Kato M, Tsunoda T, Takagi T
Department of Physics, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyou-ku, Tokyo 113-0033, Japan.
Genome Inform Ser Workshop Genome Inform. 2000;11:118-28.
Inferring gene regulatory networks by differential equations from the time series data of a DNA microarray is one of the most challenging tasks in the post-genomic era. However, there have been no studies actually inferring gene regulatory networks by differential equations from genome-level data. The reason for this is that the number of parameters in the equations exceeds the number of measured time points. We here succeeded in executing the inference, not by directly determining parameters but by applying multiple regression analysis to our equations. We derived our differential equations and steady state equations from the rate equations of transcriptional reactions in an organism. Verification with a number of genes related to respiration indicated the validity and effectiveness of our method. Moreover, the steady state equations were more appropriate than the differential equations for the microarray data used.
从DNA微阵列的时间序列数据通过微分方程推断基因调控网络是后基因组时代最具挑战性的任务之一。然而，目前还没有从基因组水平数据通过微分方程实际推断基因调控网络的研究。原因是方程中的参数数量超过了测量的时间点数。我们在此成功地执行了推断，不是直接确定参数，而是对我们的方程应用多元回归分析。我们从生物体中转录反应的速率方程推导出我们的微分方程和稳态方程。对许多与呼吸相关的基因进行验证表明了我们方法的有效性和实用性。此外，对于所使用的微阵列数据，稳态方程比微分方程更合适。
