计算生物学的极限。

Q2 Medicine In Silico Biology Pub Date : 2015-01-01 DOI:10.3233/ISB-140461
Dennis Bray
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引用次数: 8

摘要

我们是否接近一个完整的生命过程清单,以便我们可以期望在不久的将来复制生命所必需的每一个基本方面?或者细胞和有机体中是否存在我们目前无法理解的机制和过程?在这里,我认为,对一个特别容易理解的系统——大肠杆菌趋化性系统——的仔细研究表明,我们离完整的描述还有很长的路要走。分子有一定程度的不确定性,特别是负责微调和适应无数外部条件的分子,我们目前无法在计算机上解决或复制这些问题。此外,同样的不确定性存在于任何生物体的任何过程中,在人类等高等动物中尤其明显和重要。胚胎发育、组织稳态、免疫识别、记忆形成以及在现实世界中的生存,都依赖于细胞化学中大量的细微变化,其中大多数目前尚不清楚或只有很少的特征。要克服这些限制,我们不仅需要积累大量非常详细的数据,还需要开发新的计算方法,能够重现活细胞的大规模并行处理。
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Limits of computational biology.

Are we close to a complete inventory of living processes so that we might expect in the near future to reproduce every essential aspect necessary for life? Or are there mechanisms and processes in cells and organisms that are presently inaccessible to us? Here I argue that a close examination of a particularly well-understood system--that of Escherichia coli chemotaxis--shows we are still a long way from a complete description. There is a level of molecular uncertainty, particularly that responsible for fine-tuning and adaptation to myriad external conditions, which we presently cannot resolve or reproduce on a computer. Moreover, the same uncertainty exists for any process in any organism and is especially pronounced and important in higher animals such as humans. Embryonic development, tissue homeostasis, immune recognition, memory formation, and survival in the real world, all depend on vast numbers of subtle variations in cell chemistry most of which are presently unknown or only poorly characterized. Overcoming these limitations will require us to not only accumulate large quantities of highly detailed data but also develop new computational methods able to recapitulate the massively parallel processing of living cells.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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