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用于系统生物学和合成生物学的不断发展的细胞模型。

Evolving cell models for systems and synthetic biology.

作者信息

Cao Hongqing, Romero-Campero Francisco J, Heeb Stephan, Cámara Miguel, Krasnogor Natalio

出版信息

Syst Synth Biol. 2010 Mar;4(1):55-84. doi: 10.1007/s11693-009-9050-7. Epub 2010 Jan 22.

DOI:10.1007/s11693-009-9050-7
PMID:20186253
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2816226/
Abstract

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.

摘要

本文提出了一种用于系统生物学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系统，这是一种离散、随机且模块化的形式化建模语言。使用进化算法进行生物模型的自动设计，包括模型结构及其随机动力学常数的优化。进化算法通过组合从预定义模块库中选取的不同模块来演化模型结构，然后对相关的随机动力学常数进行微调。我们研究了进化算法中用于适应度计算的四种替代目标函数：（1）等加权和方法，（2）归一化方法，（3）随机加权和方法，以及（4）等加权积方法。该方法的有效性在四个复杂度不断增加的案例研究中进行了测试，包括负反馈和正反馈自动调节，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演化细胞模型进行了系统分析。

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