A contradiction-based framework for testing gene regulation hypotheses

S. Racunas, N. Shah, N. Fedoroff
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引用次数: 5

Abstract

We have developed a mathematical framework for representing and testing hypotheses about gene, protein, and signaling molecule interactions. It takes a hierarchical, contradiction-based approach, and can make use of multiple data sources to assess hypothesis viability and to generate a viability partial order over the space of hypotheses. We have developed an event-based formal language for the expression of such hypotheses. This language seamlessly integrates regulatory diagrams (graphical inputs) and structured English (text input) to maximize flexibility. We have developed a pre-topological formalism that allows us to make precise statements about hypothesis similarity and the convergence of iterative refinements of a base hypothesis. To this, we add mathematical machinery that allows us to make precise statements about control and regulation.
基于矛盾的基因调控假设测试框架
我们已经开发了一个数学框架来表示和测试关于基因、蛋白质和信号分子相互作用的假设。它采用分层的、基于矛盾的方法,可以利用多个数据源来评估假设的可行性,并在假设空间上生成可行性偏序。我们已经开发了一种基于事件的形式语言来表达这些假设。这种语言无缝地集成了规则图(图形输入)和结构化英语(文本输入),以最大限度地提高灵活性。我们已经开发了一种前拓扑形式,它允许我们对假设的相似性和基本假设的迭代改进的收敛性做出精确的陈述。在此基础上,我们添加了数学机制,使我们能够对控制和调节做出精确的陈述。
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