Maus Carsten, Rybacki Stefan, Uhrmacher Adelinde M
University of Rostock, Institute of Computer Science, Albert-Einstein-Str, 22, 18059 Rostock, Germany.
BMC Syst Biol. 2011 Oct 17;5:166. doi: 10.1186/1752-0509-5-166.
Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax.
Multi-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II.
Rule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.
蛋白质、单个细胞和细胞群体代表了组织层次结构的不同级别，每个级别都有其自身的动态变化。多层次建模关注于在这些不同级别上描述一个系统，并关联它们的动态变化。基于规则的建模由于能够简洁紧凑地描述生化系统而越来越受到关注。此外，它允许使用不同的模型分析方法，因为对于相同的语法可以定义不止一种语义。
多层次建模意味着模型实体的层次嵌套以及对不同级别之间向下和向上因果关系的明确支持。确定了在基于规则的语言中支持多层次建模的概念。这些概念包括规则模式、物种的层次嵌套、为每个级别上的物种分配属性和解决方案，以及在应用规则时保留嵌套物种的内容。进一步的要求是能够应用规则，并灵活定义反应速率动力学以及对嵌套物种和嵌套在其他物种中的物种的约束。给出了一个示例模型，该模型分析了细胞内控制回路与细胞水平状态之间的相互作用、其与细胞分裂的关系以及与细胞群体内细胞间通信的联系。该示例在ML-Rules中进行了描述——ML-Rules是一种基于规则的多层次方法，已在基于插件的建模和仿真框架JAMES II中实现。
基于规则的语言是开发用于细胞生物学系统多层次建模的简洁紧凑语言的合适起点。嵌套物种、分配属性以及根据这些属性约束反应的组合对于实现所需的表达能力至关重要。规则模式允许对复杂模型进行简洁紧凑的描述。因此，所提出的方法有助于开发和维护例如关联细胞内和细胞间动态变化的多层次模型。
