Computational modelling of biochemical systems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illustrating how to generate atomic components by a set of given reactants and two user pre-defined component patterns, we propose an integrative top-down and bottom-up modelling approach for stepwise qualitative exploration of interactions among reactants in biochemical systems. Evolution strategy is applied to the top-down modelling approach to compose models, and simulated annealing is employed in the bottom-up modelling approach to explore potential interactions based on models constructed from the top-down modelling process. Both the top-down and bottom-up approaches support stepwise modular addition or subtraction for the model evolution. Experimental results indicate that our modelling approach is feasible to learn the relationships among biochemical reactants qualitatively. In addition, hidden reactants of the target biochemical system can be obtained by generating complex reactants in corresponding composed models. Moreover, qualitatively learned models with inferred reactants and alternative topologies can be used for further web-lab experimental investigations by biologists of interest, which may result in a better understanding of the system.

中文翻译：

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用于探索生化系统的综合自上而下和自下而上的定性模型构建框架。

在过去十年中，基于自上而下和自下而上方法的生化系统计算建模得到了很好的研究。在这项研究中，在说明了如何通过一组给定的反应物和两个用户预定义的组分模式生成原子组分之后，我们提出了一种集成的自顶向下和自底向上的建模方法，用于逐步定性地探索生化系统中反应物之间的相互作用. 自顶向下建模方法采用演化策略组成模型，自底向上建模方法采用模拟退火，基于自顶向下建模过程构建的模型探索潜在相互作用。自上而下和自下而上的方法都支持模型演化的逐步模块化加法或减法。实验结果表明，我们的建模方法可以定性地学习生化反应物之间的关系。此外，通过在相应的组合模型中生成复杂的反应物，可以获得目标生化系统的隐藏反应物。此外，具有推断反应物和替代拓扑的定性学习模型可用于感兴趣的生物学家进一步的网络实验室实验研究，这可能会更好地理解系统。