Boolean modelling of biochemical networks

生物系统的研究，从细胞到有机体和种群，正变得越来越定量。特别是，描述生物化学网络的方式正在从传统的可能相互作用的图解草图转变为一组数学方程，模拟（即模型）每种分子物种的浓度如何随时间变化。数学模型的一个关键优势是，它们可以用来预测网络对外部扰动的反应，例如环境条件的变化或药理学试剂的添加。这减少了对大量昂贵且耗时的实验的需求。然而，生化网络模型越复杂，它可能表现出的动态行为范围就越大。因此，为了获得实验行为的准确预测，大量的计算机模拟是必要的。对于可以包含数百种分子物种的生物学现实模型，所需的模拟数量远远超过在实际时间尺度上可能实现的模拟数量。因此，为了充分实现生物学中数学模型的预测能力，迫切需要一种方法，使它们能够以一种计算高效的方式全面探索它们的行为。拟议的项目将通过开发一种基于将生化网络表示为数字电路的新建模方法来解决这一需求。在这种方法中，每个物种被认为是“开”（即存在）或“关”（即不存在），浓度的变化被简单地视为这两种状态之间的过渡。由此带来的计算复杂性的显著降低，有可能极大地扩展可有效建模的网络范围。作为该方法的测试案例，将构建一个重要植物物种拟南芥（Arabidopsis thaliana）中产生昼夜节律振荡的基因网络的数字电路模型。昼夜节律振荡调节着许多对植物生长和繁殖至关重要的过程，如光合作用和种子萌发。作为这项工作的一部分，将利用数字电路模型的计算可追溯性来确定温度如何改变植物的昼夜节律网络。从长远来看，这可能对预测未来温度变化将如何影响重要作物物种的生存能力很重要，从而为培育对气候变化抵抗力增强的品种提供信息。

The study of biological systems, from cells, to organisms and populations, is becoming increasingly quantitative. In particular, the way that biochemical networks are described is changing from the traditional diagrammatic sketch of possible interactions to a set of mathematical equations that simulate (i.e. model) how the concentration of each molecular species varies with time. A key advantage of mathematical models is that they can be used to predict the response of networks to external perturbations, such as changes in environmental conditions or the addition of pharmacological agents. This reduces the need for large numbers of expensive and time-consuming experiments. However, the more complex a biochemical network model, the greater the range of possible dynamic behaviours it can exhibit. Consequently, extensive computer simulations are necessary for accurate predictions of experimental behaviour to be obtained. For biologically realistic models that can comprise hundreds of molecular species, the number of simulations required far exceeds that which is possible on a practical timescale. It follows that for the predictive power of mathematical models in biology to be fully realised, there is a pressing need for methods that allow their behaviour to be comprehensively explored in a computationally efficient manner. The proposed project will address this need by developing a new modelling methodology based on representing biochemical networks as digital circuits. In this approach, each species is considered to be either "on" (i.e. present) or "off" (i.e. absent), and changes in concentration are simply treated as transitions between these two states. The significant reduction in computational complexity that this affords has the potential to greatly expand the range of networks that can be usefully modelled. As a test case of the approach, digital circuit models will be constructed of the gene network that generates circadian oscillations in an important plant species, Arabidopsis thaliana. Circadian oscillations regulate many processes critical for plant growth and reproduction, such as photosynthesis and seed germination. As part of this work, the computational tractability of the digital circuit models will be exploited to determine how temperature modifies the plant circadian network. In the long term, this may prove important for predicting how future temperature shifts will affect the viability of important crop species, thereby informing strategies for breeding varieties with increased resistance to climate change.