scale behaviors must be understood to be able to develop