考虑生物生长过程中受到的不可预知的跳跃性的环境扰动,运用一类非高斯噪声建立了随机的基因转录调控系统. 利用 Monte Carlo 法得到了系统的稳态概率密度函数,研究了非高斯噪声的各个参数对蛋白质浓度的影响,发现噪声强度不能够诱导基...考虑生物生长过程中受到的不可预知的跳跃性的环境扰动,运用一类非高斯噪声建立了随机的基因转录调控系统. 利用 Monte Carlo 法得到了系统的稳态概率密度函数,研究了非高斯噪声的各个参数对蛋白质浓度的影响,发现噪声强度不能够诱导基因开关,而稳定性指标和偏斜参数能够作为基因开关的控制参量. 进一步研究了非高斯噪声作用下系统从一个态跃迁到另一个态的平均首通时间(MFPT) ,并讨论了各个参数不同的作用机理.展开更多
Modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using t...Modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using the discrete dynamic system （DDS） model approach. Although considerable research has been devoted to building GRNs, many of the works did not consider the time-delay effect. Here, the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes. The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed genetic network using linear multiple regression matches with the original data very well. Simulation results are given to demonstrate the effectiveness of the proposed method and model.展开更多
文摘考虑生物生长过程中受到的不可预知的跳跃性的环境扰动,运用一类非高斯噪声建立了随机的基因转录调控系统. 利用 Monte Carlo 法得到了系统的稳态概率密度函数,研究了非高斯噪声的各个参数对蛋白质浓度的影响,发现噪声强度不能够诱导基因开关,而稳定性指标和偏斜参数能够作为基因开关的控制参量. 进一步研究了非高斯噪声作用下系统从一个态跃迁到另一个态的平均首通时间(MFPT) ,并讨论了各个参数不同的作用机理.
基金Shao Hong-Bo’s laboratoryis jointly supported by National Economic Development Committee of China,SpecializedInitiation Foundation of Excellent Ph.D. Dissertation of Chinese Academy of Sciences, Doctoral Foundation of QUST (0022221)NationalScience&Technology Supporting Plan of China (2006BAC15B03) .
基金supported in part by HKRGC GrantHKU Strategic Theme Grant on Computational SciencesNational Natural Science Foundation of China under Grant Nos.10971075 and 11271144
文摘Modeling genetic regulatory networks is an important research topic in genomic research and computationM systems biology. This paper considers the problem of constructing a genetic regula- tory network （GRN） using the discrete dynamic system （DDS） model approach. Although considerable research has been devoted to building GRNs, many of the works did not consider the time-delay effect. Here, the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes. The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points. Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs. The constructed genetic network using linear multiple regression matches with the original data very well. Simulation results are given to demonstrate the effectiveness of the proposed method and model.
