SIGNAL: Stochastic process algebra for biochemical signalling pathway analysis

计算系统生物学使用活的生物体的计算机模型来帮助我们理解那些我们不能直接看到或测量的在内部工作的过程。基于实验确定的数据，一位系统生物学家发明了一个他们认为事物是如何工作的模型。该模型通常通过两种计算机模拟中的一种进行分析：随机的或确定性的。在随机模拟中，概率的数学理论被用来表示关于反应发生的速度或正在使用的反应物数量的不确定程度。在确定性模拟中，常微分方程组的理论被用来对大量的分子元素进行有效的连续近似。计算模型在这里是有帮助的，因为基于精细的实验是一个极其昂贵、耗时和劳动密集型的过程。计算建模的一个重要课题是信号转导。信号转导通路是一种生化通路，它允许细胞感知刺激，并将信号传递给细胞核，然后细胞核做出适当的反应。它们是带有内置反馈机制的复杂信号传递过程。信号通路嵌入在更大的网络中，参与重要的过程，如增殖、细胞生长、运动、细胞通讯和细胞程序性死亡(细胞凋亡)。故障会导致大量疾病，包括癌症、糖尿病和许多其他疾病。尽管近年来取得了巨大的实验进展，但仍然缺乏良好的、可预测的途径模型来指导实验和药物开发。到目前为止，模型要么编码了哪些蛋白质可能相互作用的方面，要么使用普通的微分方程式提供了系统动力学的模拟。我们将根据我们对并发计算系统建模的经验开发一种新的分析路径建模方法。关键思想是，路径具有随机的、可计算的内容。我们将使用非连续时间马尔可夫链的随机过程代数对路径进行建模，从而为构建路径和不完全行为的原因提供新的定量分析和新的途径。

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.