生物化学网络的代数推断方法：极大似然法
Algebraic Methods for Inferring Biochemical Networks: a Maximum Likelihood Approach
摘要：
我们提出了一种新的方法，用于确定一个生化反应网络的基础上，在相应的反应速率方程从各种（可能不同）的实验到达多套估计的反应速率。目前的方法，不像一些在文献中提出的图形方法，使用的实验测量值仅相对于生化反应的几何形状的假设下，基本的反应网络是相同的所有的实验。所提出的方法利用代数统计方法，以参数化的一组可能的反应，以确定最有可能的网络结构，并很容易扩展到非常复杂的生化系统，涉及大量的物种和反应。该方法示出了一个假设的网络形式的“传质”型模型的数值例子。
英文摘要：
We present a novel method for identifying a biochemical reaction network based on multiple sets of estimated reaction rates in the corresponding reaction rate equations arriving from various (possibly different) experiments. The current method, unlike some of the graphical approaches proposed in the literature, uses the values of the experimental measurements only relative to the geometry of the biochemical reactions under the assumption that the underlying reaction network is the same for all the experiments. The proposed approach utilizes algebraic statistical methods in order to parametrize the set of possible reactions so as to identify the most likely network structure, and is easily scalable to very complicated biochemical systems involving a large number of species and reactions. The method is illustrated with a numerical example of a hypothetical network arising form a "mass transfer"-type model.
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