Lall Raman, Voit Eberhard O
Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, 303K Cannon Place, 135 Cannon Street, Charleston, SC 29425, USA.
Comput Biol Chem. 2005 Oct;29(5):309-18. doi: 10.1016/j.compbiolchem.2005.08.001. Epub 2005 Oct 6.
Modern biology is increasingly developing techniques for measuring time series of global gene expression and of many simultaneous proteins or metabolites. These data contain valuable information on the dynamics of cells, which has to be extracted with computational means. Given a suitable mathematical model, this extraction is in principle a straightforward regression task, but the complexity and nonlinearity of the differential equations that describe biological systems cause severe difficulties when the systems are of realistic size. We propose a method of stepwise regression that can be applied effectively to linear portions of pathways. The method may be combined with other estimation methods and either directly yields reasonable parameter estimates or at least provides appropriate start values for subsequent nonlinear search algorithms. We illustrate the method with the analysis of in vivo NMR data describing the dynamics of glycolytic metabolites in Lactococcus lactis.
现代生物学正日益发展出用于测量全球基因表达以及许多同时存在的蛋白质或代谢物时间序列的技术。这些数据包含有关细胞动态的宝贵信息，必须通过计算手段来提取。给定一个合适的数学模型，这种提取原则上是一项直接的回归任务，但当生物系统具有实际规模时，描述生物系统的微分方程的复杂性和非线性会带来严重困难。我们提出一种逐步回归方法，可有效地应用于途径的线性部分。该方法可与其他估计方法相结合，要么直接产生合理的参数估计值，要么至少为后续的非线性搜索算法提供合适的起始值。我们通过分析描述乳酸乳球菌中糖酵解代谢物动态的体内核磁共振数据来说明该方法。
