绘图、建模和重编程细胞命运决策系统。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Lucy Ham, Taylor E Woodward, Megan A Coomer, Michael P H Stumpf
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引用次数: 0

摘要

许多细胞过程涉及信息处理和决策。我们可以通过增加分子细节来探索这些过程。异构数据的分析仍然是一个挑战,需要以定量、预测和机制的方式思考细胞的新方法。我们讨论了数学模型在整个生命之树的细胞命运决策系统中的作用。复杂的多细胞生物一直是一个特别的焦点,但单细胞生物也必须感知和响应他们的环境。我们的讨论集中在设计原则的概念上,我们可以从观察和建模中学习,并利用这些原则来(重新)设计或指导细胞行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping, Modeling, and Reprogramming Cell-Fate Decision-Making Systems.

Many cellular processes involve information processing and decision-making. We can probe these processes at increasing molecular detail. The analysis of heterogeneous data remains a challenge that requires new ways of thinking about cells in quantitative, predictive, and mechanistic ways. We discuss the role of mathematical models in the context of cell-fate decision-making systems across the tree of life. Complex multicellular organisms have been a particular focus, but single-celled organisms also have to sense and respond to their environment. We center our discussion around the idea of design principles that we can learn from observations and modeling and exploit in order to (re)-design or guide cellular behavior.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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