Data integration in logic-based models of biological mechanisms
Data integration in logic-based models of biological mechanisms
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DOI：
10.1016/j.coisb.2021.100386
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发表时间：
2021-12-01
影响因子：
3.7
通讯作者：
Niarakis, Anna
中科院分区：
文献类型：
--
作者：
Hall, Benjamin A.;Niarakis, Anna
离散的、基于逻辑的模型越来越多地用于描述生物机制。这些模型最初是为了研究基因调控而引入的，后来发展到涵盖了各种分子机制，如信号传导、转录因子协同性甚至代谢过程。离散模型的抽象性和对强大的数学分析的顺从性使它们适合于解决广泛的复杂生物学问题。最近的技术突破产生了丰富的高通量数据。新颖的，基于文献的生物过程和新兴算法的表示提供了新的机会，模型构建。在这里，我们回顾了最新的努力，以解决具有挑战性的生物学问题，将组学数据纳入基于逻辑的模型，并讨论了关键的困难，在构建和分析综合的，大规模的，基于逻辑的模型的生物机制。
Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signaling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high-throughput data. Novel, literature-based representations of biological processes and emerging algorithms offer new opportunities for model construction. Here, we review up-to-date efforts to address challenging biological questions by incorporating omic data into logic-based models and discuss critical difficulties in constructing and analyzing integrative, large-scale, logic-based models of biological mechanisms.