TY - JOUR

T1 - Linear-time varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data

AU - Kim, Jongrae

AU - Bates, Declan

AU - Postlethwaite, Ian

AU - Heslop-Harrison, Pat

AU - Cho, Kwang-Hyun

PY - 2008

Y1 - 2008

N2 - Motivation: 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.
Availability: The software used in this article is available from http://sbie.kaist.ac.kr/software

AB - Motivation: 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.
Availability: The software used in this article is available from http://sbie.kaist.ac.kr/software

U2 - 10.1093/bioinformatics/btn107

DO - 10.1093/bioinformatics/btn107

M3 - Article

VL - 24

SP - 1286

EP - 1292

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 10

ER -