Kim Jongrae, Bates Declan G, Postlethwaite Ian, Heslop-Harrison Pat, Cho Kwang-Hyun
Department of Aerospace Engineering, University of Glasgow, Glasgow, UK.
Bioinformatics. 2008 May 15;24(10):1286-92. doi: 10.1093/bioinformatics/btn107. Epub 2008 Mar 26.
Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles.
A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells.
The software used in this article is available from http://sbie.kaist.ac.kr/software
生物分子相互作用中固有的非线性使得网络相互作用的识别变得困难。主要问题之一是，所有基于线性时不变模型的方法在推断某些非线性网络相互作用的能力上都将存在根本限制。另一个困难是可能解的多样性，因为对于给定的数据集，可能存在许多不同的可能网络会生成相同的时间序列表达谱。
提出了一种从时间表达数据推断生物分子相互作用网络的新算法。该算法采用线性时变模型，它能够表示比线性时不变模型更广泛的一类时间序列数据。从时间序列表达谱中，通过求解一个非线性优化问题来识别模型参数。为了系统地减少优化问题的可能解集，使用带有随机数值扰动的相图分析进行过滤过程。所提出的方法具有以下优点：不需要系统处于稳定稳态，使用由单个实验生成的时间序列谱，并且能够识别非线性网络相互作用。通过将其应用于三个例子来说明所提出算法正确推断网络相互作用的能力：盘基网柄菌中cAMP振荡的非线性模型、酿酒酵母的细胞周期数据以及一组同步化的盘基网柄菌细胞的大规模非线性模型。
本文中使用的软件可从http://sbie.kaist.ac.kr/software获取
