ADAM：用计算机代数分析生物系统的离散模型
ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra
摘要：
背景资料：许多生物系统的定性建模与离散模型，如概率布尔网络，逻辑模型，Petri网，和基于代理的模型，以获得更好的理解系统的目标。分析这些模型的完整动力学的计算复杂性在变量的数量上呈指数增长，这阻碍了复杂模型的工作。虽然存在复杂的算法来确定离散模型的动态，但它们的实现通常需要模型公式化的劳动密集型格式化，并且它们通常不被没有编程技能的用户访问。需要有效的分析方法，建模者可以访问和易于使用。方法：通过将离散模型转化为代数模型，利用计算代数的工具对其进行动力学分析。具体来说，我们提出了一种方法来识别吸引子的离散模型，这是相当于解决一个系统的多项式方程，一个长期研究的问题，在计算机代数。结果如下：一种有效识别吸引子的方法，以及基于网络的工具动态代数模型分析（ADAM），它为离散模型提供了这种和其他分析方法。ADAM将几种离散模型类型自动转换为多项式动力系统，并使用计算机代数工具分析其动力学。基于对系统生物学中出现的离散模型和随机生成的网络的广泛实验，我们发现本文中提出的代数算法对于具有大多数生物系统所保持的结构的系统是快速的，即稀疏性，即，虽然生物网络中的节点数量可能相当大，但是每个节点仅受少量其他节点的影响，并且鲁棒性，即，少量的吸引子。
英文摘要：
Background: Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, with the goal to gain a better understanding of the system. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. Although there exist sophisticated algorithms to determine the dynamics of discrete models, their implementations usually require labor-intensive formatting of the model formulation, and they are oftentimes not accessible to users without programming skills. Efficient analysis methods are needed that are accessible to modelers and easy to use. Method: By converting discrete models into algebraic models, tools from computational algebra can be used to analyze their dynamics. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Results: A method for efficiently identifying attractors, and the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness, i.e., while the number of nodes in a biological network may be quite large, each node is affected only by a small number of other nodes, and robustness, i.e., small number of attractors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
