Cancer hybrid automata: Model, beliefs and therapy
Cancer hybrid automata: Model, beliefs and therapy
复制标题
DOI：
10.1016/j.ic.2014.01.013
复制
发表时间：
2014-08-01
影响因子：
1
通讯作者：
Mishra, Bud
中科院分区：
文献类型：
--
作者：
Loohuis, Loes Olde;Witzel, Andreas;Mishra, Bud
本文介绍了癌症混合自动机(CHAS)，这是一种通过离散表型来模拟癌症进展的形式主义。在生物学文献中，使用分期和特征等离散状态对癌症进展进行分类已经很常见，但主要是作为一种组织原则，而不是一种可执行的形式主义。这里开发的精确计算模型旨在开发这一未开发的潜力，即通过自动验证进展模型(例如，一致性、因果关系等)、对不可及或不稳定状态的分类以及计算机生成的(个性化或通用)治疗计划。这篇论文建立在现象学方法的基础上，因此不需要假设潜在的自然进程的生物化学模型。相反，它抽象地模拟状态之间的转换时间以及药物和临床试验的影响，从而允许关于进展的时间陈述以及定时治疗的概念的形式化。这里提出的模型最终是基于混合自动机的，我们展示了如何将现有的控制器综合算法推广到CHA模型，以便自动生成治疗方案。在整篇文章中，我们使用癌症特征来表示癌症进展所经历的离散状态，但其他离散或连续变化状态形式主义的概念也可以用来推导类似的治疗方法。(C)2014年，爱思唯尔公司出版。
This paper introduces Cancer Hybrid Automata (CHAs), a formalism to model the progression of cancers through discrete phenotypes. The classification of cancer progression using discrete states like stages and hallmarks has become common in the biology literature, but primarily as an organizing principle, and not as an executable formalism. The precise computational model developed here aims to exploit this untapped potential, namely, through automatic verification of progression models (e.g., consistency, causal connections, etc.), classification of unreachable or unstable states and computer-generated (individualized or universal) therapy plans. The paper builds on a phenomenological approach, and as such does not need to assume a model for the biochemistry of the underlying natural progression. Rather, it abstractly models transition timings between states as well as the effects of drugs and clinical tests, and thus allows formalization of temporal statements about the progression as well as notions of timed therapies. The model proposed here is ultimately based on hybrid automata, and we show how existing controller synthesis algorithms can be generalized to CHA models, so that therapies can be generated automatically. Throughout this paper we use cancer hallmarks to represent the discrete states through which cancer progresses, but other notions of discretely or continuously varying state formalisms could also be used to derive similar therapies. (C) 2014 Published by Elsevier Inc.