延迟遗传调控网络稳定性及分支

本文主要研究了延迟遗传调控网络的局部稳定性和该网络的Hopf分支存在条件．延迟遗传调控网络是无穷维系统,此类系统在平衡点线性化后的特征方程为超越方程。通过对此超越方程进行研究,得到了系统系数不同时的系统稳定的条件及相关结论,又进一步说明了此系统的Hopf分支存在条件．最后,举一个例子进行了数值仿真验证了所得到的结论．     

基于Petri网的基因调控网络建模与分析

如何有效描述与分析复杂的基因调控网络(GRN)是生物学家研究基因表达调控机制的关键步骤.现有大部分方法在建模过程中忽略了生物中广泛存在的协同作用,模型预测结果与实际生物行为之间存在误差.基于混合函数Petri网(HFPN)理论提出了一种对基因调控网络进行定量分析的新方法.首先简要介绍GRN与HFPN的基础理论,然后为HFPN引入两类新元素:逻辑库所与逻辑变迁,描述基因调控网络的逻辑规则以及转录因子间的协同作用,最后构建海胆endo16基因调控网络的Petri网模型,并预测模型在不同位点发生突变时的基因表达水平变化.分析结果与文献实验数据相一致,验证了方法的正确性.     

Genetic Causation in Complex Regulatory Systems: An Integrative Dynamic Perspective

The logic of genetic discovery has changed little over time, but the focus of biology is shifting from simple genotype–phenotype relationships to complex metabolic, physiological, developmental, and behavioral traits. In light of this, the traditional reductionist view of individual genes as privileged difference-making causes of phenotypes is re-examined. The scope and nature of genetic effects in complex regulatory systems, in which dynamics are driven by regulatory feedback and hierarchical interactions across levels of organization are considered. This review argues that it is appropriate to treat genes as specific actual difference-makers for the molecular regulation of gene expression. However, they are often neither stable, proportional, nor specific as causes of the overall dynamic behavior of regulatory networks. Dynamical models, properly formulated and validated, provide the tools to probe cause-and-effect relationships in complex biological systems, allowing to go beyond the limitations of genetic reductionism to gain an integrative understanding of the causal processes underlying complex phenotypes.     

基于GES机制遗传算法构建基因调控网络

目的：基因调控网络在药物研发与疾病防治方面有重要的生物学意义。目前基于芯片数据构建网络的方法普遍效率不高,准确度较低,为此提出了一种新的高效调控网络结构预测算法。方法：提出了一种基于贪婪等价搜索机制的遗传算法构建基因调控网络模型。通过引入遗传算法的多点并行性,使得算法易于摆脱局部最优。通过编码网络结构作为遗传算法的染色体和设计基于GES机制的变异算子,使网络的进化过程基于马尔科夫等价空间而不是有向无环图空间。结果：通过对标准网络ASIA和酵母调控网络的预测,与近期Xue-wen Chen等提出的Order K2算法进行了比较,在网络构建准确率上获得了更佳的结果。与标准遗传算法比较下在执行效率上大大提高。结论：提出的算法在网络结构预测准确率上相对于最近提出的Order K2算法在准确率上效果更佳,并且相较标准遗传算法网络在进化过程上效率更高。     

From Genes to Flower Patterns and Evolution: Dynamic Models of Gene Regulatory Networks

Genes and proteins form complex dynamical systems or gene regulatory networks (GRN) that can reach several steady states (attractors). These may be associated with distinct cell types. In plants, the ABC combinatorial model establishes the necessary gene combinations for floral organ cell specification. We have developed dynamic gene regulatory network (GRN) models to understand how the combinatorial selection of gene activity is established during floral organ primordia specification as a result of the concerted action of ABC and non-ABC genes. Our analyses have shown that the floral organ specification GRN reaches six attractors with gene configurations observed in primordial cell types during early stages of flower development and four that correspond to regions of the inflorescence meristem. This suggests that it is the overall GRN dynamics rather than precise signals that underlie the ABC model. Furthermore, our analyses suggest that the steady states of the GRN are robust to random alterations of the logical functions that define the gene interactions. Here we have updated the GRN model and have systematically altered the outputs of all the logical functions and addressed in which cases the original attractors are recovered. We then reduced the original three-state GRN to a two-state (Boolean) GRN and performed the same systematic perturbation analysis. Interestingly, the Boolean GRN reaches the same number and type of attractors as reached by the three-state GRN, and it responds to perturbations in a qualitatively identical manner as the original GRN. These results suggest that a Boolean model is sufficient to capture the dynamical features of the floral network and provide additional support for the robustness of the floral GRN. These findings further support that the GRN model provides a dynamical explanation for the ABC model and that the floral GRN robustness could be behind the widespread conservation of the floral plan among eudicotyledoneous plants. Other aspects of evolution of flower organ arrangement and ABC gene expression patterns are discussed in the context of the approach proposed here. álvaro Chaos, Max Aldana and Elena Alvarez-Buylla contributed equally to this work.     

乳腺癌转移相关基因表达调控网络的线性微分方程模型的初步研究

初步构建乳腺癌转移相关基因表达调控网络的线性微分方程模型,并分析模型的可靠性和生物学意义. 采用基因芯片技术,分别对30例伴有淋巴结转移的乳腺癌组织及其相应淋巴结转移癌组织进行基因表达谱的比较,选择差异基因通过线性微分数学方法构建表达调控网络模型. 差异表达基因共27个,其中Ratio > 3的明显上调基因14个,而Ratio < 0.33的明显下调基因13个. 比较伴有淋巴结转移的乳腺癌组织和其相应淋巴结转移癌组织,分析筛选了27个表达差异基因,应用数学线性微分方程方法初步构建乳腺癌转移相关基因表达调控网络的线性微分方程模型,通过分析模型中重要节点、通路的生物学意义,判定网络的数学特性,初步表明,调控网络的可靠性和乳腺癌转移的形成是与多基因、多通路异常引起的细胞恶性转化相关.     

具常数时滞细胞神经网络概周期解

利用矩阵不等式的分析技巧和Banach空间中不动点定理,得到了具常数时滞细胞神经网络概周期解的存在性、唯一性和全局指数稳定性,推广和改进了已有文献的结论。     

Differential Expression of microRNAs and their Regulatory Networks in Skin Tissue of Liaoning Cashmere Goat during Hair Follicle Cycles

MicroRNAs (miRNAs) are endogenous small noncoding RNA molecules that negatively regulate gene expression. Herein, we investigated a selective number of miRNAs for their expression in skin tissue of Liaoning Cashmere goat during hair follicle cycles, and their intracellular regulatory networks were constructed based on bioinformatics analysis. The relative expression of six miRNAs (mir-103-3p, -15b-5p, 17-5p, -200b, -25-3p, and -30c-5p) at anagen phase is significantly higher than that at catagen and/or telogen phases. In comparison to anagen, the relative expression of seven miRNAs (mir-148a-3p, -199a-3p, -199a-5p, -24-3p, -30a-5p, -30e-5p, and -29a-3p) was revealed to be significantly up-regulated at catagen and/or telogen stages. The network analyses of miRNAs indicated those miRNAs investigated might be directly or indirectly involved in several signaling pathways through their target genes. These results provided a foundation for further insight into the roles of these miRNAs in skin tissue of Liaoning Cashmere goat during hair follicle cycles.     

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data isvery important to understand the underlying biological system,and it has been a challenging task in bioinformatics.TheBayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determinethe network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithmwhich integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use ofboth simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of theknown real regulatory relationships from literature and predict the others unknown with high validity and accuracy.     

带脉冲变系数的BAM神经网络的全局指数稳定性

在固定脉冲时刻,利用无需有界、单调和可微的李普希茨激励函数,来研究BAM脉冲神经网络,获得平衡点的存在唯一性和全局指数稳定性的充分条件,然后通过举例来验证所得结论的有效性．     

具时滞的Hopfield型神经网络模型的全局渐近稳定性

本文研究了具时滞的Hopfield型神经网络模型平衡点的全局渐近稳定性,获得了一系列充分条件。     

‘神马’菊花的离体保存及遗传稳定性

用添加不同蔗糖与甘露醇配比的培养基对切花菊品种‘神马’(Jinba)试管苗进行离体保存,并对保存材料再生后代的遗传稳定性进行了研究。结果表明,在温度(23±2)℃、光照强度2 000~3 000 lx、光照时间12 h/d的培养条件下,MS 0.3 mg.L-16-BA 0.1 mg.L-1NAA培养基中分别添加10 g.L-1蔗糖和10 g.L-1甘露醇、10 g.L-1蔗糖和20 g.L-1甘露醇、20 g.L-1蔗糖和20 g.L-1甘露醇或30 g.L-1蔗糖和10 g.L-1甘露醇均能使菊花试管苗连续保存12个月,其中10 g.L-1蔗糖和20 g.L-1甘露醇、20 g.L-1蔗糖和20 g.L-1甘露醇组合处理保存12个月后成活率较高,分别达到50.00%和60.00%,且均保持最高的增殖倍数2.67。保存12个月的试管苗在增殖、生根培养基上均能正常恢复生长,且其再生后代的田间生物学性状、过氧化物酶(POD)及酯酶(EST)同工酶酶谱、ISSR扩增图谱与对照株无差异,保持了良好的遗传稳定性,说明利用上述方法保存‘神马’种质是可行的。     

植物种质超低温保存遗传稳定性的研究进展

超低温保存被认为是种质长期保存最有效的方法,其中生物材料低温保存的遗传稳定性是植物种质资源保存中最受关注的问题之一。本文对近年来超低温保存后植物材料的遗传稳定性及变异的研究情况进行了介绍,涉及表型性状分析、基因组遗传稳定性、表观遗传变化及超低温保存的筛选效应等,为进一步研究超低温保存的应用提供参考。     

基于神经网络和遗传算法的木糖醇发酵培养基优化研究

发酵过程机理复杂、影响因素众多。菌种的生理生化特性及发酵的工艺确定之后 ,适宜的培养基配方成了发酵水平、原料成本高低的决定因素。为了优化培养基配方 ,采用遗传算法是一种行之有效的方法。遗传算法 (ＧＡ)是基于达尔文进化论和孟德尔遗传学说来实现随机、自适应、并行性全局搜索的一种无须数学模型的优化算法。与其它搜索方法相比 ,ＧＡ的优越性主要有 :(1)在搜索过程中ＧＡ不易陷入局部最优 ,即使所定义的目标函数非连续、不规则或伴有噪声 ,它也能以很大的概率找到全局最优解 ;(2 )由于ＧＡ固有的并行性 ,使得它非常适合于大规模并…     

变时延神经网络的全局指数稳定性及收敛性

利用Lyapunov泛函方法和线性矩阵不等式（LMI）技术,通过引入一系列参数,给出全局指数稳定的平衡点的判别条件和时延的最大上界和神经网络的收敛速度,所得结果较之一些文献中的结果简单、实用并且对于具体设计带时延神经网络有重要的指导意义.最后,通过实例表明给出的判定条件是有效、可行的.     

具有时滞的双向联想记忆神经网络的全局渐近稳定性

双向联想记忆模型是两层异联想网络,本文讨论了具有轴突信号传输时滞的双向联想记忆神经网络的全局渐近稳定性,得出了保证神经网络平衡点稳定的几个充分条件,所得到的结论对于具有时滞的连续双向联想记忆神经网络的设计和应用都是很有意义的。