具有表观遗传控制的进化布尔调控网络

Coevolving Boolean and Multi-Valued Regulatory Networks

Random Boolean networks have been used widely to explore aspects of gene regulatory networks. A modified form of the model through which to systematically explore the effects of increasing the number of gene states has previously been introduced. In this paper, these discrete dynamical networks are coevolved within coupled, rugged fitness landscapes to explore their behaviour. Results suggest the general properties of the Boolean model remain with higher valued logic regardless of the update scheme or fitness sampling method. Introducing topological asymmetry in the coevolving networks is seen…

Evolving Boolean Regulatory Networks with Variable Gene Expression Times

The time taken for gene expression varies not least because proteins vary in length considerably. This paper uses an abstract, tuneable Boolean regulatory network model to explore gene expression time variation. In particular, it is shown how non-uniform expression times can emerge under certain conditions through simulated evolution. That is, gene expression time variance appears beneficial in the shaping of the dynamical behaviour of the regulatory network without explicit consideration of protein function.

遗传调控网络中蛋白质复合物的产生式系统规则

Production System Rules as Protein Complexes from Genetic Regulatory Networks

本文介绍了一种设计产生式系统规则的新方法。一个间接的编码方案，提出了这样的规则，作为蛋白质复合物产生的人工遗传调控网络的时间行为。这项初步研究首先使用一个简单的布尔调节网络，以产生传统的三进制编码的规则，然后移动到一个模糊的变体，以产生实值规则。竞争力的表现与相关的基因调控网络和基于规则的系统的基准问题。

Evolving Boolean Networks with RNA Editing

The editing of transcribed RNA by other molecules such that the form of the final product differs from that specified in the corresponding DNA sequence is ubiquitous. This paper uses an abstract, tunable Boolean genetic regulatory network model to explore aspects of RNA editing. In particular, it is shown how dynamically altering expressed sequences via a guide RNA-inspired mechanism can be selected for by simulated evolution under various single and multicellular scenarios.

随机布尔网络中吸引子距离的分析

Analysis of attractor distances in Random Boolean Networks

我们研究了随机布尔网络中吸引子之间的距离的性质，随机布尔网络是遗传调控网络的一个突出模型。我们定义了三种距离测度，在此基础上构造了吸引子距离矩阵，并计算了它们的主要统计参数。实验分析表明，有序网络有一个非常聚集的吸引子集，而混沌网络的吸引子是分散的;关键网络显示，相反，一个模式的特点，有序和混沌网络。