From species to pathway and tissue as process
From species to pathway and tissue as process
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从物种到途径和组织作为过程
DOI：
10.1145/1839764.1839765
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Calder M
中科院分区：
文献类型：
--
作者：
Calder M
进程代数最初是为模拟并发计算而设计的。在过去的十年中，计算机科学家已经探索了它们在生物分子过程建模中的应用，并取得了相当大的成功。一个主要的抽象是分子作为过程[RSS 01，Car 08]，其中每个过程代表一个分子。分析是通过模拟和随机设置，有一个明确的对应关系，随机模拟提出的吉莱斯皮[吉尔77]。另一种抽象是物种作为过程[CGH 06，CH 09 b]，基于模型，是连续时间马尔可夫链（CTMC）的浓度水平。这种基于人口的抽象允许控制表示的粒度，在对应于吉莱斯皮模拟的频谱的一端，在另一端，常微分方程。这种风格的一个关键特征是，除了模拟之外，它还允许一系列分析技术，即关系（例如互模拟）和模型检查属性表示在定性和定量逻辑。在thepeces-as-processparadigm中，一个有用的风格是试剂中心模型[CH 09 a]，其中反应中的所有试剂映射到过程，其变化反映了通过消费减少和通过产品形成（消费者和生产者）增加。以试剂为中心的建模风格提供了系统的分布式视图，并且很容易以基于状态的形式主义表示，其中状态变量表示浓度水平。一个例子是PRISM模型检查器[KNP 02]中使用的反应模块语言。虽然这种语言不是严格的进程代数：进程由模块表示，但模块之间存在进程代数同步。此外，模块可以是通用的。本次演讲概述了以代理为中心的建模范式的最新进展和应用，扩展了关于浓度水平的基本推理，然后发展了更高层次的概念，如路径作为过程和组织作为过程。我们考虑如何通过增加趋势公式，代表浓度上升或下降趋势的状态公式来扩展关于浓度水平的基本推理[AC 10]。这些类似于一阶导数的符号，但在随机设置中。然后，我们考虑扩展thespecies-as-processparadigm topathway-as-process.While仍然采用试剂为中心的风格，我们建模的信号通路作为一个（同步）并行组合（与重命名）的通用模块的实例，其中既有内部和外部的反应。其动机是研究通路相互作用，称为串扰，因此通路本身是组成的。我们展示了如何使用定量逻辑来检测串扰，以及定性逻辑来确定串扰类型[DC 10 b]。最后，我们描述了一个新的随机过程代数建模不同层次的抽象，特别是生物化学和组织。该代数的动机是基于反应扩散方程的模式形成建模。过程既代表生物化学物种，也代表特定位置的组织;几何空间的明确概念嵌入在代数中。两个级别之间的同步是通过称为钩子的特殊操作[DC 10a]实现的。最终目标是能够比较类似组织形成的模型，但具有不同的基础生物化学。
Process algebras were originally designed for modelling concurrent computations. Over the last decade, computer scientists have explored their application to modelling bio-molecular processes, with considerable success. A predominant abstraction ismolecule-as-process[RSS01, Car08], where each process represents a molecule. Analysis is by simulation and in a stochastic setting, there is a clear correspondence with stochastic simulation as proposed by Gillespie [Gil77].An alternative abstraction isspecies-as-process[CGH06, CH09b], based on models that are continuous time Markov chains (CTMC) with levels of concentration. This population-based abstraction allows control of the granularity of representation, at one end of the spectrum corresponding to Gillespie simulation and at the other end, ordinary differential equations. A key feature of this style is it permits a range of analysis techniques in addition to simulation, namely relations (e.g. bisimulation) and model-checking properties expressed in qualitative and quantitative logics.Within thespecies-as-processparadigm, a useful style has beenreagent-centricmodels[CH09a], where all reagents in a reaction map to processes, whose variation reflect decrease through consumption and increase through product formation (consumers and producers). The reagent-centric style of modelling provides a distributed view of a system and is easily represented in a state-based formalism where state variables represent levels of concentration. An example is the language of reactive modules used in the PRISM model-checker [KNP02]. Whilst this language is not strictly a process algebra: processes are represented by modules, there is process algebraic synchronisation between modules. Moreover, modules can be generic.This talk gives an overview of recent advances and applications of thereagent-centricmodelling paradigm, extending basic reasoning about concentration levels and then developing higher level concepts such aspathway-as-processandtissue-as-process.We consider how to extend basic reasoning about concentration levels by the addition oftrend formulas, state formulas that represent ascending or descending trends of concentration [AC10]. These are similar to the sign of a first-order derivative, but in a stochastic setting. We then consider extending thespecies-as-processparadigm topathway-as-process.While still adopting the reagent-centric style, we model a signalling pathway as a (synchronising) parallel composition (with renaming) of instances of generic modules, which have both internal and external reactions. The motivation is to investigate pathway interactions, known as crosstalk, and so pathways are themselves composed. We show how we can use a quantitative logic to detect cross-talk, and a qualitative logic to characterise the type of crosstalk [DC10b]. Finally, we describe a new stochastic process algebra for modelling different levels of abstraction, specifically biochemistry and tissue. The algebra is motivated by modelling pattern formation based on reaction-diffusion equations. Processes represent both biochemical species and tissues at certain locations; an explicit notion of geometrical space is embedded in the algebra. Synchronisation between the two levels is through special actions called hooks [DC10a]. The ultimate goal is to be able to compare models of similar tissue formation, but with different underlying biochemistry.
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DOI：
10.1016/j.entcs.2010.12.002
发表时间：
2010-12
期刊：
--
影响因子：
--
作者：
Oana Andrei;Muffy Calder
通讯作者：
Oana Andrei;Muffy Calder
DOI：
10.1142/9789814447362_0045
发表时间：
2000-12
影响因子：
--
作者：
A. Regev;William Silverman;E. Shapiro
通讯作者：
A. Regev;William Silverman;E. Shapiro
带有模式形成模型钩子的过程代数
DOI：
10.1016/j.entcs.2010.12.004
发表时间：
2010
期刊：
Trans. Comp. Sys. Biology
影响因子：
--
作者：
A. Degasperi;Muffy Calder
通讯作者：
Muffy Calder
影响因子：
3
作者：
Yang X;Han R;Guo Y;Bradley J;Cox B;Dickinson R;Kitney R
通讯作者：
Kitney R
影响因子：
--
作者：
Donaldson R
通讯作者：
Donaldson R