SIGNAL: Stochastic process algebra for biochemical signalling pathway analysis

计算系统生物学使用计算机模拟生物体来帮助我们理解那些我们无法直接看到或测量的内部工作过程。基于实验确定的数据，系统生物学家发明了一个他们认为事物如何工作的模型。该模型通常是由两种计算机模拟分析之一：随机或确定性。在随机模拟中，概率的数学理论被用来表达关于反应发生的速度或反应物的使用量的不确定性.在确定性模拟中，常微分方程理论被用来给出大量分子元素的有效连续近似。计算建模在这里是有帮助的，因为基于实验室的实验是一个极其昂贵，耗时和劳动密集型的过程。信号转导是计算机模拟的一个重要课题，信号转导通路是细胞感受刺激并将信号传递给细胞核，从而做出适当反应的生化通路。它们是具有内置反馈机制的复杂信号过程。信号通路嵌入在更大的网络中，并参与重要的过程，如增殖，细胞生长，运动，细胞通讯和程序性细胞死亡（凋亡）。功能失常会导致许多疾病，包括癌症、糖尿病和许多其他疾病。尽管近年来实验研究取得了巨大的进展，但仍然缺乏良好的、可预测的通路模型来指导实验和药物开发。到目前为止，模型要么encodestatic方面，如蛋白质有可能相互作用，或提供模拟系统动力学使用ordinary differentialequations.We将开发一种新的方法，分析pathway modeling basedon我们的经验，并行计算系统的建模。其核心思想是，路径具有随机性，计算性的内容。我们将使用随机过程代数表示连续时间马尔可夫链，从而提供新的定量分析和新的方法来结构路径和原因的不完全行为的途径建模.

The science of computational Systems Biology uses computer modellingof living organisms to help us understand those process at work insidewhich we cannot directly see or measure. Based on experimentallydetermined data, a systems biologist invents a model of how they thinkthat things work. The model is usually analysed by one of two kindsof computer simulation: stochastic or deterministic. In a stochasticsimulation the mathematical theory of probability is used to express adegree of uncertainty about how fast reactions happen or thequantities of reactants which are in use. In a deterministicsimulation the theory of ordinary differential equations is used togive an efficient continuous approximation of very large numbers ofmolecular elements. Computational modelling is helpful here becauselaboratory-based experimentation is an extremely expensive,time-consuming and labour-intensive process. An important subject forcomputational modelling is signal transduction.Signal transduction pathways are biochemical pathways which allowcells to sense a stimulus and communicate a signal to the nucleus,which then makes a suitable response. They are complicated signallingprocesses with built-in feedback mechanisms. Signalling pathways areembedded in larger networks and are involved in important processessuch as proliferation, cell growth, movement, cell communication, andprogrammed cell death (apoptosis). Malfunction results in a largenumber of diseases including cancer, diabetes and many others. Despiteenormous experimental advances in recent years there is still anabsence of good, predictive pathway models which can guideexperimentation and drug development. To date, models either encodestatic aspects such as which proteins have the potential to interact,or provide simulations of system dynamics using ordinary differentialequations.We will develop a novel approach to analytic pathway modelling basedon our experience of modelling concurrent computing systems. The keyidea is that pathways have stochastic, computational content. We willmodel pathways using stochastic process algebras which denotecontinuous time Markov chains thus affording new quantitative analysisand new ways to structure pathways and reason about incompletebehaviour.