一类调控基因网络的随机模型与数值算法
Stochastic models and numerical algorithms for a class of regulatory gene networks
摘要：
调控基因网络包含通用模块，如涉及反馈回路的模块，这些模块对许多生物功能的调控至关重要。我们考虑一类自调控基因，这是许多调控基因网络的基石，并研究了相关的Gillespie算法的稳态分布，通过提供有效的数值算法。我们还研究了在合成生物学和基因治疗中感兴趣的调控基因网络，使用具有时间延迟的平均场模型。对一类带反馈环的线性催化网络，建立了相应的时间非齐次马氏链的收敛性
英文摘要：
Regulatory gene networks contain generic modules like those involving feedback loops, which are essential for the regulation of many biological functions. We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady state distributions of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in synthetic biology and in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops
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