Evolving cell models for systems and synthetic biology.
Evolving cell models for systems and synthetic biology.
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DOI：
10.1007/s11693-009-9050-7
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发表时间：
2010-03
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影响因子：
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通讯作者：
Krasnogor, Natalio
中科院分区：
文献类型：
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作者：
Cao, Hongqing;Romero-Campero, Francisco J;Heeb, Stephan;Camara, Miguel;Krasnogor, Natalio
本文提出了一种用于系统和合成生物学细胞模型自动设计的新方法。我们的建模框架基于 P Systems，一种离散、随机和模块化的形式建模语言。使用进化算法执行生物模型的自动化设计，包括模型结构及其随机动力学常数的优化。进化算法通过组合来自预定义模块库的不同模块来进化模型结构，然后微调相关的随机动力学常数。我们研究了进化算法中适应度计算的四种替代目标函数：（1）等权重和法，（2）归一化法，（3）随机权重和法，以及（4）等权重乘积法。该方法的有效性在四个日益复杂的案例研究中进行了测试，包括负向和正向自动调节以及实现脉冲发生器和带宽检测器的两个基因网络。我们对进化算法的结果以及由此产生的进化细胞模型进行了系统分析。
This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models.