具有加性噪声的基因调控网络的稳定性分析
Stability analysis of genetic regulatory network with additive noises.
作者信息
Jin Yufang, Lindsey Merry
机构信息
Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA.
出版信息
BMC Genomics. 2008;9 Suppl 1(Suppl 1):S21. doi: 10.1186/1471-2164-9-S1-S21.
BACKGROUND
Genetic regulatory networks (GRN) can be described by differential equations with SUM logic which has been found in many natural systems. Identification of the network components and transcriptional rates are critical to the output behavior of the system. Though transcriptional rates cannot be measured in vivo, biologists have shown that they are alterable through artificial factors in vitro.
RESULTS
This study presents the theoretical research work on a novel nonlinear control and stability analysis of genetic regulatory networks. The proposed control scheme can drive the genetic regulatory network to desired levels by adjusting transcriptional rates. Asymptotic stability proof is conducted with Lyapunov argument for both noise-free and additive noises cases. Computer simulation results show the effectiveness of the control design and robustness of the regulation scheme with additive noises.
CONCLUSIONS
With the knowledge of interaction between transcriptional factors and gene products, the research results can be applied in the design of model-based experiments to regulate gene expression profiles.
背景
遗传调控网络（GRN）可用具有和逻辑的微分方程来描述，这种逻辑在许多自然系统中都能找到。识别网络组件和转录速率对于系统的输出行为至关重要。虽然转录速率无法在体内测量，但生物学家已表明它们可在体外通过人工因素改变。
结果
本研究提出了关于遗传调控网络新型非线性控制和稳定性分析的理论研究工作。所提出的控制方案可通过调整转录速率将遗传调控网络驱动到期望水平。针对无噪声和加性噪声情况，用李雅普诺夫论证进行了渐近稳定性证明。计算机模拟结果表明了控制设计的有效性以及该调控方案在加性噪声情况下的鲁棒性。
结论
基于转录因子与基因产物之间相互作用的知识，研究结果可应用于基于模型的实验设计，以调控基因表达谱。
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