title: Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  BACKGROUND
  The software used in this article is available from http://sbie.kaist.ac.kr/software
authors: Department of Aerospace Engineering, University of Glasgow, Glasgow, UK.
authors: Jongrae
authors: J
authors: Kim
authors: Declan G
authors: DG
authors: Bates
authors: Ian
authors: I
authors: Postlethwaite
authors: Pat
authors: P
authors: Heslop-Harrison
authors: Kwang-Hyun
authors: KH
authors: Cho
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D016014:Linear Models; D008954:Models, Biological; D017711:Nonlinear Dynamics; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome